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  • Italian Edition of The Rise of Integrated Thinking Environments

    Published Jun 2, 2022

    Gli ambienti di pensiero integrato

    Just a quick note of gratitude. Antonio Dini has generously translated my article “Obsidian, Roam, and the rise of Integrated Thinking Environments—what they are, what they do, and what’s next” into Italian. Exciting!

    Thanks, Antonio. I hope Italian readers find the concept useful.

  • Note titles as API calls

    Published Oct 13, 2021

    i saw someone say that note titles should be like API names ( from @davestridsr on the Obsidian Discord)

    It was Andy Matuschak.

    An app adds an API when its developers want other developers to be able to extend and work with the app’s functions, e.g., to develop other features or to let services interact with one another.

    Some “good” features of an API in software development might be that the API is: (1) deep (you can get at as many functions of the underlying app as are useful), (2) expressive (you can fine-tune interactions between your extension and the app), and (3) intuitive (by looking at an API function’s title and parameters, it’s pretty easy to guess what it allows you to do).

    Andy’s saying that a good note title fits those parameters too. The metaphor goes: you “extend” an atomic evergreen note by invoking its title in a new note. By doing so (and if done right), you can grasp the details of that note easily while, at the same time, you can reinterpret it in its new context.

  • Intuition is confident abduction

    Last updated Nov 7, 2020 | Originally published Nov 7, 2020

    # Intuition is confident abductive-inferential thinking

    In a recent episode of Hello Monday, Jessi Hempel interviews Dr. Natalie Nixon on creativity and her new book, The Creativity Leap. Natalie’s PhD in Design Management—plus her work in fashion, design, and business—led her to a catchy and compelling description of creative work. We accomplish creative work, she says, “by toggling between wonder and rigour.”

    In the podcast conversation, Jessi and Natalie talk about intuition—and I was struck by something. “We don’t talk about intuition,” Natalie notes at about 6 minutes in. “We don’t talk about intuition in business school, in law school, or in medical school.” And yet, she says, “I observed that really successful leaders—especially really successful startup leaders—in their origin stories, there’s always this moment where ‘Something told me not to do the deal. Something told me to work with her over him.’ […] Every successful leader really reckons with incorporating acting on their intuition to make decisions.” Jessi agrees, noting that intuition comes up often in her interviews with leaders on Hello Monday as leaders cite it as the reason for their success.

    The thing is, just because we don’t name intuition doesn’t mean we aren’t talking about it. That’s because intuition is really just confident, logical thinking.

    Charles Sanders Peirce was a philosopher. He investigated how we inquire into and discover new knowledge.1 Before Peirce, we generally recognized the logical processes of deduction and induction. Deductive thinking helps us identify what must be true about a situation in order to explain it. When we deduce something, we look at the general rules and principles we know of and draw specific conclusions from that evidence. Inductive thinking involves drawing general conclusions from specific, limited evidence.

    Peirce argued that effective reasoning follows a pattern: we determine the specific consequences of an idea (deduction), and then we judge whether the available evidence fits that idea and its consequences (induction). But how do we develop ideas?2

    Abduction is the name of the logical process Peirce described for developing ideas. To think abductively means to generate and choose ideas that fit the situation at hand. A good idea should be verifiable—we should be able to use evidence to judge its fit—and should help us resolve the situation at hand. Peirce also had criteria to help choose the best ideas to test. He suggested that we should strive to conserve resources (e.g., those that most are most efficiently verifiable and usable in the situation), identify the most valuable ideas (specifically the “uberty” of an idea, or how likely it is that a possible idea might bring about an innovation), and the most relevant ideas (e.g., those that may apply beyond our current focus, too).3

    Abduction is clearly an important step in any innovative process—but it is no more important than testing and using the ideas you generate. What, then, if you don’t have enough evidence to truly test and prove your ideas?

    The process Peirce described—abduction, deduction, induction—is the ideal. However, we do not always have time and energy to follow the process diligently. Instead, we quickly make creative judgements based on a few observed qualities. This requires two related processes.4 The first Peirce called “abductory induction,” and it combines the first and last step of the inquiry process. We observe the qualities of the situation, and we generate possible ideas to resolve it based on those observations. The second process is known as “inference to the best explanation” (IBE).5 IBE is exactly what it sounds like. Given a number of possible ways of resolving a problem, choose the best one. (Peirce’s criteria, noted above, apply here.)

    So what does all this have to do with intuition?

    Intuition is the confident application of these shorthand logical approaches to creative problem solving. As Jessi and Natalie noted, we aren’t often explicitly taught about strengthening our intuition. Yet, everything we learn supports its development. The more we have to draw on in order to pull into the processes described above, the better our intuitive decisions will be.

    I say that intuition is the confident application of these processes because they only work when we follow through. In reality, we use abductory induction and IBE all the time. When we engage in creative problem solving, we’re not only using information from the evidence in front of us. We’re drawing on our lived experience and our knowledge base. Even if we don’t directly recall or reference that background information, it is drawn into the creativity of abduction and it defines the general rules and principles we use in deduction. It provides us with the heuristics we use when engaging in IBE. But if we don’t have a bias towards action and instead operate with e.g., perfectionism, we fail to actually execute on these ideas. Thus, we need to have confidence in our abductory induction and IBE processes.

    All this is simply a gentle challenge of the idea that we don’t talk about intuition. I think that all knowledge management practices and forms of education are actually fundamentally about strengthening our intuition.

    That said, Natalie’s work is fascinating. I recommend the episode of Hello Monday and plan on picking up her book!


    1. In this article, my reading of Peirce comes from the writing of William Mcauliffe↩︎

    2. Peirce was actually specifically concerned with science and hypotheses generation, selection, and testing. Here I refer to generating, selecting, testing, and using ideas to apply these concepts to problem-solving more broadly. ↩︎

    3. He also cautioned not to produce ideas that stop the inquiry process—e.g., magical thinking, or by suggesting that whatever happened must be a complete mystery. ↩︎

    4. Actually, the difference between these two processes is the subject of substantive, controversial debate. This is in part because the scholars who study inference to the best explanation have also used Peirce’s term “abduction” to describe it. This understandably caused extensive confusion, but also probably a lot of philosophical debates and scholarship, so maybe it was for the best. ↩︎

    5. Philosopher Gilbert Harman originally described and named this process… and mistakenly suggested it was the same thing as abduction. ↩︎

  • Researchers detail huge hack-for-hire campaigns against environmentalists

    Published Jun 10, 2020

    The report concludes that the campaigns represent “a clear danger to democracy” and could allow powerful organizations to target their opponents. “The extensive targeting of American nonprofits exercising their first amendment rights is exceptionally troubling,” Citizen Lab’s report says.

    We didn’t want this part of cyberpunk sci-fi…

  • A quick sketch of an interdisciplinary systems model

    Published Apr 24, 2020

    # Why are we exceeding the Earth’s carrying capacity?

    This is a quickly-sketched model created from a breakout group conversation during the MUN School of Graduate Studies’ “Earth’s Carrying Capacity” dialogue.

    Download the model

  • Roger Martin, Bianca Andreescu, and systemic strategy

    Published Apr 11, 2020

    According to designer/strategist Roger Martin, a strategy is an imagined possibility with which we ask the question “What has to be true for this possibility to become real?”

    In this episode IDEO U’s Creative Confidence podcast, Roger talks about how that approach helped unlock Bianca Andreescu’s success at the Grand Slam singles championship in 2019.

    One of the fascinating things about this approach is that it acknowledges the need for system-wide changes. By asking “What has to be true?”, a strategist must consider all of the conditions of a system that should shift to make the imagined possibility a reality. Of course, most approaches to strategy do require some appreciation of the state of the strategic environment (e.g., the five forces model). None, however, emphasize the need to guarantee these systemic conditions quite as explicitly as asking “What must be true?”

  • Systemic lessons from South Korea's Patient 31

    Published Mar 30, 2020

    This changed with the emergence of “Patient 31.”

    Reuters’ coverage of the “Korean clusters” provided the world with a vivid glimpse of the volatility of COVID-19. One person showed poor judgement, and in turn caused cascading catastrophe in her communities.

    Events like the COVID-19 pandemic are thankfully rare. Moments like these—when a lot happens all at once, and the experience is shared by a collective—shape future history like nothing else. We are learning a lot from this. Not only are epidemiologists now a famous profession, but we’re all learning exactly what it takes to provide good healthcare, what good governance looks like, how public health is community health, and more.

    Patient 31 holds a simple lesson for systemics: the fragility of apparently solid social systems. South Korea seemed to do everything right. Yet, due to the volatile nature of this particular socio-health system, a single “free radical” caused immense damage.

    Similar volatility is evident—but more subtle—in other social systems. Consider how memes spread. Our massive communities may seem immovable at times, but it’s clear that the wrong (or right) phenomena can spread rapidly and deeply.

    Stay safe.

  • Divide and conquer

    Published Mar 29, 2020

    # Divide & conquer

    I have often hesitated to draft up an idea because I’m not sure the folks reading this site want to hear it. I (aim to) publish about a few disparate subjects, really:

    • Systemics, design, and social change
    • Data modelling and data for social change
    • Productivity and personal knowledge management
    • Scripting and (personal) automation
    • Leadership, innovation, and changemaking

    Obviously, this is too many topics for any one blog. If you’re reading this, you probably came here for just one of the topics above, and you might be interested in another one or two. And listen, I like you, and I want any visit of yours to be a valuable one. That’s why I’ve launched a sibling blog.

    I could cluster the topics above a number of ways. Here’s what I think makes the most sense: This blog, Fulcra, will focus on finding leverage for complex change, including:

    • Systemics, design, and social change
    • Data modelling and data for social change
    • Leadership, innovation, and changemaking

    The newest one, Axle, will focus on how we change ourselves, including:

    • Personal development
    • Design and technology for augmented cognition
    • Productivity and personal knowledge management
    • Scripting and (personal) automation

    Moving forward, this site (Fulcra) will be a platform for writing on complex systems change. I aim to study, share, and write about how the world changes—and how we can get better at changing it.

    Axle is a new site I will use to share my thoughts on how we change ourselves. After all, the better we get, the better we better get. The easy thing to write about (and what you’ll probably see the most often there) are the apps and tools I use and the designs I apply in my life and work. I also plan to share functional resources (such as scripts) as well as ask questions and debate about making progress in life and work.

    This was a weird decision. After all, I barely publish here, so running two different sites seems like a terrible idea. I hope, however, that having more focused platforms for these different topics will help me publish more impulsively. Feel free to follow both, or none!

  • Welcome to a new revolution

    Published Mar 29, 2020

    # A turn of events

    Welcome to Axle.

    I probably don’t need two blogs, but I have found myself hesitating to publish a variety of ideas on Fulcra because I wasn’t sure that those who read that blog for the systems/design thinking or social change focus would care about my thoughts on productivity, practice, and personal development.

    So that’s what this place is for.

    While my writing on Fulcra will (continue to) explore how the world changes, Axle is about how we change. In particular, I’m interested in “augmenting cognition”: how information systems and systemic design can help us think, do, and be better. I’ll also be writing about the different techniques and tools I use in my own work, as well as publishing resources that you might find useful.

    What’s in the name? Well, while Fulcra emphasizes the search for leverage points in complex systems change, Axle is centred on the core of that change. That’s us—the people driving it. We try our best to amplify the forces of the world in order to shift something that matters to us. We roll with our changing worlds—and we are also in constant motion (and transformation) ourselves.

    Welcome!

data

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  • As Lambda students speak out the schools debt-swapping partnership disappears from the internet

    Published Feb 19, 2020

    “The ISA is the business model, not education,” says Kim Crayton, a business strategist and founder of CauseAScene , an organization that’s seeking to disrupt the status quo in tech. “You cannot tell me that education is your business model when you have not registered as an institution.” For months, Crayton has been speaking about the problems with coding bootcamps on her podcast, where she’s argued that they target vulnerable communities. “You’re put in these spaces and putting in 110 percent and it’s still not working and you’re told to ‘trust the process,’” she says.

    Great reporting on this at The Verge.

    Kim Crayton makes an excellent point. The promise of many of these neo-credentials is for students to leapfrog the things everyone fears about the conventional education system. No one is more vulnerable to taking on loads of student debt than those who need it most. Those students are also going to suffer the most if their university or college fails to equip them for a career. Lambda solves both of these problems, making it extremely attractive to poor students.

    Sadly, there’s always a catch.

  • Bring It On Haters With Special Guest Ben Thompson

    Published Feb 9, 2020

    Ben Thompson, in discussion with John Gruber:

    It was mindblowing. It was absolutely incredible. The way that you could just do stuff that wasn’t really possible [on a computer]. Again, it was technically possible on a computer, but the user interface and experience was just transformative on the iPad. It was absolutely incredible.

    And Jobs knew it. It’s one of my all-time favourites Jobs moments. It’s like fifteen seconds after the demo, and it’s just like… he’s used this. He was involved in the creation of it. They had run through the demo. He knew it. And even then, he was just astonished. He’s just like ‘I can’t believe [this]…’

    […]

    It was, to my mind, the culmination of his life’s work. He comes on there, and he’s like, ‘Isn’t it incredible? Now anyone can make music.’

    I almost want to transcribe this whole episode. John Gruber and Ben Thompson discuss the potential of the iPad—and its failure to reach it.

    Ben uses the term “transformative” deliberately above. They discuss how, before the iPad, no computing experience could adapt to become wholly new tools and environments for whatever the user wanted to do. But the iPad can become a piano or a canvas or a television. In this sense, they argue that the iPad has (or had) the potential for disruptive innovation (RIP Clay Christensen)—but it’s not supposed to be a Mac.

    These two think the iPad’s lost the chance to fulfill that potential, mostly because Apple has missed the opportunity to build a vibrant developer ecosystem due to App Store policies. I hope that isn’t the case, though I think we have to look beyond the iPad to fully appreciate what might happen next. The introduction of tablets and transformative computing experiences continues to echo throughout a variety of industries. Graphic designers and illustrators have a new suite of tools to directly interact with their creations in the iPad Pro and the Surface. Similarly, tablet or hybrid devices have transformed schools—schoolchildren now have a “homework” device for all kinds of assignments. It’s true that we still need developers to imagine ever-more revolutionary applications for these devices, but there’s no denying that disruption is taking root.

    Either way, the episode is well worth a listen. Enjoy from 15:50 to ~31:22 and 1:26:59 to the end of the show if you want to focus on the iPad discussion.

  • Starting the Decade by Giving You More Control Over Your Privacy

    Published Jan 28, 2020

    My bank, fitness and workout apps, and food delivery services I haven’t used in months—those were some of the 30+ apps interacting with Facebook data. Ostensibly this data is used to personalize ads.

    As of today, our Off-Facebook Activity tool is available to people on Facebook around the world. Other businesses send us information about your activity on their sites and we use that information to show you ads that are relevant to you. Now you can see a summary of that information and clear it from your account if you want to.

    Off-Facebook Activity marks a new level of transparency and control. We’ve been working on this for a while because we had to rebuild some of our systems to make this possible.

    Now, thankfully, you can review these connections yourself and clear any history manually. Check out Facebook’s Off-Facebook Activity controls, and happy Data Privacy Day.

  • Leaked Documents Expose the Secretive Market for Your Web

    Published Jan 27, 2020

    If the product is free, you are the product:

    An antivirus program used by hundreds of millions of people around the world is selling highly sensitive web browsing data to many of the world’s biggest companies.

  • What part of 'viral' content makes platforms want to encourage its spread?

    Published Jan 23, 2020

    The Twttr prototype app gave me another feedback form today. It’s been my habit to complain, at every opportunity, about the trends page you have to engage with whenever you go to the Search tab. I feel a little bad for the designers and developers, because the beta is really all about how conversations on Twitter look and feel. Still, this feedback form was no different. Here’s what I wrote in the “Dislike” section:

    I wish I could control the trends page.

    It is the absolute worst part of my Twitter experience. It just feels… unhealthy. Like going through a grocery store magazine aisle. Sure, some of the headings are instructive or inspiring, but many are gross, irrelevant, or completely malignant gossip.

    The experience is also invasive. Because trends are forced upon you when you intend on searching for something specific, and because they’re algorithmically-tunes to be as attention grabbing as possible, it’s easy to be distracted and forget why you even entered the search pane. I never explicitly consent to learning about celebrity gossip or US politics when I use Twitter. If I tap on some of those topics, it’s not because I want to. It’s because it’s malicious click bait. In turn, it’s corrupt to design an experience that drags the user through it repeatedly.

    Sure, this content is viral. But shouldn’t we be inoculating against viruses, not encouraging them to spread?

  • US announces AI software export restrictions for China

    Published Jan 23, 2020

    A surprising headline, but the restriction is very specific:

    the new export ban is extremely narrow. It applies only to software that uses neural networks (a key component in machine learning) to discover “points of interest” in geospatial imagery; things like houses or vehicles. The ruling, posted by the Bureau of Industry and Security, notes that the restriction only applies to software with a graphical user interface — a feature that makes programs easier for non- technical users to operate.

    Still, this is probably a marker of change to come. It’s a little odd to think of software as something that can be “exported”, however. Surely this isn’t a ban on shipping discs across a border. Software is downloaded. So, is this a kind of firewall?

  • This wireless power startup says it can charge your phone using only radio waves

    Published Jan 23, 2020

    Bohn and his co-founders are confident that, if done right, a proper system for wireless power transmission could shift not just how we think about keeping devices charged and powered on at all times, but also the types of devices we end up putting in our homes and what those devices get used for.

    I’d say. If this works, it’ll remove a technical limitation that is pretty built-in to our mental models of how our gadgets work.

    Guru is envisioning a world where you can keep all manner of battery-powered gadgets, big and small, all over your home or in every corner of an office, store, or warehouse without having to worry about where they draw power from or how long it lasts on a charge.

    Eliminating the “where will I plug it in?” assumption might unlock opportunities across all ways of living and working.

  • Segway’s newest self-balancing vehicle is an egg-shaped wheelchair

    Published Jan 23, 2020

    Segwayʼs newest self-balancing vehicle wonʼt require you to stand up. Dubbed the S- Pod, the new egg- shaped two-wheeler from Segway-Ninebot is meant to let people sit while they effortlessly cruise around campuses, theme parks, airports, and maybe even cities.

    Hmm. At least with self-balancing, this won’t happen.

  • Report Launch - OPSI Primer on AI for the Public Sector

    Published Jan 23, 2020

    Today, we’re excited to formally launch the final version of OPSI’s AI primer: Hello, World: Artificial Intelligence and its Use in the Public Sector

    Another interesting output from the OPSI. It seems usefully pragmatic:

    The AI primer is broken up into four chapters that seek to achieve three key aims: (1) Background and technical explainer; (2) overview of the public sector landscape; (3) implications and guidance for governments.

  • Microsoft wants to capture all of the carbon dioxide it’s ever emitted

    Published Jan 23, 2020

    The most audacious commitment from Microsoft is its push to take carbon out of the atmosphere. The company is putting its faith in nascent technology, and it’s injecting a significant investment into a still controversial climate solution. Proponents of carbon capture, like Friedmann, say that the technology is mature enough to accomplish Microsoft’s aims. It’s just way too expensive right now. Microsoft’s backing — and its $1 billion infusion of cash — could ultimately make the tech cheaper and more appealing to other companies looking for new ways to go green.

    Fantastic news. Carbon capture is a key opportunity for decelerating climate change. Hopefully more companies follow suit.

Apps-or-tools

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systemics

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  • Theory of Systemic Change and Action

    Last updated Mar 7, 2023 | Originally published Mar 7, 2023

    Theories of Change are one of the fundamental tools of changemakers and program evaluation (Mackinnon, 2006). However, when addressing wicked problems (Rittel & Webber, 1973), theories of change are too reductive and linear to properly account for the systemic phenomena, structures, and dynamics that perpetuate the issues we’re trying to address (Murphy & Jones, 2020).

    Theories of Systemic Change and Action (ToSCA) are a systemic design tool that combine theories of change with systemic understanding. The result is a theory of change that is useful for understanding, communicating, and evaluating systemic change projects.

    Here’s a rough guide to develop a ToSCA:

    1. Model the system (e.g., with causal loop diagrams; Kim, 1992).
    2. Develop systemic strategies from the model.
    3. Reorganize the modelled phenomena. From left to right:
      1. Capability building and resource mobilization for the initiative (Inputs)
      2. Inteventional activities the initiative will take on (Activities)
      3. Immediate outputs of those activities (Outputs)
      4. Results of those outputs on the overall system (Outcomes)
      5. Downstream effects of those outcomes on higher-system structures (Impacts)
    4. Reiterate on step 3 as necessary.

    The resulting diagram will look somewhat like an iceberg model (Stroh, 2015, p. 46] on its side: visible events and behaviour are on the left, while the actual patterns and structures in the system fall to the right.

    The ToSCA can then be simplified as necessary to suit different needs. For instance, if presenting the model briefly to a potential funder, you may want to collapse major feedback loops into one element on the model with a “loop” icon. This way you can still show inputs and outputs on that loop while obscuring the complexity within it for the purposes of the presentation.

  • A Case Study of Theories of Systemic Change and Action — The Ecotrust Canada Home-Lands initiative

    Last updated Mar 7, 2023 | Originally published Mar 7, 2023

    In this presentation, we reported on a case study of the Ecotrust Canada Home-Lands initiative. Lewis and I worked with Ecotrust Canada to understand the challenges they were addressing from a systemic design lens and, using that approach, to develop a theory of systemic change and action for the initiative.

    An interesting development in the work was the development of a novel systemic evaluation technique: resonance and dissonance tests. The tests were designed as a way of testing our understanding of the system without interrupting or intruding on the processes of the initiative. The general idea behind resonance and dissonance tests is to identify a set of phenomena in your understanding of the system and to search for disconfirming evidence that those phenomena are complete and accurate. So, for instance, if you think a key phenomenon in the system is “community distrust of bureaucracy”, look for examples of the community trusting bureaucracy. If you can’t find any, it increases the integrity of the theory you’ve created.

  • Leverage theory

    Last updated Mar 7, 2023 | Originally published Feb 24, 2023

    We seek leverage to find the best ways of making change.

    Leverage points are places in systems where a little effort yields a big effect (Meadows, 1997). They are also ideas that help us grab on to strategic ways forward when we’re working in complexity (Klein & Wolf, 1998).

    Acting on leverage points may accelerate systemic change towards progress and reform, but acting on the wrong ones may instead accelerate systemic change towards regression and deformity. Well-designed leverage strategies may be catalyzing or even transformative, but poorly designed ones may merely be futile (figure 1).

    One way of finding leverage points is to think through your system with reference to Meadows’s (1997) 12 types:

    Table 1. Twelve types of leverage points, in order of increasing power (adapted from Meadows, 1997).

    Twelve types of leverage points, in order of increasing power Example
    12. Constants, parameters, numbers (such as subsidies, taxes, standards) Wages, interest rates
    11. The sizes of buffers and other stabilizing stocks, relative to their flows. Current levels of debt/assets
    10. The structure of material stocks and flows (such as transport networks, population age structures) An individual’s financial structure (e.g., fixed costs and incomes)
    9. The lengths of delays, relative to the rate of system change How long it takes to find a higher-paying job
    8. The strength of negative feedback loops, relative to the impacts they are trying to correct against Rising costs of living vs. fixed income
    7. The gain around driving positive feedback loops Recession causing reducing spending
    6. The structure of information flows (who does and does not have access to what kinds of information) How aware you are of impending recession/future rising costs
    5. The rules of the system (such as incentives, punishments, constraints) Who suffers as a result of poorly-managed recessions
    4. The power to add, change, evolve, or self-organize system structure Central banks, Ministries of Finance
    3. The goals of the system GDP Growth
    2. The mindset or paradigm out of which the system—its goals, structure, rules, delays, parameters—arises Growth above all
    1. The power to transcend paradigms Sustainable development, flourishing

    Another approach, which may be complementary to the above, is to model the system as a causal loop diagram (e.g., Kim, 1992) and then to conduct leverage analysis (Murphy & Jones, 2020) on the model.

    An understanding of leverage in a system allows us to generate systemic strategies (Murphy & Jones, 2020). These strategies can also be adapted into Theories of Systemic Change (Murphy & Jones, 2020).

    # Background

    Donella Meadows (1997) popularized the idea of leverage in systemic change with her essay “Leverage Points: Places to Intervene in Complex Systems.” She proposed a typology of phenomena in a system, suggesting that acting on certain types of phenomena are higher-leverage than others.

    In an article published in the Contexts journal of systemic design, I challenged Meadows’s (1997) paradigm, proposing a few other possible ways of viewing leverage. My aim was to link the search for leverage directly to the design of powerful strategies for systemic change, and to propose a few ways forward in advancing our understanding of leverage in complex systems.

  • Roger Martin, Bianca Andreescu, and systemic strategy

    Published Apr 11, 2020

    According to designer/strategist Roger Martin, a strategy is an imagined possibility with which we ask the question “What has to be true for this possibility to become real?”

    In this episode IDEO U’s Creative Confidence podcast, Roger talks about how that approach helped unlock Bianca Andreescu’s success at the Grand Slam singles championship in 2019.

    One of the fascinating things about this approach is that it acknowledges the need for system-wide changes. By asking “What has to be true?”, a strategist must consider all of the conditions of a system that should shift to make the imagined possibility a reality. Of course, most approaches to strategy do require some appreciation of the state of the strategic environment (e.g., the five forces model). None, however, emphasize the need to guarantee these systemic conditions quite as explicitly as asking “What must be true?”

  • Can Snow Clearing Be Sexist?

    Published Mar 25, 2020

    And so the Swedish gender equality initiative team began to explore whet her snow clearing was sexist. Sure enough, they found the routine of clearing snow typically benefited men over women. In the winter, snow was cleared first on main roads leading into the city, benefiting commuters—who were mostly men. Foot- and cycle-paths were cleared last—not so good for pedestrians and cyclists, who were very often women traveling with children in pushchairs.

    There was a cost to all this: 79% of pedestrian injuries occurred in winter, of which 69% were women. The estimated cost of these falls was SKr36m per winter, about USD$3.7m / £3m / €3.4m / Indian ₹279m. By clearing paths first, accidents decreased by half and saved the local government money.

    Great action-based response to a stupid remark (“At least snow-clearing was something those ‘gender people’ can keep their noses out of,” someone had said, prompting this investigation).

  • Applied Systems Thinking

    Published Jan 23, 2019

    # Applied Systems Thinking

    Based on the Applied Systems Thinking workshop, I’ve collected a variety of resources to help you map complex problems below. The buttons link directly to files to save you some trouble. Be mindful that most of these files are published documents or books, so if you really like them, you should buy a copy and support the authors!

    If you have questions or if you’re looking for more resources, never hesitate to reach out to me via  ryan@fulcra.design.

    SLIDES - DAY 1 (2.0 MB)

    SLIDES - DAY 2 (4.1 MB)


    # Introduction

    What are systems? Interconnected sets of elements whose interactions lead to emergent, “purposeful” behaviour. (A system’s purpose is not necessarily what someone intends of it, though, nor can it be derived from rhetoric about the system. A system’s purpose can only be defined by examining its actual behaviour.)

    Systems—and the actors within them—do exactly what they are “designed” to do. That is, systems act perfectly in tune with the structures and incentives that they have. Only by understanding these complex structures and incentives can we begin to make real progress on the challenges we’re addressing.

    # Key Things To Remember

    1. The most important thing about mapping is not the map itself. It is the conversations that the map (and the mapping process!) can spark. 
    2. Stay focused on answering complex questions. Mapping is not an end in itself. You are “finished” mapping when you’ve answered your focusing questions (more below).

    # Case Studies

    • Remember the story of the development worker trying to support water access in rural Malawi. (37.4 kb) By identifying someone else who was working on a similar problem (community health workers), the development worker was able to gain substantial leverage over their problem with minimal effort. Systems work helps is to identify leverage points.
    • Remember the story of The After Prison Initiative (TAPI). (37.4 kb) By helping changemakers working on the same issue see the whole system, each was able to recognize the problems with the system that they were responsible for. Systems help us recognize that every participant in a system has responsibility for the whole system, not just their part.
    • Remember the story of the spruce budworm. (37.4 kb) The Atlantic Canadian lumber industry made itself addicted to insecticide by beginning and sustaining insecticide sprays before they understood the long-term forest ecosystem. Systemic innovation is often counterintuitive; the wrong fix in the wrong place can make matters worse.
    • Remember the story of the well-intended conference organizers. (37.4 kb) This story teaches two lessons. First, systemic problems are rarely shifted by simple solutions. If you account for only one part of the problem, your fix may fail. Second, it is difficult to understand a systemic problem without involving all of the key stakeholders, particularly the beneficiaries you aim to serve. Involve them in order to see the whole system.

    # Developing A Focusing Question

    # Why Are “How Might We..?” Questions Useful?

    • “How” invokes a sense of opportunity. Therefore, “How might we..?” questions are appreciative.
    • “Might” invokes a sense of pluralism. Therefore, “How might we..?” questions are open-ended: there is more than one possible solution.
    • “We” invokes togetherness. Therefore, “How might we..?” questions are pursued collaboratively, particularly by asking and answering questions with stakeholders.

    Rittel & Weber’s principles of wicked problems.

    However, “How might we..?” questions aim to provide solutions. Before we can “solve” systemic issues, however, we must understand them. And unfortunately for us, these are usually  wicked problems.

    To understand a problem, we must begin to explore causality. Systemic designers seek to understand complexity by searching for the patterns that cause our problems—and finding the underlying structure of those patterns that enable their persistence.

    To that end, David Stroh suggests developing a “focusing question” in systems work. The purpose of systems mapping, he says, is not to map a system; it is to answer the focusing question

    A focusing question has the form “Why does this problem persist?” or “Why, despite our best efforts, intentions, and resources, have we been unable to achieve a certain goal or solve a particular problem?”

    READ MORE ABOUT FOCUSING QUESTIONS (PAGE 92; 5.9 MB)

    # FOUR TYPES OF SYSTEMS MAPPING

    Rich Pictures

    # Rich Pictures

    LEARN MORE ABOUT SOFT SYSTEMS METHODOLOGY & RICH PICTURES (400 KB)

    Rich pictures come from Checkland’s Soft Systems Methodology (SSM). Akin to a sketchy infographic, these maps are illustrated and make heavy use of labels and symbols to help the mapper or the reader understand a messy situation.

    Actor Maps

    # Actor Maps

    LEARN MORE ABOUT ACTOR MAPPING (1 MB)

    Actor maps graph the relationships in a social system. Who (or what organizations) influence who? Who funds who? How is the vision of the system determined? By identifying different stakeholders, including who are the most important beneficiaries and victims of a system, systemic designers might catch gaps, missed connections, or other issues.

    Causal Loop Diagrams

    # Causal Loop Diagrams

    LEARN MORE ABOUT CAUSAL LOOP DIAGRAMS

    Also known as influence diagrams and effect maps, Causal Loop Diagrams graph the phenomena of a system. What are we trying to stop from happening (or what do we want to happen more often)? What encourages or limits those phenomena? Then, what encourages or limits _those_causes or limits? By drawing causal connections between the phenomena of the system, we can recognize the complex interactions that lead to the (frequently counterintuitive) emergent patterns of behaviour normally invisible.

    Stock and Flow Diagrams

    # Stock And Flow Diagrams

    LEARN MORE ABOUT STOCK AND FLOW DIAGRAMS (CHAPTER 6; P. 192; 1.2 MB)

    How much of what quantities flow at what rates? Stock and flow diagrams make explicit the system’s stores (e.g., the heat in a cup of coffee) and its rates of change (e.g., how quickly heat escapes from the cup). These diagrams also recognize what controls these rates of change.

    # Applied Systems Thinking

    # Dimensions Of A System:

    Dimensions and obstructions of systems.png

    Add detail to your systems maps by exploring the different dimensions along which influence might flow: wealth, power, values, knowledge, or beauty. Different types of obstructions—poverty, maldistribution, and insecurity—can cause different types of problems in each of these dimensions.

    READ MORE ABOUT SYSTEMS DIMENSIONS (P. 78; 4.8 MB)

    # Leverage Points:

    Leverage points are places within a system with which a little effort yields great reward. Likewise, bottlenecks are places within a system which resistance could cause significant problems.

    # Leverage Points:

    Leverage points are places within a system with which a little effort yields great reward. Likewise, bottlenecks are places within a system which resistance could cause significant problems.

    READ MORE ABOUT LEVERAGE POINTS (225 KB)

    # Systems Archetypes:

    Systems archetypes are common patterns identified in causal loop diagrams or stock-and-flow diagrams. Archetypes exhibit similar behaviours and can be resolved by similar solutions.

    READ MORE ABOUT SYSTEMS ARCHETYPES (1.2 MB)

    # The Systems “Business Idea”:

    Do you need a particular actor or phenomena to receive resources/power/etc.? Where would that resource come from? What influences how much of the resource gets distributed? How can you increase those influencing forces? The business idea uses a causal loop diagram to map the systemic structure of an organization’s strategic sustainability. It makes explicit the phenomena that generate resources for the system to reinvest—and the strategic competencies that the organization can use to enhance those phenomena.

    READ MORE ABOUT SYSTEMIC BUSINESS IDEAS (P. 11-19; 925 KB)

    # Systemic Theories Of Change:

    What is the change strategy you’re adopting? Similar to the business idea, a systemic theory of change plots a theory of change model in systemic form, identifying the goal phenomena to enhance (or limit), the key activities that can support that enhancement (or limitation), and the inputs required to sustain and scale those activities.

    READ MORE ABOUT THEORIES OF CHANGE (396 KB)

    # Systems Stories:

    Never explain a systems map in a pitch or to a general audience. Instead, follow the iceberg model to distill a systems story

    1. Describe what happened (an example of the event or phenomena the systemic designer seeks to address);
    2. Describe what has been happening (the pattern of events that lead to the problem or issue); and
    3. Describe why (the underlying causal structure that enforces the persistence of these problematic patterns).

    READ MORE ABOUT SYSTEMS STORIES (P. 38; 5.9MB)

    # Technology For Systems Mapping

    Mural

    Mural.co is a collaborative tool for design sprints. As such, it provides features for collaborative whiteboarding and sticky noting, voting via dotmocracy, and a variety of other neat and helpful tools. It is very free-form (and as such has no features specifically made for systemic design) but that open-endedness may be useful.

    Loopy

    Nicky Case’s  Loopy is a simple tool that allows you to simulate systemic behaviours. It is very unsophisticated (e.g., it is challenging to provide precise system settings) but it is extremely gestural and is therefore fun to use to illustrate and explore ideas.

    Plectica

    Plectica is a “visual mapping software”. It allows you to nest and draw connections between cards representing anything. The goal of the app is to provide a simple interface for complex ideas. 

    Kumu

    Kumu is a web app built specifically for systems mapping. It  features extensive features and documentation and a lively support community full of fellow systems mappers who like to help one another. The developers/founders are active participants in that community and regularly provide customer support, too. Develop actor maps, systems maps, and all kinds of other interesting interactive visuals with Kumu.


    # ADDITIONAL RESOURCES

    I’ve collected and described resources on related topics at https://systemic.design/resources. In particular, you may want to check out the items on organizational change (e.g., the “Notes on Leadership and Language in Regenerating Organizations” paper and the article on organizational learning).

  • Finding the Emic in Systemic Design

    Published Oct 26, 2018

    # Finding the Emic in Systemic Design

    A paper presented at RSD7 in Turin, Italy.

    Download the slides

    # ABSTRACT

    I argue that an under-emphasized but crucial variable of success in systemic design is the perspective through which systemic design processes are implemented and executed. While rooted in design (a consciously empathetic discipline, especially in recent years; cf. Kimbell, 2011), it is easy for systemic designers to conduct the research required for their projects in externalized ways. These approaches risk misrepresenting the stakeholders who contribute to projects and, in turn, they are a danger to the potential impact of these misresearched problem systems. I propose to advance a theoretical argument for this danger, the development of an assessment framework to check whether an internalized perspective has been effectively achieved, and provide a proof of concept of this framework through hermeneutic case study analysis.

    As I will show, systemic design processes that are not executed with the direct and explicit engagement of stakeholders – to the extent of achieving an emic (or from within) understanding of the system – may be flawed at their foundation. By fostering recognition of the importance of an emic perspective, and by providing a framework of principles, practices, and process to accomplish systemic design with this perspective, I hope to ensure that systemic design processes are as accurate and valid as possible with respect to the stakeholders of the system.

    This is not to suggest that systemic design practice is “too etic”. In fact, with roots in design, systemic design is often deliberately emic. Systemic designers make use of designerly tools that help the researcher to build empathy with system stakeholders (e.g., soft systems methodology, critical systems heuristics, appreciative inquiry; Jones, 2014). They often seek to engage stakeholders in the systemic design process and include reflective analysis of what has been learned in order to assess where deeper engagement with the system is required (Ryan, 2014). That said, with the advent of crowdsourcing (the facilitated involvement of the general public in problem solving, usually using online tools; Lukyanenko & Parsons, 2012) and data science (the use of computational tools to analyze and understand large quantities of data; cf. Scepanovic, 2018), it is likely that data-driven methods will increasingly influence systemic design practice. One recent example sought input from hundreds of people to identify opportunities for change in Canadian post-secondary systems through an iterative online survey (cf. Second Muse, Intel, & Vibrant Data, 2016). This data-driven direction is a powerful opportunity, of course, but it underscores the need to develop principles and best practices for assessing and supporting emic understanding as we gain more data from these tools.

    This proposal consists of two steps. First, I will look to the principles and theorists of ethnography to develop a framework for assessing the emic/etic perspective of a given research project. Namely, Geertz’ “Thick Description: Toward an Interpretive Theory of Culture” (found in The Interpretation of Cultures, 1973, chapter 1) provides a foundation for the process of emic research, while Creswell and Miller (2000) provide a set of procedural principles for emic validity. Taken together, we generate a critical research framework with which we may assess a given research project’s emic perspective. Second, I will provide a proof-of-concept of this framework (and its theoretical underpinnings) via a casebased assessment of three systemic design projects. Case studies provide an effective venue for learning about the context-dependent manifestations of the phenomena being studied (Flyvbjerg, 2006). One of these case studies is one I have developed through my experience in participating and contributing to the development of the Canadian National Youth Leadership and Innovation Strategy framework, which convened hundreds of youth and youth-serving organizations in order to understand the youth leadership and innovation system in Canada (cf. MaRS Studio Y, 2017). The second and third case studies are those profiled by Ryan and Leung (2014).

    In order to interpret and analyze the chosen case studies, I turn to the methodology of phenomenological hermeneutics (Eberle, 2014, p. 196; cf. Wernet, 2014). Phenomenological hermeneutics are appropriate as I have access to the described phenomena of the systemic design projects captured by the chosen cases, but these phenomena are not explicitly captured with reference to emic or etic perspectives – thus some construction of the inherent emic or etic data is necessary in order to make judgments about the perspectives found in the projects.

    In each case, I will use identify phenomena representing the practice of emic (or etic) understanding in the research orientation of the work, as acknowledged by the above framework. In each case, I will examine the step-by-step procedure and any associated notes about the experience of the researchers and participants involved. In each step or experience, I will look for evidence of the four steps of emic understanding or the six techniques of emic validation reported above.

    # References

    Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3), 124–130. https://doi.org/10.1207/s15430421tip3903_2

    Eberle, T. S. (2014). Phenomenology as a Research Method. In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Analysis (pp. 184–202). Los Angeles, Calif. [u.a.]: Sage. Retrieved from https://www.alexandria.unisg.ch/228374/

    Flyvbjerg, B. (2006). Five Misunderstandings About Case-Study Research. Qualitative Inquiry, 12(2), 219245. https://doi.org/10.1177/1077800405284363

    Geertz, C. (1973). The interpretation of cultures: Selected essays (Vol. 5019). Basic books.

    Jones, P. (2015). Design Research Methods for Systemic Design: Perspectives from Design Education and Practice. Proceedings of the 58th Annual Meeting of the ISSS - 2014 United States, 1(1). Retrieved from http://journals.isss.org/index.php/proceedings58th/article/view/2353

    Kimbell, L. (2011). Rethinking Design Thinking: Part I. Design and Culture, 3(3), 285–306. https://doi.org/10.2752/175470811X13071166525216

    Lukyanenko, R., & Parsons, J. (2012). Conceptual modeling principles for crowdsourcing (pp. 3–6). ACM. https://doi.org/10.1145/2390034.2390038

    MaRS Studio Y. (2017). A strategic framework for youth leadership & innovation in Canada: Insights from the 2016 National Youth Leadership and Innovation Strategy Summit. Toronto, ON. Retrieved from http://www.studioy.marsdd.com/wp-content/uploads/2016/12/MaRS_NYLISstrategic_framework_Final.pdf

    Ryan, A. (2014). A Framework for Systemic Design. FORMakademisk–research Journal for Design and Design Education, 7(4). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/787

    Ryan, A., & Leung, M. (2014). Systemic Design: Two Canadian Case Studies. FormAkademisk - Research Journal of Design and Design Education, 7(3). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/794

    Scepanovic, S. (2018). Data science for sociotechnical systems - from computational sociolinguistics to the smart grid. Aalto University. Retrieved from https://aaltodoc.aalto.fi:443/handle/123456789/30187

    Second Muse, Intel, & Vibrant Data. (2016, May 11). What Your Data Says: Post-Secondary Education Mapping Survey Highlights. RECODE. Retrieved from http://re-code.ca/whats_happening/watch-recodewebinar-what-your-data-says/

    Wernet, A. (2014). Hermeneutics and Objective Hermeneutics. In U. Flick, The SAGE Handbook of Qualitative Data Analysis (pp. 234–246). SAGE Publications, Inc. https://doi.org/10.4135/9781446282243.n16

systems

17 notes with this tag (showing first 10 results)

  • A quick sketch of an interdisciplinary systems model

    Published Apr 24, 2020

    # Why are we exceeding the Earth’s carrying capacity?

    This is a quickly-sketched model created from a breakout group conversation during the MUN School of Graduate Studies’ “Earth’s Carrying Capacity” dialogue.

    Download the model

  • Systemic lessons from South Korea's Patient 31

    Published Mar 30, 2020

    This changed with the emergence of “Patient 31.”

    Reuters’ coverage of the “Korean clusters” provided the world with a vivid glimpse of the volatility of COVID-19. One person showed poor judgement, and in turn caused cascading catastrophe in her communities.

    Events like the COVID-19 pandemic are thankfully rare. Moments like these—when a lot happens all at once, and the experience is shared by a collective—shape future history like nothing else. We are learning a lot from this. Not only are epidemiologists now a famous profession, but we’re all learning exactly what it takes to provide good healthcare, what good governance looks like, how public health is community health, and more.

    Patient 31 holds a simple lesson for systemics: the fragility of apparently solid social systems. South Korea seemed to do everything right. Yet, due to the volatile nature of this particular socio-health system, a single “free radical” caused immense damage.

    Similar volatility is evident—but more subtle—in other social systems. Consider how memes spread. Our massive communities may seem immovable at times, but it’s clear that the wrong (or right) phenomena can spread rapidly and deeply.

    Stay safe.

  • What part of 'viral' content makes platforms want to encourage its spread?

    Published Jan 23, 2020

    The Twttr prototype app gave me another feedback form today. It’s been my habit to complain, at every opportunity, about the trends page you have to engage with whenever you go to the Search tab. I feel a little bad for the designers and developers, because the beta is really all about how conversations on Twitter look and feel. Still, this feedback form was no different. Here’s what I wrote in the “Dislike” section:

    I wish I could control the trends page.

    It is the absolute worst part of my Twitter experience. It just feels… unhealthy. Like going through a grocery store magazine aisle. Sure, some of the headings are instructive or inspiring, but many are gross, irrelevant, or completely malignant gossip.

    The experience is also invasive. Because trends are forced upon you when you intend on searching for something specific, and because they’re algorithmically-tunes to be as attention grabbing as possible, it’s easy to be distracted and forget why you even entered the search pane. I never explicitly consent to learning about celebrity gossip or US politics when I use Twitter. If I tap on some of those topics, it’s not because I want to. It’s because it’s malicious click bait. In turn, it’s corrupt to design an experience that drags the user through it repeatedly.

    Sure, this content is viral. But shouldn’t we be inoculating against viruses, not encouraging them to spread?

  • The Demon Haunted World

    Published Jan 23, 2020

    I have a foreboding of an America in my children’s or grandchildren’s time—when the United States is a service and information economy; when nearly all the manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and what’s true, we slide, almost without noticing, back into superstition and darkness…

    Carl Sagan, as quoted by @Andromeda321 in this interesting Reddit thread on the regretful trends of the 2010s.

    The thread discusses the growth of anti-intellectualism and conspiracy theories. I’m reminded of this timeless Medium post about how hating Ross in Friends became a meme in and of itself, reinforcing the persecution of science in the ’90s. From David Hopkins:

    I want to discuss a popular TV show my wife and I have been binge-watching on Netflix. It’s the story of a family man, a man of science, a genius who fell in with the wrong crowd. He slowly descends into madness and desperation, led by his own egotism. With one mishap after another, he becomes a monster. I’m talking, of course, about Friends and its tragic hero, Ross Geller.

    […]

    If you remember the 1990s and early 2000s, and you lived near a television set, then you remember Friends. Friends was the Thursday night primetime, “must-see-TV” event that featured the most likable ensemble ever assembled by a casting agent: all young, all middle class, all white, all straight, all attractive (but approachable), all morally and politically bland, and all equipped with easily digestible personas. Joey is the goofball. Chandler is the sarcastic one. Monica is obsessive-compulsive. Phoebe is the hippie. Rachel, hell, I don’t know, Rachel likes to shop. Then there was Ross. Ross was the intellectual and the romantic.

    Eventually, the Friends audience — roughly 52.5 million people — turned on Ross. But the characters of the show were pitted against him from the beginning (consider episode 1, when Joey says of Ross: “This guy says hello, I wanna kill myself.”) In fact, any time Ross would say anything — about his interests, his studies, his ideas — whenever he was mid-sentence, one of his “friends” was sure to groan and say how boring Ross was, how stupid it is to be smart, and that nobody cares. Cue the laughter of the live studio audience. This gag went on, pretty much every episode, for 10 seasons. Can you blame Ross for going crazy?

    People in the Reddit thread point out that these seemingly recent trends have been taking root for a long time. While this is true, it’s also true that (just like seemingly everything else) these phenomena have been moving much faster and growing much larger in recent years. Which leads to a curious tangent: how do accelerated scales of change play on our biases? Does the interaction between these biases and our accelerated experiences change our perception of the world?

  • The ‘Amazon effect’ is flooding a struggling recycling system with cardboard

    Published Jan 23, 2020

    Chinaʼs 2017 decision to turn away Americaʼs trash has left the recycling industry reeling as it figures out what to do with all the packaging online shoppers leave behind.

    Recycling is a funny thing. For me, it’s almost a guilt-free act. “Sure, I’m using all of these boxes, but they’re recycled, so who cares?” But increasingly recycling and the trash bin seem like equivalent destinations. It’s even imaginable that recycling is worse, because recycled objects might travel farther before being dumped into a landfill anyway.

    “Itʼs very difficult for American material recovery facilities to satisfy that standard because Americans put plastic bags and chewing gum and bowling balls and dirty diapers and everything else you can imagine into the recycling containers,” Biderman says. The strict rules also apply to plastic and other recyclables, but cardboard and mixed paper have seen the sharpest drops in prices.

    I’m tempted to blame people: “It’s too bad we can’t be more considerate. Have you ever looked in the recycling bins in public receptacles?” Et cetera. But really, we should be designing systems that make this easy—or incentivize good behaviours somehow. Either way, the current situation is insufficient:

    There has also been a noticeable shift in the source of the cardboard, says Coupland: itʼs coming from peoplesʼ homes instead of brick-and-mortar businesses. Thatʼs bad news, since retailers are less likely to generate cardboard thatʼs too filthy to be recycled. Consumersʼ cardboard boxes are often mixed with other, dirty recyclables like ketchup bottles or soda cans that spill their contents over the cardboard. On average, about 25 to 30 percent of the materials picked up by a recycling truck are too contaminated to go anywhere but a landfill or incinerator, Coupland says.

  • TED and YouTube launch global climate initiative

    Published Jan 23, 2020

    Anyone, anywhere can propose an idea. YouTube creators will help spread the word, and the best proposals could be put into motion with the help of businesses, policymakers, and and celebrities supporting the initiative.

    The initiative will culminate in a summit in Bergen, Norway next October to share the solutions that came out of the effort. Countdown will work with a panel of experts and scientists to vet proposals, and the strongest will be turned into TED talks. The talks will be filmed at the summit in Norway, in front of “a hand-picked audience capable of turning those ideas into action,” according to a press release.

    An interesting partnership, and yet another example of “crowdsolving”: trying to find solutions to wicked problems via the mobilizing power of the Internet.

    I certainly expect to see some concepts from Drawdown on stage.

  • Systems Practice, Abridged

    Published Jan 23, 2020

    # Systems Practice, Abridged

    For serious system mapping work, spending [significant] time studying, thinking about, and mapping your system helps ensure you are addressing root causes rather than instituting quick fixes. In the long term, the time and resources you invest in Systems Practice will pay dividends.

    But what if youʼre not quite sold on the Systems Practice methodology yet? What if you havenʼt encountered systems thinking before and just want to dip your toes in? Or what if youʼre an expert or an educator with only a few hours to introduce Systems Practice to a fresh new group of systems thinkers?

    I have been in the latter situation, and it’s a challenge. In my experience, people who are wholly new to systems thinking can take a lot of time to acclimate to the mindset. But! If, as a teacher, you can’t illustrate the benefits quickly, it’s easy to disengage.

    So, I’m glad this exists. This is a wonderful new resource from Kumu’s Alex Vipond that helps walk you through systems and Kumu’s tools at the same time.

design

16 notes with this tag (showing first 10 results)

  • Theory of Systemic Change and Action

    Last updated Mar 7, 2023 | Originally published Mar 7, 2023

    Theories of Change are one of the fundamental tools of changemakers and program evaluation (Mackinnon, 2006). However, when addressing wicked problems (Rittel & Webber, 1973), theories of change are too reductive and linear to properly account for the systemic phenomena, structures, and dynamics that perpetuate the issues we’re trying to address (Murphy & Jones, 2020).

    Theories of Systemic Change and Action (ToSCA) are a systemic design tool that combine theories of change with systemic understanding. The result is a theory of change that is useful for understanding, communicating, and evaluating systemic change projects.

    Here’s a rough guide to develop a ToSCA:

    1. Model the system (e.g., with causal loop diagrams; Kim, 1992).
    2. Develop systemic strategies from the model.
    3. Reorganize the modelled phenomena. From left to right:
      1. Capability building and resource mobilization for the initiative (Inputs)
      2. Inteventional activities the initiative will take on (Activities)
      3. Immediate outputs of those activities (Outputs)
      4. Results of those outputs on the overall system (Outcomes)
      5. Downstream effects of those outcomes on higher-system structures (Impacts)
    4. Reiterate on step 3 as necessary.

    The resulting diagram will look somewhat like an iceberg model (Stroh, 2015, p. 46] on its side: visible events and behaviour are on the left, while the actual patterns and structures in the system fall to the right.

    The ToSCA can then be simplified as necessary to suit different needs. For instance, if presenting the model briefly to a potential funder, you may want to collapse major feedback loops into one element on the model with a “loop” icon. This way you can still show inputs and outputs on that loop while obscuring the complexity within it for the purposes of the presentation.

  • A Case Study of Theories of Systemic Change and Action — The Ecotrust Canada Home-Lands initiative

    Last updated Mar 7, 2023 | Originally published Mar 7, 2023

    In this presentation, we reported on a case study of the Ecotrust Canada Home-Lands initiative. Lewis and I worked with Ecotrust Canada to understand the challenges they were addressing from a systemic design lens and, using that approach, to develop a theory of systemic change and action for the initiative.

    An interesting development in the work was the development of a novel systemic evaluation technique: resonance and dissonance tests. The tests were designed as a way of testing our understanding of the system without interrupting or intruding on the processes of the initiative. The general idea behind resonance and dissonance tests is to identify a set of phenomena in your understanding of the system and to search for disconfirming evidence that those phenomena are complete and accurate. So, for instance, if you think a key phenomenon in the system is “community distrust of bureaucracy”, look for examples of the community trusting bureaucracy. If you can’t find any, it increases the integrity of the theory you’ve created.

  • Leverage theory

    Last updated Mar 7, 2023 | Originally published Feb 24, 2023

    We seek leverage to find the best ways of making change.

    Leverage points are places in systems where a little effort yields a big effect (Meadows, 1997). They are also ideas that help us grab on to strategic ways forward when we’re working in complexity (Klein & Wolf, 1998).

    Acting on leverage points may accelerate systemic change towards progress and reform, but acting on the wrong ones may instead accelerate systemic change towards regression and deformity. Well-designed leverage strategies may be catalyzing or even transformative, but poorly designed ones may merely be futile (figure 1).

    One way of finding leverage points is to think through your system with reference to Meadows’s (1997) 12 types:

    Table 1. Twelve types of leverage points, in order of increasing power (adapted from Meadows, 1997).

    Twelve types of leverage points, in order of increasing power Example
    12. Constants, parameters, numbers (such as subsidies, taxes, standards) Wages, interest rates
    11. The sizes of buffers and other stabilizing stocks, relative to their flows. Current levels of debt/assets
    10. The structure of material stocks and flows (such as transport networks, population age structures) An individual’s financial structure (e.g., fixed costs and incomes)
    9. The lengths of delays, relative to the rate of system change How long it takes to find a higher-paying job
    8. The strength of negative feedback loops, relative to the impacts they are trying to correct against Rising costs of living vs. fixed income
    7. The gain around driving positive feedback loops Recession causing reducing spending
    6. The structure of information flows (who does and does not have access to what kinds of information) How aware you are of impending recession/future rising costs
    5. The rules of the system (such as incentives, punishments, constraints) Who suffers as a result of poorly-managed recessions
    4. The power to add, change, evolve, or self-organize system structure Central banks, Ministries of Finance
    3. The goals of the system GDP Growth
    2. The mindset or paradigm out of which the system—its goals, structure, rules, delays, parameters—arises Growth above all
    1. The power to transcend paradigms Sustainable development, flourishing

    Another approach, which may be complementary to the above, is to model the system as a causal loop diagram (e.g., Kim, 1992) and then to conduct leverage analysis (Murphy & Jones, 2020) on the model.

    An understanding of leverage in a system allows us to generate systemic strategies (Murphy & Jones, 2020). These strategies can also be adapted into Theories of Systemic Change (Murphy & Jones, 2020).

    # Background

    Donella Meadows (1997) popularized the idea of leverage in systemic change with her essay “Leverage Points: Places to Intervene in Complex Systems.” She proposed a typology of phenomena in a system, suggesting that acting on certain types of phenomena are higher-leverage than others.

    In an article published in the Contexts journal of systemic design, I challenged Meadows’s (1997) paradigm, proposing a few other possible ways of viewing leverage. My aim was to link the search for leverage directly to the design of powerful strategies for systemic change, and to propose a few ways forward in advancing our understanding of leverage in complex systems.

  • Using leverage analysis for systemic strategy

    Last updated Mar 7, 2023 | Originally published Jun 21, 2020

    The map represents your current mental model of how this system works.

    Leverage analysis examines the patterns of connection between phenomena (using algorithms adapted from social network analysis and graph theory) in order to present relative rankings of the phenomena of the system.

    These rankings are entirely dependent on the structure of the map. All phenomena are equal, and all connections are equal. It is theoretically possible to encode the degrees to which one phenomena influences another in strict mathematical terms and formulae. In turn, we could represent the map as a systems dynamics model and use it to simulate the behaviour of the system. However, this is usually impractical, especially with imprecisely-understood or hard-to-quantify concepts (e.g., what exactly is the rate of change in wildlife due to climate change, or how exactly does culture influence conspicuous consumption?)

    For this reason, using leverage analysis is a fuzzy procedure. It depends on your intuition. Fortunately, the goal of leverage analysis is not to inductively estimate how the system will change, nor deductively falsify hypotheses about the system. Instead, using leverage analysis for strategic planning involves abductive logic: the generation of creative, useful conclusions from a set of observations.

    The goal here is to look at the model as it is rendered and to think creatively about strategic opportunities. Broadly, this means asking several questions:

    • “What is missing?”
      • If some major gap in the logic of the model is missing, it means that the associated phenomena haven’t been adequately discussed in this process. Why is that? What might it mean for strategic planning?
    • “What must be true?”
      • If this is how the system currently works, what must be true about how it should work?
    • “Where do we work?”
      • Based on your organization’s strategic capabilities and advantages, what phenomena do you hold influence over? How do the effects you have on the system relate to these phenomena?
    • “What do we aim to influence?”
      • In other words, what phenomena do you really want to change? In what way should they change?

    These questions can be answered via the following process.

    # Developing Systemic Theories of Change

    The systems map represents a kind of high-complexity theory of change: it describes how all of these phenomena interlock and respond to one another. We can therefore use leverage analysis to weave systemic theories of action:

    1. Identify the goal phenomena. What do we want to influence? What’s the ultimate impact we aim to have?
    2. Identify the opportunities within our control. What phenomena are we already influencing? What could we be influencing without developing a lot of new capacity?
    3. “Walk” the paths on the map between your chosen opportunities, any possible high-leverage phenomena, and your goals. As you do:
      1. Identify any key strategic options along the path. What kinds of activities or programs could you engage in to influence these phenomena in the right way?
      2. Identify any feedback loops. How do these paths grow, shrink, or maintain balance over time?

    The chains of phenomena (and any loops they connect with) that result from the three steps above are the seeds of systemic strategy. Use them to identify key intervention points for programming (e.g., how might you take advantage of high-leverage phenomena? how might you address bottlenecks?), signals for monitoring and evaluation, and to communicate your theory of change/theory of action to others.

  • Why the grass is greener: Making sure that shiny new alternative tool is actually going to help you

    Last updated Nov 3, 2022 | Originally published Nov 3, 2022

    “The grass is always greener on the other side.” The popular idiom discourages whoever’s listening from seeking out alternatives, suggesting that other options always look better from wherever we’re currently standing. But it has a funny problem: nobody ever explains why the grass is greener on the other side.

    That’s because it isn’t. The truth is that your side is just yellower/trampled on/eaten… and that’s because you’re on it.

    Moving to a different place will be fine at first. Then you’ll use it, too, and eventually it’ll look the same as where you started.

    (In this metaphor, you’re a goat. 🐐)

    In workflow design, in addition to the novelty of “shiny new object,” new and alternative tools are great simply because they don’t have the cruft you’ve built up in the old tool. That cruft might be noisy notes, a lifetime of guilt-inducing task management, or even just bad habits and behaviours. The problem isn’t the tool. It isn’t you, either. It’s you and the tool.

    So, after switching, the problems seem to go away… only to re-emerge (possibly in a new form) later because the issues are generated by your usage, not by the tool.

    The solution is to fall in love with the problem, not the (shiny, potential) solutions.

    1. Determine what your issues actually are, and try to figure out why they’re happening.
    2. Then, abstractly identify how you might be able to mitigate the problems.
      • Don’t say “I’ll use Bunch,” say “If I standardize certain work spaces on my computer, I can develop muscle memory for using those workspaces, reducing distraction and allowing me to spend less cognitive energy on finding everything I need to get engaged.”
    3. Last, identify some tests or success conditions that will tell you whether the solution is actually working. This’ll help minimize irrational perspectives on how well the honeymoon stage is going.

    Only after taking those three steps should you choose a tool. Find something that can implement the abstract principles you’ve articulated, and be sure to follow-through on the tests.

    In doing this, you’re actually creating and implementing a rough design theory. You’re using design science to make your work as easy and engaging as it can be! High-five for that.

  • Keeping the buzz in buzzwords

    Published Jan 23, 2020

    A thought-terminating cliché limits conversation by capturing a complex (but potentially debatable) subject within a reductive term or phrase. Merlin Mann references this idea in episode 164 of Back to Work when discussing curiosity and buzzwords.

    Thought-terminating clichés can be used to avoid discourse on a subject: by never unpacking the components of an idea that are debatable, those components go unexplored. They can also be exploited to veil ignorance or illogic—the speaker can state the complex term and allow the implication to have impact without contextualizing/explaining it while the intimidated audience shies away from critique or questioning.

    This explanation makes the phenomena seem villainous, but many of us are prone to committing these crimes—through buzzwords! Buzzwords are terms that catch on because they represent something exciting to a discourse. Then, because they’re popular, they get used frequently, by many people. Because they are somewhat novel, these different uses attach slightly different meanings to the same word. Eventually the buzzword’s overused (reducing the novelty, and therefore the impact of its meaning) and/or overloaded with meaning.

    Most of our buzzwords were real things at one point (and sometimes they still are). When buzzwords are used effectively they allow a good conversation to move faster between speakers who have the same mental models about the buzzwords.1

    Sometimes, however, buzzwords are said to represent concepts that aren’t fully understood by everyone in the conversation. When my meaning of the word “design thinking” differs from yours, but we both refer to design thinking in conversation nonetheless, we can run into trouble.

    In these situations, buzzwords obfuscate the ideas we’re actually talking about. In my experience, we also know when we’re using buzzwords. We can guess at when others are using them, too. As a result, the conversation loses meaning, and we lose trust in the conversation.

    Buzzword meaning space. The three colored shapes are three different meanings attached to the same buzzword.

    # Dealing with buzzwords

    So what can we do?

    Well, the easy thing to do is to clarify. When you use a phrase with many potential interpretations, try to clarify how you’re using the phrase. When others use words that may have multiple meanings, ask specific questions about what they actually mean. This clarification might seem like extra work, but it only needs to happen when terms are first invoked—and it’ll prevent lost time and energy due to the consequences of thought-termination later on.

    More importantly, though, we should try to avoid thought-terminating clichés altogether. Take time to break down the concepts you’re talking about in concrete terms. Explain them in ways you haven’t heard before to avoid relying on trite metaphors and anecdotes. If you can really get at what you mean, your language will be minimally re-interpretable: that is, it should be near-impossible to understand your explanations differently from how you intended.

    As a result, your communication will become more impactful. The conversations you participate in will have more novelty, too, making it more exciting to discuss the ideas you’re sharing. This may result in more buzzwords emerging, but that’s okay—use the same approach to break those down, too.

  • Beautiful is good and good is reputable

    Published Jan 23, 2020

    # Beautiful is Good and Good is Reputable: Multiple-Attribute Charity Website Evaluation and Initial Perceptions of Reputation Under the Halo Effect

    The halo effect is essentially how positive—but irrelevant—traits influence our perception of what the thing with the halo actually says or does. These authors explored how charities manifest the halo effect on their websites, and find evidence for four varieties of halo effect.

    this study employs charity websites as a multi-attribute donation channel consisting of three attributes of information content quality (mission information, financial information, and donation information) and four attributes of system quality (navigability, download speed, visual aesthetics, and security). Based on the proposed framework, this study proposes four types of halos that are relevant to charity website evaluation —collective halo (attribute-to-attribute), aesthetics halo (attribute-to- dimension), reciprocal-quality halo (dimension-to-dimension), and quality halo (dimension-to-dimension)

  • Level Up: Leveraging Skill and Engagement to Maximize Player Gameplay

    Published Nov 29, 2019

    # “Level Up”: Leveraging Skill and Engagement to Maximize Player Gameplay

    We find that high-, medium-, and low-engagement-state gamers respond differently to motivations, such as feelings of effectance and need for challenge. In the second stage, we use the results from the first stage to develop a matching algorithm that learns (infers) the gamer’s current engagement state “on the fly” and exploits that learning to match the gamer to a round to maximize game-play. Our algorithm increases gamer game-play volume and frequency by 4%–8% conservatively, leading to economically significant revenue gains for the company.

    As ever with this kind of mechanism, are we sure we want this to exist..? The potential is no doubt powerful. Imagine interactive TV shows that modulate what they’re presenting based on readings of the viewer… Hrm.

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  • Finding the Emic in Systemic Design

    Published Oct 26, 2018

    # Finding the Emic in Systemic Design

    A paper presented at RSD7 in Turin, Italy.

    Download the slides

    # ABSTRACT

    I argue that an under-emphasized but crucial variable of success in systemic design is the perspective through which systemic design processes are implemented and executed. While rooted in design (a consciously empathetic discipline, especially in recent years; cf. Kimbell, 2011), it is easy for systemic designers to conduct the research required for their projects in externalized ways. These approaches risk misrepresenting the stakeholders who contribute to projects and, in turn, they are a danger to the potential impact of these misresearched problem systems. I propose to advance a theoretical argument for this danger, the development of an assessment framework to check whether an internalized perspective has been effectively achieved, and provide a proof of concept of this framework through hermeneutic case study analysis.

    As I will show, systemic design processes that are not executed with the direct and explicit engagement of stakeholders – to the extent of achieving an emic (or from within) understanding of the system – may be flawed at their foundation. By fostering recognition of the importance of an emic perspective, and by providing a framework of principles, practices, and process to accomplish systemic design with this perspective, I hope to ensure that systemic design processes are as accurate and valid as possible with respect to the stakeholders of the system.

    This is not to suggest that systemic design practice is “too etic”. In fact, with roots in design, systemic design is often deliberately emic. Systemic designers make use of designerly tools that help the researcher to build empathy with system stakeholders (e.g., soft systems methodology, critical systems heuristics, appreciative inquiry; Jones, 2014). They often seek to engage stakeholders in the systemic design process and include reflective analysis of what has been learned in order to assess where deeper engagement with the system is required (Ryan, 2014). That said, with the advent of crowdsourcing (the facilitated involvement of the general public in problem solving, usually using online tools; Lukyanenko & Parsons, 2012) and data science (the use of computational tools to analyze and understand large quantities of data; cf. Scepanovic, 2018), it is likely that data-driven methods will increasingly influence systemic design practice. One recent example sought input from hundreds of people to identify opportunities for change in Canadian post-secondary systems through an iterative online survey (cf. Second Muse, Intel, & Vibrant Data, 2016). This data-driven direction is a powerful opportunity, of course, but it underscores the need to develop principles and best practices for assessing and supporting emic understanding as we gain more data from these tools.

    This proposal consists of two steps. First, I will look to the principles and theorists of ethnography to develop a framework for assessing the emic/etic perspective of a given research project. Namely, Geertz’ “Thick Description: Toward an Interpretive Theory of Culture” (found in The Interpretation of Cultures, 1973, chapter 1) provides a foundation for the process of emic research, while Creswell and Miller (2000) provide a set of procedural principles for emic validity. Taken together, we generate a critical research framework with which we may assess a given research project’s emic perspective. Second, I will provide a proof-of-concept of this framework (and its theoretical underpinnings) via a casebased assessment of three systemic design projects. Case studies provide an effective venue for learning about the context-dependent manifestations of the phenomena being studied (Flyvbjerg, 2006). One of these case studies is one I have developed through my experience in participating and contributing to the development of the Canadian National Youth Leadership and Innovation Strategy framework, which convened hundreds of youth and youth-serving organizations in order to understand the youth leadership and innovation system in Canada (cf. MaRS Studio Y, 2017). The second and third case studies are those profiled by Ryan and Leung (2014).

    In order to interpret and analyze the chosen case studies, I turn to the methodology of phenomenological hermeneutics (Eberle, 2014, p. 196; cf. Wernet, 2014). Phenomenological hermeneutics are appropriate as I have access to the described phenomena of the systemic design projects captured by the chosen cases, but these phenomena are not explicitly captured with reference to emic or etic perspectives – thus some construction of the inherent emic or etic data is necessary in order to make judgments about the perspectives found in the projects.

    In each case, I will use identify phenomena representing the practice of emic (or etic) understanding in the research orientation of the work, as acknowledged by the above framework. In each case, I will examine the step-by-step procedure and any associated notes about the experience of the researchers and participants involved. In each step or experience, I will look for evidence of the four steps of emic understanding or the six techniques of emic validation reported above.

    # References

    Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3), 124–130. https://doi.org/10.1207/s15430421tip3903_2

    Eberle, T. S. (2014). Phenomenology as a Research Method. In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Analysis (pp. 184–202). Los Angeles, Calif. [u.a.]: Sage. Retrieved from https://www.alexandria.unisg.ch/228374/

    Flyvbjerg, B. (2006). Five Misunderstandings About Case-Study Research. Qualitative Inquiry, 12(2), 219245. https://doi.org/10.1177/1077800405284363

    Geertz, C. (1973). The interpretation of cultures: Selected essays (Vol. 5019). Basic books.

    Jones, P. (2015). Design Research Methods for Systemic Design: Perspectives from Design Education and Practice. Proceedings of the 58th Annual Meeting of the ISSS - 2014 United States, 1(1). Retrieved from http://journals.isss.org/index.php/proceedings58th/article/view/2353

    Kimbell, L. (2011). Rethinking Design Thinking: Part I. Design and Culture, 3(3), 285–306. https://doi.org/10.2752/175470811X13071166525216

    Lukyanenko, R., & Parsons, J. (2012). Conceptual modeling principles for crowdsourcing (pp. 3–6). ACM. https://doi.org/10.1145/2390034.2390038

    MaRS Studio Y. (2017). A strategic framework for youth leadership & innovation in Canada: Insights from the 2016 National Youth Leadership and Innovation Strategy Summit. Toronto, ON. Retrieved from http://www.studioy.marsdd.com/wp-content/uploads/2016/12/MaRS_NYLISstrategic_framework_Final.pdf

    Ryan, A. (2014). A Framework for Systemic Design. FORMakademisk–research Journal for Design and Design Education, 7(4). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/787

    Ryan, A., & Leung, M. (2014). Systemic Design: Two Canadian Case Studies. FormAkademisk - Research Journal of Design and Design Education, 7(3). Retrieved from https://journals.hioa.no/index.php/formakademisk/article/view/794

    Scepanovic, S. (2018). Data science for sociotechnical systems - from computational sociolinguistics to the smart grid. Aalto University. Retrieved from https://aaltodoc.aalto.fi:443/handle/123456789/30187

    Second Muse, Intel, & Vibrant Data. (2016, May 11). What Your Data Says: Post-Secondary Education Mapping Survey Highlights. RECODE. Retrieved from http://re-code.ca/whats_happening/watch-recodewebinar-what-your-data-says/

    Wernet, A. (2014). Hermeneutics and Objective Hermeneutics. In U. Flick, The SAGE Handbook of Qualitative Data Analysis (pp. 234–246). SAGE Publications, Inc. https://doi.org/10.4135/9781446282243.n16

  • Innovation is a buzzword

    Published May 8, 2017

    Innovation is a Buzzword (but it doesn’t have to be)

    Notes, slides, and the Innovation Auditing guide presented at the talk are found below.

    # The research

    The research presented during the talk is discussed on the following pages:

    An innovation pop quiz

    # Slides

    Find a PDF of the slides I presented at the link below. Beware: the animations don’t translate well to print, so some of the pages have graphical issues.

    Download the slides

    # Innovation Auditing

    Innovation Auditing is a simple procedure that individuals, organizations, and governments can use to detect the gaps in the innovation process they seek to support.

    See the PDF guide by clicking the link below.

    Download the guide to innovation auditing

  • Creative Education Futures

    Published Apr 6, 2017

    # Creative Education Futures

    What are the futures of art and design schools in Canada?

    An abstract visualization of futures signals, trends, and drivers.

    In 2015, I supported Kinetic Café in developing OCAD University’s latest Vision and Mission statements. As part of that work, I helped scan for signals, trends, and drivers in art and design school futures. Our scan revealed five important drivers of change: reforming education, creative economies and cultures, new geographies, empowering technologies, and conscious collective.

    A walkthrough of this work is visualized and embedded below. If it doesn’t display, you can visit it directly here.

  • Innovation Education

    Published Jan 13, 2017

    # Innovation Education

    What is innovation? How do we define innovation, its outputs and processes, and what are the skills and competencies necessary to practice and excel in innovation?

    Read the full paper.


    Many strategies and policies, both federal and provincial, have attempted to improve Canada’s innovation capacity in the last few decades. Universities and colleges are often discussed in these strategies for their role in facilitating new partnerships and in developing (potentially) actionable research.

    It is rare, however, for these strategies to recognize education plays in the creation of innovators.

    Abstract shapes for decoration.

    This is counterintuitive: education is an obvious mechanism with which to develop the knowledge and abilities of a population. Yet we lack a holistic understanding of what it takes to practice innovation, let alone the kinds of curricula that might provide those skills and competencies. Moreover, we are inconsistent in the definitions and language we use to define innovation—often obsessing over technology and commercialization. We tend to assume innovation comes from research and development processes, and that innovators are simply highly skilled people.

    Presented here is the result of an intensive review of reports, strategies, policy, and theory on innovation in the Canadian context. This literature was scanned and coded—inspired by the ethnographic methods of grounded field theory—in order to synthesize a holistic theory of innovation.

    media/_innovation-domains-constructs-and-outcomes.jpeg

    13 learning domains, 47 learning constructs, and 227 learning outcomes comprise a holistic model of innovation education. Use the interactive map below to explore the model yourself!

    This theory includes a universal definition of the innovation process, a recasting of different focuses of innovation as innovation orientations, a comprehensive model of the innovation process, and a synthesis of innovation skills and competencies into 13 learning domains, 47 learning constructs, and 227 learning outcomes. These tools provide utility for policymakers and educators in pursuit of understanding and improving innovation capacity. In particular, the model of innovation education is the most comprehensive of its kind, providing an extensive set of concepts with which to understand education gaps and build curricula.

    Perhaps the most important contribution of this research, however, is the recognition that our conversations about innovation strategies and education reform must be aligned. **How exactly do people learn to be innovative, and how are our education systems currently facilitating that process? **With this study we have begun to seek answers to these questions, but there is much more work to do.

    If you use these ideas and learn something from your experience, or if you have thoughts on how to improve them, don’t hesitate to make suggestions and to share your work.

    Find resources on this page that detail the definitions and models of innovation developed through this research and how these concepts may be applied in innovation education programming.

    # Explore the Model

    Across dozens of perspectives on innovation, a set of 13 domains of skills and competencies emerged. These domains have been interactively visualized using Kumu.io. You can play with the work yourself by clicking the button. Use the controls in the bottom corners to focus on specific components of the model, to surface the innovation process so that you can explore both simultaneously, and more. Note that the visualization is best explored on a desktop!

    PLAY WITH THE VISUALIZATION

    # Learn more about the skills and competencies of innovation

    This document provides an executive summary of the insights and models on innovation education uncovered through this research and guides educators to put these concepts and tools to use.

    media/_curricula-guide.jpeg

    Get the Curricula Guide

    # Read the Research

    Read the full paper.

    Education reform presents an opportunity to improve innovation education and, in turn, advance innovation capacity. I synthesize the framing and strategy of resources from provincial, national, international, and theoretical perspectives on innovation in order to develop a holistic model of innovation and a curricula for innovation education. Then, I use systemic design to model Newfoundland and Labrador’s current education system and to suggest strategies for reform to enable improvement in Newfoundland and Labrador’s innovation education. Finally, I explore how systemic reform in Newfoundland and Labrador may serve as a systems laboratory for reform efforts in other jurisdictions.


    Innovation Education is a major research project presented to OCAD University in partial fulfillment of the requirements for the degree of Master of Design in Strategic Foresight & Innovation. This work was supported by the Social Sciences and Humanities Research Council of Canada.

  • Education Systemics

    Published Jan 13, 2017

    # Education Systemics

    What are the mechanisms of education systems change? How might we use systemic design to power a reform movement? What does systems thinking teach us about opportunities and barriers for education reform in Newfoundland and Labrador?

    Read the full paper.


    Education reform is not a new idea. Many organizations and initiatives have reimagined the schools, universities, and colleges that are the backbone of our economy and society—yet, to many, it seems like little has changed in recent decades.

    As Jal Mehta, Robert Schwartz, and Frederick Hess began their book on the subject, “if we keep doing what we’re doing, we’re never going to get there.” Traditional education reform approaches have depended on a best practices approach in what is glibly called a “silver bullet culture”.

    A single idea, found successful in a specific institution or district, becomes hailed as the be-all-end-all solution. This solution is then celebrated and championed across contexts until actors realize that expected results have not materialized, and reformers move on to the next silver bullet solution.

    This approach to education reform has not worked. According to Mehta, Schwartz, and Hess: “If we are to deliver transformative improvement, it is not enough to wedge new practices into familiar schools and districts; we must reimagine the system itself”. In other words, education systems change has been found to be more than difficult. It is a wicked problem: ill-defined, constantly fluxing, with many conflicting stakeholders and no true solutions.

    How can we provoke change in wicked problems?

    I argue that reforming education is a sociotechnical problem, involving human psychology; social, political, and economic factors; and complex interactivity–what Don Norman and Pieter Stappers have called a “DesignX” problem. These authors suggested that DesignX problems can only be solved through a process of muddling through, developing incremental sub-solutions through deep analysis. This deep analysis means partitioning the problem into modules and recognizing the intersecting dimensions of the problem.

    Peter Jones provides further advice for this kind of problem solving, defining an approach called systemic design. Systemic design integrates systems thinking and systemic methods–ways of understanding complex problems through the relationships of the phenomena and actors involved–with design thinking and design methods, applying human-centred design to these seemingly intractable, large-scale problems. With systemic design, Peter says, we use “known design [tools]—form and process reasoning, social and generative research methods, and sketching and visualization practices—to describe, map, propose, and reconfigure complex services and systems”.

    # Approach & Findings

    With this in mind, I turned to several types of modelling in order to use systemic design on the complex problem of education reform: process modelling, actor mapping, and causal loop mapping. In this modelling, I focused on Newfoundland and Labrador—my home province—and the education of innovation, examining how our system currently provides innovation learning and how we might do better.

    Finally, I applied centrality analysis – a quantitative approach to assessing leverage points and bottlenecks in networks and systems maps—in order to surface potential opportunities and challenges in the system. I examined four types of centrality analytics:

    • Reach efficiency takes an element’s reach (the proportion of the network within two steps of that element) and divides it by the number of neighbouring elements it has. Elements that score the most on this metric tend to be less connected but have high exposure to the rest of the system, making them low-hanging fruit for change efforts.

    • Betweenness assesses the number of times an element lies on the shortest path between two other elements. Elements high on the betweenness metric are bridges throughout the map, controlling the flow of phenomena throughout the system. This means that these elements may be bottlenecks or single points of failure.

    • Eigenvector centrality measures an element’s connectedness to other well-connected elements, computing an overall value that is an indicator of the element’s influence over the whole system.

    • Torque calculates each element’s reach efficiency weighted by its eigenvector centrality (e.g., how influential that element is). Elements with high torque should be relatively easy to impact (as they are not densely influenced by other phenomena in the map), but will impact the rest of the map substantially. These are key leverage points of change.

    The detailed methods and findings from each of these approaches are discussed in turn in the links below.

    # Process Modelling, Actor Mapping, And Causal Loop Mapping

    An abstract illustration of process modelling Process Modelling: Where might innovation learning come from?

    An abstract illustration of actor modelling Actor Mapping: who is involved in this system?

    An abstract illustration of causal loop mapping Causal Loop Mapping: how does the system behave?

    # Conclusions

    I aimed to see the education system for what it is in order to describe strategies for the transformative reform that Mehta, Schwartz, and Hess called for. The education system is therefore composed of a number of interrelated components, organized in a hierarchy, whose emergent phenomena lead to its own dynamics. Yet, many might say that this systemic chaos implies a system of constant change, while education is hallmarked for its derelict stagnancy in the 21st century. How is it that such a system has not evolved?

    Well, perhaps the system is not actually that broken. As eloquently argued by Ryan Burwell, an instructional designer at the MaRS Discovery District:

    The school system is not broken. It is perfectly aligned to provide equitable access to a canon of high-quality, standardized content with greater rigour and organization than any other knowledge delivery system we currently have. However, it is not designed to foster the problem-solvers, innovators and entrepreneurs that are becoming an increasingly significant part of the global economy. Incorrectly identifying this misalignment as a broken system has created a culture of fear and failure around education, leading to top-down reforms and increased numbers of mandatory programs.

    I return to Mehta, Schwartz, and Hess’ depiction of school reform’s silver bullet culture. Many stakeholders with competing interests and different priorities are invested in every debate on education systems change.

    Thus, there are many potential silver bullets–and many advocates for them. The misunderstanding of the problem described by Burwell and the complexities of education reform described by Mehta, Schwartz, and Hess perfectly capture the need for a systemic design-based approach to change.

    # Process Mapping

    From process modelling, it is clear that while NL’s education system currently offers some opportunities to learn certain constructs of innovation, the availability of these opportunities is not densely packed throughout their study. It is easy to recognize a dearth of access to the domains of Foresight and Scanning, Vision and Purpose, and Adaptability and Resilience. Further, the degree to students learn the domains and constructs of innovation skills from the public system remains unclear.

    Ultimately, now that these models exist, further analysis will be able to examine these constructs more closely as students progress through the system.

    This is especially true for many of the “optional” components of the broader education system. After school programs, hobbies, sports and recreation, volunteer and extra-curricular roles, self-directed learning, and employer training could each be vital sources of innovation education, but it was impossible to study these aspects of the system in any meaningful way in the present study. A dedicated effort should examine the availability of these sources and assess their utility for innovation learning.

    One research approach would be to survey learners along the learning journey, testing their abilities in the different constructs I’ve outlined. This ethnographic approach could reveal hidden truths: perhaps, for instance, certain regional cultures in the province actually provide powerful learning in design through a community culture alone.

    # Actor Mapping

    Systemic modelling reveals the power and wealth subsystems active amongst the actors of the education system.

    Centrality analysis of the power subsystem illustrates that parents and the provincial government have efficient influence on the system, and change that can mobilize those bodies of actors will quickly take shape.

    Meanwhile schools, the School Board, and educators have substantial global influence over the system–change efforts that engage these actors may be slow but momentous.

    Finally, power bottlenecks are educators; schools and school councils; and the Department of Education and Early Childhood Development. This suggests that these actors will ultimately need to be involved if any reform effort were to achieve success.

    Reach efficiency analysis of the wealth subsystem shows that the federal government, parents and students, and the provincial Department of Advanced Education and Skills each strongly influence the distribution of wealth. The Federal and Provincial Governments have powerful incentives with which to motivate and control reform efforts.

    Betweenness centrality revealed that the whole system is tightly linked, making it potentially volatile: economic issues in one component of the system may ripple out and impact the others.

    # Causal Loop Mapping

    Finally, these maps intimated a causal loop diagram illustrating how innovation education reform might happen in the public education system.

    Several loops and one archetype demonstrate significant effect over the system. The Low Definition loop describes an acceleration of the impact of ill-defined innovation on our ability to educate on it. The We Teach What We Know loop shows how a lack of innovation education leads to a lack of people capable of teaching it and vice-versa. The New Economy loop shows how economic shocks driven by drops in commodities pricing has raised our awareness of the importance of the innovation economy. The Innovation-driven Growth loop shows how innovation capacity will accelerate jobs in the knowledge economy, which will in turn drive our ability to create more innovators through education. The Limited Resources loop balances our ability to reform education for innovation due to a lack of funding for the reform effort due to austerity budgets, driven by drops in the price of oil. Finally, the R&D, Not Innovation archetype is an instance of the Fixes that Fail systems archetype, showing how a conflation of innovation with R&D efforts fails to improve our innovation capacity while also distracting from true innovation education.

    The result of centrality analysis on these causally-linked phenomena is rich with pragmatic insight.

    Three phenomena with efficient reach over the whole system are innovation learning from outside of the public education system, lack of emphasis on innovation education, and low price of oil. The former points to an accessible lever of change: introduce innovation education through extra- and co-curricular programs, volunteer and leadership roles, sports and recreation, or self-directed learning, and the system may catch up by offering its own programming to match. The leverage of a lack of emphasis on innovation education offers another route: increase awareness on innovation education in order to encourage the system to improve on it. Finally, low price of oil retains leverage as a dampener on the system: if the economy continues in recession, the system is less able to offer resources for reform efforts. 

    Other calls for reform is one force with substantial torque over the system, indicating that reformers must be co-opetitive with other education change efforts, else all reform efforts might fail due to competition with one another. The availability of accessible and practical models for innovation education is another high-torque element, however, elevating the potential of the present research to create change in the system. A third element with high torque is the generational shift in work, evidence that a substantial source of impetus for innovation education reform could come from changes in work and careers. 

    Finally, betweenness centrality offers a picture of the bottlenecks and points of failure within the system. Innovation capacity and innovation education are two forces semiotically central to the system, and thus it is intuitive that they will be slow to change, no matter what else is happening within the system. On the other hand, recognition of innovation deficiency, the perceived innovation gap, and the search for solutions to the innovation gap are three phenomena that are clear points of fragility in any systemic change effort. If the system does not recognize its deficiencies, perceive the gap in innovation capacity, or opt to search for solutions, reform efforts are liable to be frustrated.

    # Limitations

    Despite these clarion recommendations for systemic design, several limitations prevent wholesale adoption.

    One key limitation of the presented results of systems modelling is that the connections defined in these models are unquantified. In the refined actor map, for instance, it may be that educators have little power over their school councils, or perhaps the NL Federation of School Councils has far less lobbying capacity than the NL Federation of Teachers. Evaluating the strength of these connections and including these evaluations in our analytics would improve the acuity of those metrics substantially.

    As previously mentioned, if innovation learning is not coming from the public education system, it must be coming from somewhere else. Yet, these potential sources arguably include the whole of the human experience—as we have, after all, been learning to innovate since pre-history. Future research might take on an ethnographic approach to understanding the system, investigating different student-innovators and where they learned their innovation skills, or a longitudinal approach, following students as they become innovators through their years in the education system. These exercises fell outside the limits of the present research, unfortunately.

    Another limitation is that, while the scope and approach to mapping were designed to increase the variety of the system as much as possible, the mapping was still completed with the perspective of only this author. The representativeness of the systems models would therefore be strengthened considerably with Delphi-inspired methods as seen in previous research, bringing the mapping process to others in order to iteratively refine and the map from alternative stakeholders’ points-of-view.

    Another potential future study is to “bring the whole system into the room”. This would mean convening a group of stakeholders who were holistically representative of the actors of the system, engaging them in a systems modelling process to develop a map with their collective perspectives.

    # ACTIONS AND TAKEAWAYS

    # In The Short Term

    A few immediate actions stem from this research.

    # 1. Adopt A Model

    First, we must adopt a model of innovation skills and competencies.

    Regardless of whether the adopted model is the one developed through this project or another alternative, it is imperative that we begin to recognize the skills and competencies used by successful innovators. By identifying these skills, we will be capable of examining our weaknesses and, in turn, developing ways of resolving those weaknesses. To spur this discussion, I plan on sharing the models developed here widely. 

    # 2. Consider The Role Of Education In The Creation Of Canadian Innovators

    Second and in tandem, we must include the role of the education system in nurturing innovators in our provincial and national innovation strategies. Many approaches to innovation policy discuss the post-secondary education system with respect to its role in public-private partnerships and the commercialization of research. We must expand this role to include the development of innovation skills and competencies as well. In the near future I hope to meet with policymakers involved in the development of Newfoundland and Labrador’s innovation strategies to advocate for this approach there.

    # 3. Unite Education Reform Movements

    Education reform movements must be united in their calls for change. A host of movements relate to the notion of innovation education, from code.org (a non-profit urging computer science and programming education in K-12) to the 21st century learning movement (a pedagogical framework for the skills and knowledge necessary for the 21st century; cf. http://www.p21.org).

    The present research shows that these reform efforts may conflict, however, if they are brought forward asynchronously by their champions. It is therefore crucial that these efforts learn to “co-opete” (as in “co-opetition”) and engage educators and policymakers with aligned advocacy. I hope to work with the education systems change movements I already have relationships with in the immediate future in order to begin this dialogue.

    # In The Long Term

    As explored by David Stroh in Systems Thinking for Social Change, systems change is only possible when the actors of the system collectively recognize the tension between where the system is and where they want it to be.

    That realization isn’t possible, however, before the actors have even talked to one another—let alone come to consensus about a shared vision for the future. 

    We realize that Canada’s future prosperity is predicated on our ability to leverage the boons of our resource economy and evolve it into an “innovation rich” leader in the knowledge economy. Yet, as discussed at the beginning of this paper, the danger is that education’s role in this transformation has yet to be recognized in full. We are not talking about how to create innovators, let alone what strategies we should employ in doing so, or how the system is stuck in becoming better at innovation education. Worse, there are many simultaneously conversations happening in both education reform and innovation—conversations that compete with one another, threatening the potential of the whole.

    This research offers a model of the education system in Newfoundland and Labrador. Yet these models are untested, and as I have noted, the research is sorely lacking a futures perspective that observes both threats and strategic opportunities in our changing environment. 

    **How might we spark a collective, integrative discourse on innovation and innovation education? **Then, how might we elevate its importance such that collective action is taken—before we’ve missed the opportunities of the knowledge economy? How might we refine the systemic models, and how might we augment this work with a futures perspective, using environmental scanning to develop and integrate changing trends for strategic leverage? 

    These questions point toward a need for a powerful, strategic theory of change, and the willingness to muddle through. In other words, **this change will not come about through the efforts of ad hoc standalone initiatives like this one. **

    We need a sustained effort. We need a lab that brings together design science and systemic design, creating and testing designs of the system itself, making sure they are valid constructs of the concepts they are intended to represent, all while obeying the principles of systemic design. 

    This is not a new idea. Many have articulated the notion of social labs, design or change labs, or social innovation labs. In fact, the OECD’s Centre for Educational Research and Innovation seems to operate such an approach for systemic innovation in global education.

    I argue that Canada–or at least, Newfoundland and Labrador–needs to take a lab-based approach to navigating complex education reform in education. This lab must unite the perspectives, strategies, and actors currently engaged in similar pursuits; build, maintain, and refine models of the systemic change taking place; be engaged in environmental scanning and strategic foresight to monitor for both threats and opportunities; and prototype change initiatives, taking lessons back to these models and strategies.

    Only a dedicated, intelligent effort will help us build the education systems that will develop the skills and knowledge we need to answer the 21st century. 


    Innovation Education is a major research project presented to OCAD University in partial fulfillment of the requirements for the degree of Master of Design in Strategic Foresight & Innovation. This work was supported by the Social Sciences and Humanities Research Council of Canada.

change

12 notes with this tag (showing first 10 results)

  • Researchers detail huge hack-for-hire campaigns against environmentalists

    Published Jun 10, 2020

    The report concludes that the campaigns represent “a clear danger to democracy” and could allow powerful organizations to target their opponents. “The extensive targeting of American nonprofits exercising their first amendment rights is exceptionally troubling,” Citizen Lab’s report says.

    We didn’t want this part of cyberpunk sci-fi…

  • The Demon Haunted World

    Published Jan 23, 2020

    I have a foreboding of an America in my children’s or grandchildren’s time—when the United States is a service and information economy; when nearly all the manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and what’s true, we slide, almost without noticing, back into superstition and darkness…

    Carl Sagan, as quoted by @Andromeda321 in this interesting Reddit thread on the regretful trends of the 2010s.

    The thread discusses the growth of anti-intellectualism and conspiracy theories. I’m reminded of this timeless Medium post about how hating Ross in Friends became a meme in and of itself, reinforcing the persecution of science in the ’90s. From David Hopkins:

    I want to discuss a popular TV show my wife and I have been binge-watching on Netflix. It’s the story of a family man, a man of science, a genius who fell in with the wrong crowd. He slowly descends into madness and desperation, led by his own egotism. With one mishap after another, he becomes a monster. I’m talking, of course, about Friends and its tragic hero, Ross Geller.

    […]

    If you remember the 1990s and early 2000s, and you lived near a television set, then you remember Friends. Friends was the Thursday night primetime, “must-see-TV” event that featured the most likable ensemble ever assembled by a casting agent: all young, all middle class, all white, all straight, all attractive (but approachable), all morally and politically bland, and all equipped with easily digestible personas. Joey is the goofball. Chandler is the sarcastic one. Monica is obsessive-compulsive. Phoebe is the hippie. Rachel, hell, I don’t know, Rachel likes to shop. Then there was Ross. Ross was the intellectual and the romantic.

    Eventually, the Friends audience — roughly 52.5 million people — turned on Ross. But the characters of the show were pitted against him from the beginning (consider episode 1, when Joey says of Ross: “This guy says hello, I wanna kill myself.”) In fact, any time Ross would say anything — about his interests, his studies, his ideas — whenever he was mid-sentence, one of his “friends” was sure to groan and say how boring Ross was, how stupid it is to be smart, and that nobody cares. Cue the laughter of the live studio audience. This gag went on, pretty much every episode, for 10 seasons. Can you blame Ross for going crazy?

    People in the Reddit thread point out that these seemingly recent trends have been taking root for a long time. While this is true, it’s also true that (just like seemingly everything else) these phenomena have been moving much faster and growing much larger in recent years. Which leads to a curious tangent: how do accelerated scales of change play on our biases? Does the interaction between these biases and our accelerated experiences change our perception of the world?

  • The ‘Amazon effect’ is flooding a struggling recycling system with cardboard

    Published Jan 23, 2020

    Chinaʼs 2017 decision to turn away Americaʼs trash has left the recycling industry reeling as it figures out what to do with all the packaging online shoppers leave behind.

    Recycling is a funny thing. For me, it’s almost a guilt-free act. “Sure, I’m using all of these boxes, but they’re recycled, so who cares?” But increasingly recycling and the trash bin seem like equivalent destinations. It’s even imaginable that recycling is worse, because recycled objects might travel farther before being dumped into a landfill anyway.

    “Itʼs very difficult for American material recovery facilities to satisfy that standard because Americans put plastic bags and chewing gum and bowling balls and dirty diapers and everything else you can imagine into the recycling containers,” Biderman says. The strict rules also apply to plastic and other recyclables, but cardboard and mixed paper have seen the sharpest drops in prices.

    I’m tempted to blame people: “It’s too bad we can’t be more considerate. Have you ever looked in the recycling bins in public receptacles?” Et cetera. But really, we should be designing systems that make this easy—or incentivize good behaviours somehow. Either way, the current situation is insufficient:

    There has also been a noticeable shift in the source of the cardboard, says Coupland: itʼs coming from peoplesʼ homes instead of brick-and-mortar businesses. Thatʼs bad news, since retailers are less likely to generate cardboard thatʼs too filthy to be recycled. Consumersʼ cardboard boxes are often mixed with other, dirty recyclables like ketchup bottles or soda cans that spill their contents over the cardboard. On average, about 25 to 30 percent of the materials picked up by a recycling truck are too contaminated to go anywhere but a landfill or incinerator, Coupland says.

  • TED and YouTube launch global climate initiative

    Published Jan 23, 2020

    Anyone, anywhere can propose an idea. YouTube creators will help spread the word, and the best proposals could be put into motion with the help of businesses, policymakers, and and celebrities supporting the initiative.

    The initiative will culminate in a summit in Bergen, Norway next October to share the solutions that came out of the effort. Countdown will work with a panel of experts and scientists to vet proposals, and the strongest will be turned into TED talks. The talks will be filmed at the summit in Norway, in front of “a hand-picked audience capable of turning those ideas into action,” according to a press release.

    An interesting partnership, and yet another example of “crowdsolving”: trying to find solutions to wicked problems via the mobilizing power of the Internet.

    I certainly expect to see some concepts from Drawdown on stage.

  • MTA floods NYC subway entrance because ‘climate change is real’

    Published Jan 23, 2020

    An incredible story out of New York today, as reported by The Verge:

    A flooded subway entrance stopped Brooklyn commuters in their tracks yesterday. For four hours on Wednesday, the staircase leading down to Broadway Station in Williamsburg was blocked off and completely submerged. The sight was even stranger since it hadnʼt rained in New York City that day.

    The Transit Authority was testing adaptations they’d installed in case of real flooding. Still, I’m sure that the social/informational impact was felt, too.

    Also, the MTA’s sarcastic explanation is gold. From Twitter:

    We’re pivoting to submarines. ^JLP

  • Medical Crowdsourcing: Harnessing the 'Wisdom of the Crowd' to Solve Medical Mysteries

    Published Jan 23, 2020

    Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge.

    An interesting application of crowdsourcing. What’s the incentive for healthcare providers to participate, though? I’m not sure doctors can bill for participation in Figure 1. I think the main reason they engage at all is curiosity, and that would likely degrade if, as the authors of the linked study discuss, there was a lot of “noise” from uninteresting posts by patients who aren’t medically literate.

  • John Kerry, Arnold Schwarzenegger wage ‘World War Zero’ on climate change

    Published Jan 23, 2020

    Today former Secretary of State John Kerry and former California governor Arnold Schwarzenegger declared war on climate change. The two led an all-star cast of lawmakers and celebrities to launch an initiative called World War Zero, which aims to get individuals, businesses, and governments to drastically slash greenhouse gas emissions. The initiative, for now, boasts a lot of glitzy names without many details on how it will achieve its goal. Its bipartisan founding members — which include Bill and Hillary Clinton, Richard Branson, Jimmy Fallon, Cindy McCain, and Al Sharpton, and more than 70 other notable names — plan to hold 10 million “climate conversations” in 2020, The New York Times reported over the weekend.

    Seems like an incredible effort. And it’s an excellent angle. “War”—when declared by major public figures—certainly catches the public attention.

    Kerry compared the urgency of climate change to the challenges facing America during World War II. “When America was attacked in World War II we set aside our differences, united and mobilized to face down our common enemy,” Kerry said in a statement. “We are launching World War Zero to bring that spirit of unity, common purpose, and urgency back to the world today to fight the great threat of our time.”

    Of course, actually waging war doesn’t always garner the unity or have the results we aim for, especially when it’s a war against a social issue.

  • Introducing Into the Dataverse, the article series

    Published Jan 23, 2020

    # README.txt: Introducing Into the Dataverse, the article series

    There is a significant gap in research about Canadian data collection activities on a granular scale. This lack of knowledge regarding data collection practices within Canada hinders the ability of policymakers, civil society organizations, and the private sector to respond appropriately to the challenges and harness unrealized benefits.

    So true. This looks like an interesting series from the great team at Brookfield.

  • How Tesla's first gigafactory is changing Reno, Nevada

    Published Jan 23, 2020

    “In the five years that weʼve had to asses the effect [the Gigafactory has] had on the workforce, on the community, I think there have been these ramifications that we talk about in the episode that nobody was really prepared for,” Damon said in an interview with The Verge. “Like, we knew there was going to be an issue with housing, which other cities are experiencing, too. But thatʼs become super critical.”

    Side-effects of growth are not a new problem, but the massive initiatives we’re seeing recently might spark new varieties of old issues.

  • Former Go champion beaten by DeepMind retires after declaring AI invincible

    Published Jan 23, 2020

    The South Korean Go champion Lee Se-dol has retired from professional play, telling Yonhap news agency that his decision was motivated by the ascendancy of AI. “With the debut of AI in Go games, Iʼve realized that Iʼm not at the top even if I become the number one through frantic efforts,” Lee told Yonhap. “Even if I become the number one, there is an entity that cannot be defeated.”

    Wow. Perhaps the first real example of “AI took my job?”

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productivity

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  • Why a review habit never seems to stick: hidden complexity in weekly reviews

    Published Mar 30, 2020

    A prominent—infamous, even—feature of many popular productivity systems is the review.

    The basic concept of a review is self-explanatory. You ask yourself questions like “what have I done?” and “what do I need to do?”, aided by lists of checked items or apps that serve up active and dormant projects.[^There can be more to it. See this episode of the Getting Things Done podcast for a more detailed discussion.]

    Reviews are infamous, however, because they are notoriously challenging to do continuously. There are even whole podcasts dedicated to the challenge.

    The review process is the keystone of most systems. It’s how we monitor, celebrate, and forgive the progress we make on the things we care about. It’s literally the most important feature in these systems for “staying organized.” So then why is it so difficult?

    Perhaps it’s because this seemingly-basic process is actually quite complex.

    Complexity is one of those topics that has an intuitive definition for most people. When something’s complex, it’s difficult! There’s a lot of steps or parts. It might be difficult to separate the components of a complex thing into separate pieces.

    That intuitive definition, however, doesn’t appear to explain why reviews are hard. At face value, there’s not a lot of separate pieces in a review—only “what’s completed?”, “what’s not?”, and “what’s next?”, across the various projects you might have.

    In practice, that intuitive definition of complexity is imprecise. We can learn more about complexity by comparing it to its siblings: complicated and simple.

    A simple problem doesn’t have many steps or components, and the solution to a simple problem is the same regardless of the environment. Tying your shoelaces is a simple problem. Once you’ve learned how, you can follow the steps and arrive at the same conclusion every time.

    A complicated problem might have many parts, but its solution is usually algorithmic. It might be more complicated to figure out a complicated problem, but once a solution is found, that solution can be applied again and again to get the same result. People like to say “this isn’t rocket science” to suggest that something’s not simple—and they’re right. Rocket science is complicated. Yet, once we have figured out how to launch a rocket, we can apply the same resources and processes to the same problem over and over again and get the same result.[^ Note that this doesn’t mean rocket science is easy. In fact, there are so many moving parts in rocket science that consistently solving its problems requires immensely powerful systems to make sure everything is done correctly and completely. “Murphy’s Law” is actually a parable of rocket science. Despite having the entire process of launching a test rocket completely mapped out and followed, a small mistake or malfunction still caused a test launch to fail, leading Edward A. Murphy, Jr. to suggest that if anything can be done wrong, somebody, somewhere will do it wrong. Murphy actually wanted his law to be the inverse: “if it can happen, it will.”]

    A complex problem may have many parts and steps, but in addition, the application of those steps depends entirely on the system within which they are implemented. Raising a child is a particularly illustrative example of complex problems. Clearly, it’s impossible to raise any two children the same way. The same rules and incentives will apply completely differently to two siblings, let alone to children in different households or cultures.

    So why are reviews complex? Well, no person ever reviews the same project twice, for it’s not the same project and they’re not the same person. We change, the world changes, and our responsibilities change. Arguably reviewing even has a quasi-quantum property: by observing our responsibilities, we change them. Ergo, even if you were to conduct a second review immediately after finishing a first one, the second review would yield different results.

    From my perspective, this complexity is hidden. Reviews seem like a simple—or complicated, at worst—thing. That’s because we (are supposed to) do them regularly, and the content of our reviews are the things we deal with on a daily basis. Surely we shouldn’t be challenged simply by the idea of looking at these things to make sure we’re not missing anything.

    Hidden complexity in a problem is itself a problem. Hidden complexity is a problem because we fail to use the right mindsets, tactics, and techniques to deal with the dynamics and uncertainty created by that complexity. Without the right approach, we exhaust our resources (in this case, our motivation and working memory) while failing to produce solutions. This means that we fail to either fully address our reviews or, worse, that reviewing becomes an impossible habit to stick to.

    So what? How does this help?

    One takeaway is to take advantage of the components of a review that are simple or complicated. For example, create a checklist what, exactly, you should do in a review. You could make this a template or you could create it at the outset, but either way, you shouldn’t engage in the process without without first explicitly defining its scope or path. Personally, I have a Shortcut that creates a new checklist in Trello for my review process. I just need to tap that, and then a boundary for the review is defined for me. Apps like OmniFocus can also help boil out complexity. OmniFocus encourages you to define review cycles for each area or project in your life, so that (for example) “Maintain the garden” doesn’t show up each week in the middle of Winter.

    Second, acknowledge the limitations of your working memory. A comprehensive review makes you face down every single challenge you’ve decided to take on. It’s overwhelming by definition. The whole reason you wrote all of those things down and put them away in a list or an app is because you can’t think about them all at the same time… yet here you are, trying to juggle them all in your head at once. You would think that’s enough. Sadly, no: you’re also trying to grapple with latent personal changes and shifts in the world around you that have taken root since you last looked at the items in front of you. As a result, you probably experience cognitive overload. This overload ruins your ability to deal with the information in front of you while draining your capacity to continue with the review.

    This means that you can’t actually do a review with only your lists of responsibilities and projects. Instead, to review effectively, you should also have your calendar(s) open, quick access to any potentially-relevant reference materials, and a freeform “review cache” (e.g., a blank page) where you can offload any of the questions or thoughts that come to mind as you look at the ideas in front of you. Ideally all of these things are visible to you at once. Switching back and forth between windows or pages is a sure way to overtax your working memory, as you’re trying to keep both concepts and the locations of information in your short-term memory.

    The purpose of the “review cache” is to offload your thoughts into a semi-permanent visible space. When you think of a question or idea that doesn’t have an immediate answer, destination, or action, mark it down. Feel free to list, mindmap, doodle, whatever—as long as there’s somewhere to turn whatever’s on your mind into temporary reference material. If you do this effectively (which can be difficult—we are often tempted to hold onto a thought for “just a second”), it should make the review process easier and more joyful.

    A third (but perhaps most important) lesson from this reflection is that the complexity of reviews are rarely acknowledged. It may be beneficial simply to realize that the review process is a potentially taxing one, and that you should be careful to go into it with lots of space and energy. For instance, I have always defaulted to trying to do a weekly review at the end of a day later in the week—by which point other responsibilities have had plenty of opportunity to get in the way and drain my stamina. By the time I get to my self-scheduled timeslot, the act of reviewing seems unimaginable.1 Based on these reflections, I schedule reviews at the outset of a day. By reviewing with a clear head and lots of energy, I’m actually able to get through it mindfully. In turn, the process itself is invigorating, I am encouraged by the feeling of control it gives me, and I look forward to it instead of dreading it.

    So, to sum up, there’s a reason why it’s so hard to stick to a regular review schedule. To better equip yourself to do so, (1) try to simplify the process as much as possible through tools like checklists. (2) While you’re doing the review, limit cognitive load by keeping everything you need visible and by caching your thoughts as you work through the review. Finally, (3) acknowledge the actual complexity inherent in the process of conducting a review. Give yourself appropriate time and space so that you can actually engage with the content successfully.

    Good luck!


    1. And of course, I beat myself up over this because I should be able to muster enough energy to do a single stupid review! Hurrah for vicious, self-defeating feedback loops. ↩︎

  • Paul Jarvis on Hurry Slowly: Small is Beautiful

    Published Jan 23, 2020

    The ever-refreshing Paul Jarvis shares some uncommon thoughts on productivity in Jocelyn K. Glei’s Hurry Slowly podcast.

    In particular, Paul and Jocelyn discuss the importance of resilience. Citing research and his own experience, Paul points out that resilience is a more important factor in success than many others.

    Obviously, though, enabling resilience is not as easy as simply pointing out how important it is. As they discuss, resilience isn’t something innate—which means that it can only be developed through experience. And this is where things get tricky: who gets to have resilience-building experiences?

    In my research on innovation skills, I discovered that resilience was one of three key domains that wasn’t an important outcome for our public education systems. This means that resilience training isn’t necessarily a public good. Only if you’re lucky (or privileged) will you have the chance to build up your resilience muscle.

  • Adam Savage on Lists, More Lists, and the Power of Checkboxes

    Published Jan 23, 2020

    # Adam Savage on Lists, More Lists, and the Power of Checkboxes

    In this Wired article, Adam Savage provides a pragmatic description of how he breaks down complex projects using lists.

    In my mind, a list is how I describe and understand the mass of a project, its overall size and the weight that it displaces in the world, but the checkbox can also describe the project’s momentum. And momentum is key to finishing anything.

    Momentum isn’t just physical, though. It’s mental, and for me it’s also emotional. I gain so much energy from staring at a bunch of colored-in checkboxes on the left side of a list, that I’ve been known to add things I’ve already done to a list, just to have more checkboxes that are dark than are empty. That sense of forward progress keeps me enthusiastically plugging away at rudimentary, monotonous tasks as well as huge projects that seem like they might never end.

    I love the physics metaphor here. There’s lots of other insights to be gained by thinking about how work follows physical principles. For instance, projects also have inertia, friction, and surface area:

    1. Inertia. The longer a project sits waiting for you—weighing on your mind—the harder it is to get it moving.
    2. Friction. Inertia is driven by initial friction. In parallel, of course, kinetic friction can make it hard to stop working on something. This is why multitasking doesn’t make sense with most projects.
    3. Surface area: It can be hard to attack a single, huge project idea, just like how a large ice cube melts slower than many little ones. List making is a key way of breaking up the surface of a project into smaller pieces, making it easier to handle. Increasing surface area also facilitates collaboration: it’s easier to hand off smaller pieces to others, and to put them back together again.

    To return to momentum, though, Adam makes an excellent point: breaking down the work helps keep momentum going even when you put the work down.

    That may be the greatest attribute of checkboxes and list making, in fact, because there are going to be easy projects and hard projects. With every project, there are going to be easy days and hard days. Every day, there are going to be problems that seem to solve themselves and problems that kick your ass down the stairs and take your lunch money. Progressing as a maker means always pushing yourself through those momentum-killers. A well-made list can be the wedge you need to get the ball rolling, and checkboxes are the footholds that give you the traction you need to keep pushing that ball, and to build momentum toward the finish.

    Another point in the article that’s worth emphasizing:

    [I]n a project with any amount of complexity, the early stages won’t look at all like the later stages, and [the manager] wanted to take the pressure off any members of the group who may have thought that quality was the goal in the early stages.

    I’ve heard this discussed in the context of critique, or “10% feedback”. When sharing work with others, it’s important to disclose the stage the work is at. Typos should be caught at a project that’s basically ready to publish. They shouldn’t even be discussed when a work is being conceptualized. The focus on early stages should be the concepts themselves, and how they fit within the broader context.

    Last thing. This is excellent:

    There is a famous Haitian proverb about overcoming obstacles: Beyond mountains, more mountains.

    🏔

  • Procrastination Station

    Published Aug 19, 2018

    # Procrastination Station

    Procrastination is itself a systems trap. That balancing loop sucks you in. You take on sneaky little actions as dopaminergic relief to the burden you’re escaping from, while the burden only grows heavier. (A form of Fixes that Fail.)

  • New year, new you

    Published Jan 3, 2017

    New Year’s Resolutions systems traps

    Could New Year’s resolutions be doomed to fail? Driven by New Year’s culture, we commit to actions, call them resolutions, and set off to become better people. This phenomena of setting New Year’s resolutions is reinforcing (R1).

    Yet, as we get further away from the culture of resolutions, our commitment – linked at heart to New Year’s culture – fades. This, coupled with how slow progress can be, leads to the gradual extinction of our promise to ourselves. (B1)

    Perhaps, then, resolutions made outside of and separate from the cultural phenomena are more likely to result in real changes, as their founding is based in intrinsic motivation as opposed to the extrinsic incentives of New Year’s culture.

    (The time since New Year’s sets a limit to growth on the new habit.)

innovation

8 notes with this tag

  • Intuition is confident abduction

    Last updated Nov 7, 2020 | Originally published Nov 7, 2020

    # Intuition is confident abductive-inferential thinking

    In a recent episode of Hello Monday, Jessi Hempel interviews Dr. Natalie Nixon on creativity and her new book, The Creativity Leap. Natalie’s PhD in Design Management—plus her work in fashion, design, and business—led her to a catchy and compelling description of creative work. We accomplish creative work, she says, “by toggling between wonder and rigour.”

    In the podcast conversation, Jessi and Natalie talk about intuition—and I was struck by something. “We don’t talk about intuition,” Natalie notes at about 6 minutes in. “We don’t talk about intuition in business school, in law school, or in medical school.” And yet, she says, “I observed that really successful leaders—especially really successful startup leaders—in their origin stories, there’s always this moment where ‘Something told me not to do the deal. Something told me to work with her over him.’ […] Every successful leader really reckons with incorporating acting on their intuition to make decisions.” Jessi agrees, noting that intuition comes up often in her interviews with leaders on Hello Monday as leaders cite it as the reason for their success.

    The thing is, just because we don’t name intuition doesn’t mean we aren’t talking about it. That’s because intuition is really just confident, logical thinking.

    Charles Sanders Peirce was a philosopher. He investigated how we inquire into and discover new knowledge.1 Before Peirce, we generally recognized the logical processes of deduction and induction. Deductive thinking helps us identify what must be true about a situation in order to explain it. When we deduce something, we look at the general rules and principles we know of and draw specific conclusions from that evidence. Inductive thinking involves drawing general conclusions from specific, limited evidence.

    Peirce argued that effective reasoning follows a pattern: we determine the specific consequences of an idea (deduction), and then we judge whether the available evidence fits that idea and its consequences (induction). But how do we develop ideas?2

    Abduction is the name of the logical process Peirce described for developing ideas. To think abductively means to generate and choose ideas that fit the situation at hand. A good idea should be verifiable—we should be able to use evidence to judge its fit—and should help us resolve the situation at hand. Peirce also had criteria to help choose the best ideas to test. He suggested that we should strive to conserve resources (e.g., those that most are most efficiently verifiable and usable in the situation), identify the most valuable ideas (specifically the “uberty” of an idea, or how likely it is that a possible idea might bring about an innovation), and the most relevant ideas (e.g., those that may apply beyond our current focus, too).3

    Abduction is clearly an important step in any innovative process—but it is no more important than testing and using the ideas you generate. What, then, if you don’t have enough evidence to truly test and prove your ideas?

    The process Peirce described—abduction, deduction, induction—is the ideal. However, we do not always have time and energy to follow the process diligently. Instead, we quickly make creative judgements based on a few observed qualities. This requires two related processes.4 The first Peirce called “abductory induction,” and it combines the first and last step of the inquiry process. We observe the qualities of the situation, and we generate possible ideas to resolve it based on those observations. The second process is known as “inference to the best explanation” (IBE).5 IBE is exactly what it sounds like. Given a number of possible ways of resolving a problem, choose the best one. (Peirce’s criteria, noted above, apply here.)

    So what does all this have to do with intuition?

    Intuition is the confident application of these shorthand logical approaches to creative problem solving. As Jessi and Natalie noted, we aren’t often explicitly taught about strengthening our intuition. Yet, everything we learn supports its development. The more we have to draw on in order to pull into the processes described above, the better our intuitive decisions will be.

    I say that intuition is the confident application of these processes because they only work when we follow through. In reality, we use abductory induction and IBE all the time. When we engage in creative problem solving, we’re not only using information from the evidence in front of us. We’re drawing on our lived experience and our knowledge base. Even if we don’t directly recall or reference that background information, it is drawn into the creativity of abduction and it defines the general rules and principles we use in deduction. It provides us with the heuristics we use when engaging in IBE. But if we don’t have a bias towards action and instead operate with e.g., perfectionism, we fail to actually execute on these ideas. Thus, we need to have confidence in our abductory induction and IBE processes.

    All this is simply a gentle challenge of the idea that we don’t talk about intuition. I think that all knowledge management practices and forms of education are actually fundamentally about strengthening our intuition.

    That said, Natalie’s work is fascinating. I recommend the episode of Hello Monday and plan on picking up her book!


    1. In this article, my reading of Peirce comes from the writing of William Mcauliffe↩︎

    2. Peirce was actually specifically concerned with science and hypotheses generation, selection, and testing. Here I refer to generating, selecting, testing, and using ideas to apply these concepts to problem-solving more broadly. ↩︎

    3. He also cautioned not to produce ideas that stop the inquiry process—e.g., magical thinking, or by suggesting that whatever happened must be a complete mystery. ↩︎

    4. Actually, the difference between these two processes is the subject of substantive, controversial debate. This is in part because the scholars who study inference to the best explanation have also used Peirce’s term “abduction” to describe it. This understandably caused extensive confusion, but also probably a lot of philosophical debates and scholarship, so maybe it was for the best. ↩︎

    5. Philosopher Gilbert Harman originally described and named this process… and mistakenly suggested it was the same thing as abduction. ↩︎

  • The changing work of innovation for public value and social impact

    Published Jan 23, 2020

    In two senses, the work of innovation for public value and social impact is changing in Australia and around the world. What we expect public innovation to do and what we need it to achieve, and how that work should be done, are both changing. And they are changing together while they are changing each other.

    It’s true. It’s hard to keep up with the discipline of changemaking, but it’s even harder to keep up with the change that needs to be made. Therefore Martin Stewart-Weeks calls for optimism:

    Despite some of the uncomfortable and unsettled conditions, there is real energy in the search for more effective ways to solve the big problems we face in common – managing our complex cities, rewiring large and complex health and social care systems, tackling climate change, searching for better ways to integrate the human and technology capabilities of the digital age and making our communities healthy and resilient.

    The speed, intensity and sheer connectedness of these and many other complex, public challenges are giving rise to new methods and tools that can help to tackle them with purpose and skill.

  • Piret Tõnurist & Systems Change: how to get started and keep going?

    Published Jan 23, 2020

    This is a great talk from Piret Tönurist of the Observatory on Public Sector Innovation.

    One of the core issues of the talk is innovation doubt—the “if it ain’t broke, don’t fix it” mentality. To paraphrase Piret:

    […] why are we doing innovation at all? Maybe sometimes things are working fine, why do we think about innovation at all? We start off with four questions:

    1. Do you want to do things better?
    2. Do you have goals and purposes to fulfill?
    3. Do you want to address the needs of your stakeholders?
    4. Do you want to prepare for the risks and uncertainties that the future holds? If you answered “yes” to at least one of those questions, then your job is to do innovation—your job is to be a changemaker.

    Also, the talk includes a neat model for different varieties of innovation, image courtesy of this post by Adrian M. Senn over on Medium: https://miro.medium.com/max/1210/1*AaPZeqAVLoo85RfY7Dxspw.png

    I came across this talk via a related panel discussion.

  • Paul Jarvis on Hurry Slowly: Small is Beautiful

    Published Jan 23, 2020

    The ever-refreshing Paul Jarvis shares some uncommon thoughts on productivity in Jocelyn K. Glei’s Hurry Slowly podcast.

    In particular, Paul and Jocelyn discuss the importance of resilience. Citing research and his own experience, Paul points out that resilience is a more important factor in success than many others.

    Obviously, though, enabling resilience is not as easy as simply pointing out how important it is. As they discuss, resilience isn’t something innate—which means that it can only be developed through experience. And this is where things get tricky: who gets to have resilience-building experiences?

    In my research on innovation skills, I discovered that resilience was one of three key domains that wasn’t an important outcome for our public education systems. This means that resilience training isn’t necessarily a public good. Only if you’re lucky (or privileged) will you have the chance to build up your resilience muscle.

  • IBM expert Tamreem El Tohamy on bridging the skills gap in Africa

    Published Jan 23, 2020

    In the next three years, as many as 120 million workers in the world’s 12 largest economies may need to be retrained or reskilled as a result of Artificial Intelligence (AI) and intelligent automation.

    cf. Lee Se-Dol.

    This is according to the latest IBM Institute for Business Value (IBV) study, titled The Enterprise Guide to Closing the Skills Gap.

    Seems like an interesting guide. This metric surprised me:

    In 2014, it took three days on average to close a capability gap through training in the enterprise. In 2018, it took 36 days.

    I didn’t know this measure existed, but I can see the utility. As knowledge work grows ever more specialized, this time-to-capability can only grow.

  • Innovation is a buzzword

    Published May 8, 2017

    Innovation is a Buzzword (but it doesn’t have to be)

    Notes, slides, and the Innovation Auditing guide presented at the talk are found below.

    # The research

    The research presented during the talk is discussed on the following pages:

    An innovation pop quiz

    # Slides

    Find a PDF of the slides I presented at the link below. Beware: the animations don’t translate well to print, so some of the pages have graphical issues.

    Download the slides

    # Innovation Auditing

    Innovation Auditing is a simple procedure that individuals, organizations, and governments can use to detect the gaps in the innovation process they seek to support.

    See the PDF guide by clicking the link below.

    Download the guide to innovation auditing

  • Innovation Education

    Published Jan 13, 2017

    # Innovation Education

    What is innovation? How do we define innovation, its outputs and processes, and what are the skills and competencies necessary to practice and excel in innovation?

    Read the full paper.


    Many strategies and policies, both federal and provincial, have attempted to improve Canada’s innovation capacity in the last few decades. Universities and colleges are often discussed in these strategies for their role in facilitating new partnerships and in developing (potentially) actionable research.

    It is rare, however, for these strategies to recognize education plays in the creation of innovators.

    Abstract shapes for decoration.

    This is counterintuitive: education is an obvious mechanism with which to develop the knowledge and abilities of a population. Yet we lack a holistic understanding of what it takes to practice innovation, let alone the kinds of curricula that might provide those skills and competencies. Moreover, we are inconsistent in the definitions and language we use to define innovation—often obsessing over technology and commercialization. We tend to assume innovation comes from research and development processes, and that innovators are simply highly skilled people.

    Presented here is the result of an intensive review of reports, strategies, policy, and theory on innovation in the Canadian context. This literature was scanned and coded—inspired by the ethnographic methods of grounded field theory—in order to synthesize a holistic theory of innovation.

    media/_innovation-domains-constructs-and-outcomes.jpeg

    13 learning domains, 47 learning constructs, and 227 learning outcomes comprise a holistic model of innovation education. Use the interactive map below to explore the model yourself!

    This theory includes a universal definition of the innovation process, a recasting of different focuses of innovation as innovation orientations, a comprehensive model of the innovation process, and a synthesis of innovation skills and competencies into 13 learning domains, 47 learning constructs, and 227 learning outcomes. These tools provide utility for policymakers and educators in pursuit of understanding and improving innovation capacity. In particular, the model of innovation education is the most comprehensive of its kind, providing an extensive set of concepts with which to understand education gaps and build curricula.

    Perhaps the most important contribution of this research, however, is the recognition that our conversations about innovation strategies and education reform must be aligned. **How exactly do people learn to be innovative, and how are our education systems currently facilitating that process? **With this study we have begun to seek answers to these questions, but there is much more work to do.

    If you use these ideas and learn something from your experience, or if you have thoughts on how to improve them, don’t hesitate to make suggestions and to share your work.

    Find resources on this page that detail the definitions and models of innovation developed through this research and how these concepts may be applied in innovation education programming.

    # Explore the Model

    Across dozens of perspectives on innovation, a set of 13 domains of skills and competencies emerged. These domains have been interactively visualized using Kumu.io. You can play with the work yourself by clicking the button. Use the controls in the bottom corners to focus on specific components of the model, to surface the innovation process so that you can explore both simultaneously, and more. Note that the visualization is best explored on a desktop!

    PLAY WITH THE VISUALIZATION

    # Learn more about the skills and competencies of innovation

    This document provides an executive summary of the insights and models on innovation education uncovered through this research and guides educators to put these concepts and tools to use.

    media/_curricula-guide.jpeg

    Get the Curricula Guide

    # Read the Research

    Read the full paper.

    Education reform presents an opportunity to improve innovation education and, in turn, advance innovation capacity. I synthesize the framing and strategy of resources from provincial, national, international, and theoretical perspectives on innovation in order to develop a holistic model of innovation and a curricula for innovation education. Then, I use systemic design to model Newfoundland and Labrador’s current education system and to suggest strategies for reform to enable improvement in Newfoundland and Labrador’s innovation education. Finally, I explore how systemic reform in Newfoundland and Labrador may serve as a systems laboratory for reform efforts in other jurisdictions.


    Innovation Education is a major research project presented to OCAD University in partial fulfillment of the requirements for the degree of Master of Design in Strategic Foresight & Innovation. This work was supported by the Social Sciences and Humanities Research Council of Canada.

  • Innovation systemics

    Published Aug 10, 2016

    # Innovation Systemics

    # Takeaways

    # Problem 1: Canada Struggles To Understand How It Can Fit The ‘Idea Economy’ Into The Broader Economic Context (I.e. Other Sectors).

    Thus, Canada should take steps to foster the idea economy. The world is changing, and Canada needs to understand how the tenets of the idea and knowledge economy will best flourish across the primary sectors that drive the broader Canadian economy. Building regional collectives (like the Waterloo-Kitchener corridor) to advance innovation is only one step, whereas our findings suggest the need to take a collective leap and author a White Paper that will activate a national approach on the new, ideas-based economy. In doing so, the Government of Canada will engage a collective of stakeholders, thought leaders and businesses both large and small to lead a national dialogue on how innovation ought to play out across Canada’s diverse communities, companies and campuses.

    # Problem 2: Canadians Do Not Recognize The Unique Strengths Of The Economy As It Is, And Neglect Recognition Of The Innovations Already Taking Place. 

    Canada should change the narrative. Innovation is happening, but it is taking place in sectors and industries different than the ones we tend to pay attention to. Increasing awareness about the need for, and impact of, innovation within advanced manufacturing, natural resources and agriculture will help cross pollinate the economy with more robust, productive approaches and solutions. This will also help to shift the culture of entrepreneurship by empowering a generation of Canadians to create sustainable value and ensure they and their communities prosper.

    # Problem 3: Canada Currently Allocates A Disproportionate Amount Of Resources (Talent And Capital) To “Tech Solutionism”, At The Cost Of Others.

    Canada should redirect the pipeline. The ultimate step is to increase the carrying capacity of the system to generate value for entrepreneurs from outside the technology sector. Drawing sectors and industries together with the innovation ecosystem will build deeper and more diverse connections. Diverting the flow of resources—talent and capital—and binding it with existing infrastructure and institutional support will build a depth that will support a more robust Canadian economy. Furthermore, investing in improving the structures that nurture prosperity—incubators, accelerators, entrepreneurship, and innovation programs—and bringing them together with campuses and companies already making an impact, will propel Canada forward.  

    # The Context

    “Innovation drives an economy’s ability to create more economic value from an hour of work, thereby increasing economic output per capita. The resulting productivity growth creates potential for rising wages and incomes, and thus for a higher standard of living.” (University of Lethbridge Research Services, 2015)

    A robust innovation ecosystem has the ability to improve productivity, economic growth, and job creation metrics in countries adequately supporting this process. In these countries, there also tend to be more resources available to support spending in education, health, and infrastructure, to name a few (“Innovation details and analysis”, 2013). Accordingly, the importance of a healthy innovation system is tied closely to that of a healthy national economy, along with its people, communities and institutions. 

    In Canada, however, economic discourse around innovation and entrepreneurship has recently pointed towards a decline in productive returns from startup investment. Although an improvement from previous years, in 2015 Canada was ranked 9th out of 16 peer countries in innovation by the Conference Board of Canada, receiving a letter grade of “C” on its Innovation Report Card (“Innovation details and analysis”, 2013). This ranking points to a persistent weakness in the Canadian innovation system, commonly referred to as the “innovation gap”. This phenomenon is being reported by some of the country’s top journalists, startup CEOs, established investors, think-tanks, and members of various levels of government across the country. While these reports highlight culture, politics, economics, and education as the cause of this gap, and underlining the need for reform, policy changes, and new programs, few of these calls to action are gaining traction. There is no easy answer to this wicked problem.

    With the above in mind, we initially sought to answer the question “Why are investments in innovation resulting in diminishing returns in productivity?” through our research, but we quickly realized that in order to address this issue, we would first need develop a thorough understanding of the innovation ecosystem in Canada and thus we broadened our research questions to “How might we understand the Canadian innovation system?” all the while still focusing on this oft reported, elusive innovation gap. 

    Innovation details and analysis. (2013). The Conference Board of Canada. Retrieved from http://www.conferenceboard.ca/hcp/details/innovation.aspx

    University of Lethbridge Research Services. (2015). Putting Innovation in Context. Lethbridge, Alberta, Canada: University of Lethbridge. Retrieved from http://www.agility-ulethbridge. ca/2015/09/11/putting-innovation-in-context/ 

    # Research Synthesis Map

    A synthesis map of the research.

    This synthesis map summarizes our research on Canada’s systemic innovation challenges this past term. 

    # Leverage Analysis

    We used centrality analysis to examine our map of the system for potential leverage points. The elements that surface from this analysis offer opportunities (or, in some cases, roadblocks) for policy and program interventions.

    # The Paper

    Read the paper.

    # The Researchers

    This work was completed by a team of Master of Design students in OCAD U’s Strategic Foresight & Innovation program.

    Michael Berman

    Robyn McCallum

    David Fascinato

    Ryan J. A. Murphy

Research

8 notes with this tag

Systemic Design

8 notes with this tag

Automation

6 notes with this tag

education

6 notes with this tag

  • As Lambda students speak out the schools debt-swapping partnership disappears from the internet

    Published Feb 19, 2020

    “The ISA is the business model, not education,” says Kim Crayton, a business strategist and founder of CauseAScene , an organization that’s seeking to disrupt the status quo in tech. “You cannot tell me that education is your business model when you have not registered as an institution.” For months, Crayton has been speaking about the problems with coding bootcamps on her podcast, where she’s argued that they target vulnerable communities. “You’re put in these spaces and putting in 110 percent and it’s still not working and you’re told to ‘trust the process,’” she says.

    Great reporting on this at The Verge.

    Kim Crayton makes an excellent point. The promise of many of these neo-credentials is for students to leapfrog the things everyone fears about the conventional education system. No one is more vulnerable to taking on loads of student debt than those who need it most. Those students are also going to suffer the most if their university or college fails to equip them for a career. Lambda solves both of these problems, making it extremely attractive to poor students.

    Sadly, there’s always a catch.

  • IBM expert Tamreem El Tohamy on bridging the skills gap in Africa

    Published Jan 23, 2020

    In the next three years, as many as 120 million workers in the world’s 12 largest economies may need to be retrained or reskilled as a result of Artificial Intelligence (AI) and intelligent automation.

    cf. Lee Se-Dol.

    This is according to the latest IBM Institute for Business Value (IBV) study, titled The Enterprise Guide to Closing the Skills Gap.

    Seems like an interesting guide. This metric surprised me:

    In 2014, it took three days on average to close a capability gap through training in the enterprise. In 2018, it took 36 days.

    I didn’t know this measure existed, but I can see the utility. As knowledge work grows ever more specialized, this time-to-capability can only grow.

  • Resources

    Last updated Nov 14, 2019 | Originally published Mar 15, 2017

    # Resources

    Updated Nov 14, 2019

    Below I’m collecting a set of resources dealing with a variety of topics for shareable reference—a sort of living, semi-public annotated bibliography. Many of these readings should be accessible via a library or a good Internet search, but if you find something here that shouldn’t be this easy to download, feel free to contact me and I’ll remove it. Or, if you come across any broken links, please let me know!

    # Getting Started with Systemic Innovation

    Riddell & Moore’s Scaling Out, Scaling Up, Scaling Deep (2015)

    Kudoz’ Where Are You Scaling? Placemat (2017) KudozWhere Are You Scaling? “placemat” is an explorable tool to help think through what scaling really means in a given venture. It builds upon Riddell & Moore’s (2015) Scaling Out, Scaling Up, and Scaling Deep in expanding our notion of scaling. When you hear the word “scale” in a conventional conversation, the meaning is likely synonymous with scaling out: reaching more people (stakeholders or customers) with the product, service, program, or policy at the heart of the conversation. Yet there are other underappreciated aspects of scaling: some are required in order to successfully scale out, while others are different ways of achieving impact—maybe scaling out isn’t the best route to take after all! Scaling up, for instance, refers to reaching higher in power hierarchies in order to implement higher-level solutions to the problem you’re addressing. Scaling deep means to reach the same people you’re already reaching more deeply, or with greater impact. Kudoz introduces two more concepts: scaling infrastructure, or building out more tools and supports to make the work more effective and sustainable; and scaling scree, or lending support and legitimacy to other, different actors and initiatives addressing the same issues you are.

    Mulgan & Leadbeater’s Systemic Innovation (2013) While the above concepts help make concrete the different ways an intervention might change a system, Mulgan and Leadbeater’s (2013) discussion paper on systemic innovation dives a bit deeper into theory. They link the practice of systems thinking to approaches to innovation, providing a framework for “joining up” a set of innovations in order to achieve systemic change.

    # Getting Started with Social Innovation

    Mulgan’s Social Innovation: What It Is, Why It Matters, And How It Can Be Accelerated (2007) Geoff Mulgan offers a comprehensive yet down-to-Earth guide to social innovation. The reading really helps communicate what the differences are between social innovation and conventional innovation with practical examples.

    Phills Et Al.‘S Rediscovering Social Innovation (2008) The Stanford Social Innovation Review has been one of the preeminent publishers on social innovation since its inception in the early 2000’s. In this review Phills et al. summarize a brief history on social innovation itself while paving a way forward in research and practice.

    # Getting Started with Design

    What is “design”, really? It’s easy to think that design is the layout of graphics or illustrations—perhaps even making decisions about layouts, as in architecture. These disciplines—graphic design and architecture—certainly leverage design, and many people employed in these industries are designers. Yet these disciplines do not fully capture what design is, thus we should not limit the understanding and application of design to these industries.

    Kolko’s Sensemaking And Framing (2010)

    The term design shares latin roots with designation. To design is therefore to mark; to frame. Framing means to view a given problem or system from different perspectives in order to deeply understand it and, in turn, to develop novel ways of doing something about it or with it. To become a better designer, you must get better at both analyzing and creating frames.

    # Getting Started with Systems Thinking

    Systems thinking is most useful when applied to complex, chronic issues: “wicked problems” that resist taming through conventional approaches. David Stroh, who wrote  Systems Thinking for Social Change, argues that effective systems initiatives begin with a focusing question: a question that bounds the project (so that you don’t spend forever debating about where the problem system really ends) and anchors your systems-understanding work in a framing that you can return to if you get lost. This is a question of the form “Why, despite our best efforts and intentions, does [x] persist?” or “…have we not reached our goal of [y]?”—where [x] and [y] are phenomena that remain unassailable through conventional initiatives.

    Goodman, Kemeny & Roberts’ The Language Of Systems Thinking (2000) The Language of Systems Thinking introduces the reader to reinforcing and balancing loops using simple examples.

    Braun’s The Systems Archetypes (2002) Systems archetypes extend the links and loops of basic systems thinking into more complicated structures – and in doing so, begin to share with the reader deeper insights into the counterintuitive behaviour of the systems we work with.

    Donella Meadows’ Leverage Points: Places To Intervene In A System (1997) With robust systemic models, we can begin to identify leverage points: key phenomena that have great potential for change throughout the system. 

    # Getting Started with Futures Thinking

    Jim Dator’s What Futures Studies Is, And Is Not (1995) What Futures Studies Is, and Is Not is a wonderful introductory and reader-friendly summary of futures thinking.

    Choo’s The Art Of Scanning The Environment (1999) Chun Wei Choo’s The Art of Scanning the Environment offers a walkthrough of scanning practices.

    Bishop, Hines & Collins’ The Current State Of Scenario Development (2007) The Current State of Scenario Development provides a comprehensive review of scenario development approaches and techniques. 

    # Deeper Reading: Organizational Change

    Dubberly Et Al.‘S Notes On The Role Of Leadership & Language In Regenerating Organizations (2002) Hugh Dubberly and a suite of colleagues provide an elegant—poetic, even—walkthrough of how change happens to organizations through their language. They reframe an organization as a continuous set of conversations between people, in turn showing the power of changing the language those people use in order to change the nature of the organization itself.

    Weick & Westley’s Organizational Learning: Affirming An Oxymoron (1999) Karl Weick and Frances Westley attack the very idea of organizational learning by showing how “organizing” and “learning” are, in fact, opposing processes. To organize is to order; to learn is to disrupt. Success in organizational change is therefore rooted in a leader’s ability to handle the tension between organizing and learning. Too much organizing and the organization will miss opportunity in change—but too much learning and the organization might not be stable enough to survive.

    Gawde’s Learning From Systems Thinking (2017) How can systems thinking help with changemaking in an organization? Writing for People Matters, Sanjay Gawde provides a succinct argument and explanation linking systems approaches to organizational change.

  • Innovation is a buzzword

    Published May 8, 2017

    Innovation is a Buzzword (but it doesn’t have to be)

    Notes, slides, and the Innovation Auditing guide presented at the talk are found below.

    # The research

    The research presented during the talk is discussed on the following pages:

    An innovation pop quiz

    # Slides

    Find a PDF of the slides I presented at the link below. Beware: the animations don’t translate well to print, so some of the pages have graphical issues.

    Download the slides

    # Innovation Auditing

    Innovation Auditing is a simple procedure that individuals, organizations, and governments can use to detect the gaps in the innovation process they seek to support.

    See the PDF guide by clicking the link below.

    Download the guide to innovation auditing

  • Innovation Education

    Published Jan 13, 2017

    # Innovation Education

    What is innovation? How do we define innovation, its outputs and processes, and what are the skills and competencies necessary to practice and excel in innovation?

    Read the full paper.


    Many strategies and policies, both federal and provincial, have attempted to improve Canada’s innovation capacity in the last few decades. Universities and colleges are often discussed in these strategies for their role in facilitating new partnerships and in developing (potentially) actionable research.

    It is rare, however, for these strategies to recognize education plays in the creation of innovators.

    Abstract shapes for decoration.

    This is counterintuitive: education is an obvious mechanism with which to develop the knowledge and abilities of a population. Yet we lack a holistic understanding of what it takes to practice innovation, let alone the kinds of curricula that might provide those skills and competencies. Moreover, we are inconsistent in the definitions and language we use to define innovation—often obsessing over technology and commercialization. We tend to assume innovation comes from research and development processes, and that innovators are simply highly skilled people.

    Presented here is the result of an intensive review of reports, strategies, policy, and theory on innovation in the Canadian context. This literature was scanned and coded—inspired by the ethnographic methods of grounded field theory—in order to synthesize a holistic theory of innovation.

    media/_innovation-domains-constructs-and-outcomes.jpeg

    13 learning domains, 47 learning constructs, and 227 learning outcomes comprise a holistic model of innovation education. Use the interactive map below to explore the model yourself!

    This theory includes a universal definition of the innovation process, a recasting of different focuses of innovation as innovation orientations, a comprehensive model of the innovation process, and a synthesis of innovation skills and competencies into 13 learning domains, 47 learning constructs, and 227 learning outcomes. These tools provide utility for policymakers and educators in pursuit of understanding and improving innovation capacity. In particular, the model of innovation education is the most comprehensive of its kind, providing an extensive set of concepts with which to understand education gaps and build curricula.

    Perhaps the most important contribution of this research, however, is the recognition that our conversations about innovation strategies and education reform must be aligned. **How exactly do people learn to be innovative, and how are our education systems currently facilitating that process? **With this study we have begun to seek answers to these questions, but there is much more work to do.

    If you use these ideas and learn something from your experience, or if you have thoughts on how to improve them, don’t hesitate to make suggestions and to share your work.

    Find resources on this page that detail the definitions and models of innovation developed through this research and how these concepts may be applied in innovation education programming.

    # Explore the Model

    Across dozens of perspectives on innovation, a set of 13 domains of skills and competencies emerged. These domains have been interactively visualized using Kumu.io. You can play with the work yourself by clicking the button. Use the controls in the bottom corners to focus on specific components of the model, to surface the innovation process so that you can explore both simultaneously, and more. Note that the visualization is best explored on a desktop!

    PLAY WITH THE VISUALIZATION

    # Learn more about the skills and competencies of innovation

    This document provides an executive summary of the insights and models on innovation education uncovered through this research and guides educators to put these concepts and tools to use.

    media/_curricula-guide.jpeg

    Get the Curricula Guide

    # Read the Research

    Read the full paper.

    Education reform presents an opportunity to improve innovation education and, in turn, advance innovation capacity. I synthesize the framing and strategy of resources from provincial, national, international, and theoretical perspectives on innovation in order to develop a holistic model of innovation and a curricula for innovation education. Then, I use systemic design to model Newfoundland and Labrador’s current education system and to suggest strategies for reform to enable improvement in Newfoundland and Labrador’s innovation education. Finally, I explore how systemic reform in Newfoundland and Labrador may serve as a systems laboratory for reform efforts in other jurisdictions.


    Innovation Education is a major research project presented to OCAD University in partial fulfillment of the requirements for the degree of Master of Design in Strategic Foresight & Innovation. This work was supported by the Social Sciences and Humanities Research Council of Canada.

  • Education Systemics

    Published Jan 13, 2017

    # Education Systemics

    What are the mechanisms of education systems change? How might we use systemic design to power a reform movement? What does systems thinking teach us about opportunities and barriers for education reform in Newfoundland and Labrador?

    Read the full paper.


    Education reform is not a new idea. Many organizations and initiatives have reimagined the schools, universities, and colleges that are the backbone of our economy and society—yet, to many, it seems like little has changed in recent decades.

    As Jal Mehta, Robert Schwartz, and Frederick Hess began their book on the subject, “if we keep doing what we’re doing, we’re never going to get there.” Traditional education reform approaches have depended on a best practices approach in what is glibly called a “silver bullet culture”.

    A single idea, found successful in a specific institution or district, becomes hailed as the be-all-end-all solution. This solution is then celebrated and championed across contexts until actors realize that expected results have not materialized, and reformers move on to the next silver bullet solution.

    This approach to education reform has not worked. According to Mehta, Schwartz, and Hess: “If we are to deliver transformative improvement, it is not enough to wedge new practices into familiar schools and districts; we must reimagine the system itself”. In other words, education systems change has been found to be more than difficult. It is a wicked problem: ill-defined, constantly fluxing, with many conflicting stakeholders and no true solutions.

    How can we provoke change in wicked problems?

    I argue that reforming education is a sociotechnical problem, involving human psychology; social, political, and economic factors; and complex interactivity–what Don Norman and Pieter Stappers have called a “DesignX” problem. These authors suggested that DesignX problems can only be solved through a process of muddling through, developing incremental sub-solutions through deep analysis. This deep analysis means partitioning the problem into modules and recognizing the intersecting dimensions of the problem.

    Peter Jones provides further advice for this kind of problem solving, defining an approach called systemic design. Systemic design integrates systems thinking and systemic methods–ways of understanding complex problems through the relationships of the phenomena and actors involved–with design thinking and design methods, applying human-centred design to these seemingly intractable, large-scale problems. With systemic design, Peter says, we use “known design [tools]—form and process reasoning, social and generative research methods, and sketching and visualization practices—to describe, map, propose, and reconfigure complex services and systems”.

    # Approach & Findings

    With this in mind, I turned to several types of modelling in order to use systemic design on the complex problem of education reform: process modelling, actor mapping, and causal loop mapping. In this modelling, I focused on Newfoundland and Labrador—my home province—and the education of innovation, examining how our system currently provides innovation learning and how we might do better.

    Finally, I applied centrality analysis – a quantitative approach to assessing leverage points and bottlenecks in networks and systems maps—in order to surface potential opportunities and challenges in the system. I examined four types of centrality analytics:

    • Reach efficiency takes an element’s reach (the proportion of the network within two steps of that element) and divides it by the number of neighbouring elements it has. Elements that score the most on this metric tend to be less connected but have high exposure to the rest of the system, making them low-hanging fruit for change efforts.

    • Betweenness assesses the number of times an element lies on the shortest path between two other elements. Elements high on the betweenness metric are bridges throughout the map, controlling the flow of phenomena throughout the system. This means that these elements may be bottlenecks or single points of failure.

    • Eigenvector centrality measures an element’s connectedness to other well-connected elements, computing an overall value that is an indicator of the element’s influence over the whole system.

    • Torque calculates each element’s reach efficiency weighted by its eigenvector centrality (e.g., how influential that element is). Elements with high torque should be relatively easy to impact (as they are not densely influenced by other phenomena in the map), but will impact the rest of the map substantially. These are key leverage points of change.

    The detailed methods and findings from each of these approaches are discussed in turn in the links below.

    # Process Modelling, Actor Mapping, And Causal Loop Mapping

    An abstract illustration of process modelling Process Modelling: Where might innovation learning come from?

    An abstract illustration of actor modelling Actor Mapping: who is involved in this system?

    An abstract illustration of causal loop mapping Causal Loop Mapping: how does the system behave?

    # Conclusions

    I aimed to see the education system for what it is in order to describe strategies for the transformative reform that Mehta, Schwartz, and Hess called for. The education system is therefore composed of a number of interrelated components, organized in a hierarchy, whose emergent phenomena lead to its own dynamics. Yet, many might say that this systemic chaos implies a system of constant change, while education is hallmarked for its derelict stagnancy in the 21st century. How is it that such a system has not evolved?

    Well, perhaps the system is not actually that broken. As eloquently argued by Ryan Burwell, an instructional designer at the MaRS Discovery District:

    The school system is not broken. It is perfectly aligned to provide equitable access to a canon of high-quality, standardized content with greater rigour and organization than any other knowledge delivery system we currently have. However, it is not designed to foster the problem-solvers, innovators and entrepreneurs that are becoming an increasingly significant part of the global economy. Incorrectly identifying this misalignment as a broken system has created a culture of fear and failure around education, leading to top-down reforms and increased numbers of mandatory programs.

    I return to Mehta, Schwartz, and Hess’ depiction of school reform’s silver bullet culture. Many stakeholders with competing interests and different priorities are invested in every debate on education systems change.

    Thus, there are many potential silver bullets–and many advocates for them. The misunderstanding of the problem described by Burwell and the complexities of education reform described by Mehta, Schwartz, and Hess perfectly capture the need for a systemic design-based approach to change.

    # Process Mapping

    From process modelling, it is clear that while NL’s education system currently offers some opportunities to learn certain constructs of innovation, the availability of these opportunities is not densely packed throughout their study. It is easy to recognize a dearth of access to the domains of Foresight and Scanning, Vision and Purpose, and Adaptability and Resilience. Further, the degree to students learn the domains and constructs of innovation skills from the public system remains unclear.

    Ultimately, now that these models exist, further analysis will be able to examine these constructs more closely as students progress through the system.

    This is especially true for many of the “optional” components of the broader education system. After school programs, hobbies, sports and recreation, volunteer and extra-curricular roles, self-directed learning, and employer training could each be vital sources of innovation education, but it was impossible to study these aspects of the system in any meaningful way in the present study. A dedicated effort should examine the availability of these sources and assess their utility for innovation learning.

    One research approach would be to survey learners along the learning journey, testing their abilities in the different constructs I’ve outlined. This ethnographic approach could reveal hidden truths: perhaps, for instance, certain regional cultures in the province actually provide powerful learning in design through a community culture alone.

    # Actor Mapping

    Systemic modelling reveals the power and wealth subsystems active amongst the actors of the education system.

    Centrality analysis of the power subsystem illustrates that parents and the provincial government have efficient influence on the system, and change that can mobilize those bodies of actors will quickly take shape.

    Meanwhile schools, the School Board, and educators have substantial global influence over the system–change efforts that engage these actors may be slow but momentous.

    Finally, power bottlenecks are educators; schools and school councils; and the Department of Education and Early Childhood Development. This suggests that these actors will ultimately need to be involved if any reform effort were to achieve success.

    Reach efficiency analysis of the wealth subsystem shows that the federal government, parents and students, and the provincial Department of Advanced Education and Skills each strongly influence the distribution of wealth. The Federal and Provincial Governments have powerful incentives with which to motivate and control reform efforts.

    Betweenness centrality revealed that the whole system is tightly linked, making it potentially volatile: economic issues in one component of the system may ripple out and impact the others.

    # Causal Loop Mapping

    Finally, these maps intimated a causal loop diagram illustrating how innovation education reform might happen in the public education system.

    Several loops and one archetype demonstrate significant effect over the system. The Low Definition loop describes an acceleration of the impact of ill-defined innovation on our ability to educate on it. The We Teach What We Know loop shows how a lack of innovation education leads to a lack of people capable of teaching it and vice-versa. The New Economy loop shows how economic shocks driven by drops in commodities pricing has raised our awareness of the importance of the innovation economy. The Innovation-driven Growth loop shows how innovation capacity will accelerate jobs in the knowledge economy, which will in turn drive our ability to create more innovators through education. The Limited Resources loop balances our ability to reform education for innovation due to a lack of funding for the reform effort due to austerity budgets, driven by drops in the price of oil. Finally, the R&D, Not Innovation archetype is an instance of the Fixes that Fail systems archetype, showing how a conflation of innovation with R&D efforts fails to improve our innovation capacity while also distracting from true innovation education.

    The result of centrality analysis on these causally-linked phenomena is rich with pragmatic insight.

    Three phenomena with efficient reach over the whole system are innovation learning from outside of the public education system, lack of emphasis on innovation education, and low price of oil. The former points to an accessible lever of change: introduce innovation education through extra- and co-curricular programs, volunteer and leadership roles, sports and recreation, or self-directed learning, and the system may catch up by offering its own programming to match. The leverage of a lack of emphasis on innovation education offers another route: increase awareness on innovation education in order to encourage the system to improve on it. Finally, low price of oil retains leverage as a dampener on the system: if the economy continues in recession, the system is less able to offer resources for reform efforts. 

    Other calls for reform is one force with substantial torque over the system, indicating that reformers must be co-opetitive with other education change efforts, else all reform efforts might fail due to competition with one another. The availability of accessible and practical models for innovation education is another high-torque element, however, elevating the potential of the present research to create change in the system. A third element with high torque is the generational shift in work, evidence that a substantial source of impetus for innovation education reform could come from changes in work and careers. 

    Finally, betweenness centrality offers a picture of the bottlenecks and points of failure within the system. Innovation capacity and innovation education are two forces semiotically central to the system, and thus it is intuitive that they will be slow to change, no matter what else is happening within the system. On the other hand, recognition of innovation deficiency, the perceived innovation gap, and the search for solutions to the innovation gap are three phenomena that are clear points of fragility in any systemic change effort. If the system does not recognize its deficiencies, perceive the gap in innovation capacity, or opt to search for solutions, reform efforts are liable to be frustrated.

    # Limitations

    Despite these clarion recommendations for systemic design, several limitations prevent wholesale adoption.

    One key limitation of the presented results of systems modelling is that the connections defined in these models are unquantified. In the refined actor map, for instance, it may be that educators have little power over their school councils, or perhaps the NL Federation of School Councils has far less lobbying capacity than the NL Federation of Teachers. Evaluating the strength of these connections and including these evaluations in our analytics would improve the acuity of those metrics substantially.

    As previously mentioned, if innovation learning is not coming from the public education system, it must be coming from somewhere else. Yet, these potential sources arguably include the whole of the human experience—as we have, after all, been learning to innovate since pre-history. Future research might take on an ethnographic approach to understanding the system, investigating different student-innovators and where they learned their innovation skills, or a longitudinal approach, following students as they become innovators through their years in the education system. These exercises fell outside the limits of the present research, unfortunately.

    Another limitation is that, while the scope and approach to mapping were designed to increase the variety of the system as much as possible, the mapping was still completed with the perspective of only this author. The representativeness of the systems models would therefore be strengthened considerably with Delphi-inspired methods as seen in previous research, bringing the mapping process to others in order to iteratively refine and the map from alternative stakeholders’ points-of-view.

    Another potential future study is to “bring the whole system into the room”. This would mean convening a group of stakeholders who were holistically representative of the actors of the system, engaging them in a systems modelling process to develop a map with their collective perspectives.

    # ACTIONS AND TAKEAWAYS

    # In The Short Term

    A few immediate actions stem from this research.

    # 1. Adopt A Model

    First, we must adopt a model of innovation skills and competencies.

    Regardless of whether the adopted model is the one developed through this project or another alternative, it is imperative that we begin to recognize the skills and competencies used by successful innovators. By identifying these skills, we will be capable of examining our weaknesses and, in turn, developing ways of resolving those weaknesses. To spur this discussion, I plan on sharing the models developed here widely. 

    # 2. Consider The Role Of Education In The Creation Of Canadian Innovators

    Second and in tandem, we must include the role of the education system in nurturing innovators in our provincial and national innovation strategies. Many approaches to innovation policy discuss the post-secondary education system with respect to its role in public-private partnerships and the commercialization of research. We must expand this role to include the development of innovation skills and competencies as well. In the near future I hope to meet with policymakers involved in the development of Newfoundland and Labrador’s innovation strategies to advocate for this approach there.

    # 3. Unite Education Reform Movements

    Education reform movements must be united in their calls for change. A host of movements relate to the notion of innovation education, from code.org (a non-profit urging computer science and programming education in K-12) to the 21st century learning movement (a pedagogical framework for the skills and knowledge necessary for the 21st century; cf. http://www.p21.org).

    The present research shows that these reform efforts may conflict, however, if they are brought forward asynchronously by their champions. It is therefore crucial that these efforts learn to “co-opete” (as in “co-opetition”) and engage educators and policymakers with aligned advocacy. I hope to work with the education systems change movements I already have relationships with in the immediate future in order to begin this dialogue.

    # In The Long Term

    As explored by David Stroh in Systems Thinking for Social Change, systems change is only possible when the actors of the system collectively recognize the tension between where the system is and where they want it to be.

    That realization isn’t possible, however, before the actors have even talked to one another—let alone come to consensus about a shared vision for the future. 

    We realize that Canada’s future prosperity is predicated on our ability to leverage the boons of our resource economy and evolve it into an “innovation rich” leader in the knowledge economy. Yet, as discussed at the beginning of this paper, the danger is that education’s role in this transformation has yet to be recognized in full. We are not talking about how to create innovators, let alone what strategies we should employ in doing so, or how the system is stuck in becoming better at innovation education. Worse, there are many simultaneously conversations happening in both education reform and innovation—conversations that compete with one another, threatening the potential of the whole.

    This research offers a model of the education system in Newfoundland and Labrador. Yet these models are untested, and as I have noted, the research is sorely lacking a futures perspective that observes both threats and strategic opportunities in our changing environment. 

    **How might we spark a collective, integrative discourse on innovation and innovation education? **Then, how might we elevate its importance such that collective action is taken—before we’ve missed the opportunities of the knowledge economy? How might we refine the systemic models, and how might we augment this work with a futures perspective, using environmental scanning to develop and integrate changing trends for strategic leverage? 

    These questions point toward a need for a powerful, strategic theory of change, and the willingness to muddle through. In other words, **this change will not come about through the efforts of ad hoc standalone initiatives like this one. **

    We need a sustained effort. We need a lab that brings together design science and systemic design, creating and testing designs of the system itself, making sure they are valid constructs of the concepts they are intended to represent, all while obeying the principles of systemic design. 

    This is not a new idea. Many have articulated the notion of social labs, design or change labs, or social innovation labs. In fact, the OECD’s Centre for Educational Research and Innovation seems to operate such an approach for systemic innovation in global education.

    I argue that Canada–or at least, Newfoundland and Labrador–needs to take a lab-based approach to navigating complex education reform in education. This lab must unite the perspectives, strategies, and actors currently engaged in similar pursuits; build, maintain, and refine models of the systemic change taking place; be engaged in environmental scanning and strategic foresight to monitor for both threats and opportunities; and prototype change initiatives, taking lessons back to these models and strategies.

    Only a dedicated, intelligent effort will help us build the education systems that will develop the skills and knowledge we need to answer the 21st century. 


    Innovation Education is a major research project presented to OCAD University in partial fulfillment of the requirements for the degree of Master of Design in Strategic Foresight & Innovation. This work was supported by the Social Sciences and Humanities Research Council of Canada.

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  • Intuition is confident abduction

    Last updated Nov 7, 2020 | Originally published Nov 7, 2020

    # Intuition is confident abductive-inferential thinking

    In a recent episode of Hello Monday, Jessi Hempel interviews Dr. Natalie Nixon on creativity and her new book, The Creativity Leap. Natalie’s PhD in Design Management—plus her work in fashion, design, and business—led her to a catchy and compelling description of creative work. We accomplish creative work, she says, “by toggling between wonder and rigour.”

    In the podcast conversation, Jessi and Natalie talk about intuition—and I was struck by something. “We don’t talk about intuition,” Natalie notes at about 6 minutes in. “We don’t talk about intuition in business school, in law school, or in medical school.” And yet, she says, “I observed that really successful leaders—especially really successful startup leaders—in their origin stories, there’s always this moment where ‘Something told me not to do the deal. Something told me to work with her over him.’ […] Every successful leader really reckons with incorporating acting on their intuition to make decisions.” Jessi agrees, noting that intuition comes up often in her interviews with leaders on Hello Monday as leaders cite it as the reason for their success.

    The thing is, just because we don’t name intuition doesn’t mean we aren’t talking about it. That’s because intuition is really just confident, logical thinking.

    Charles Sanders Peirce was a philosopher. He investigated how we inquire into and discover new knowledge.1 Before Peirce, we generally recognized the logical processes of deduction and induction. Deductive thinking helps us identify what must be true about a situation in order to explain it. When we deduce something, we look at the general rules and principles we know of and draw specific conclusions from that evidence. Inductive thinking involves drawing general conclusions from specific, limited evidence.

    Peirce argued that effective reasoning follows a pattern: we determine the specific consequences of an idea (deduction), and then we judge whether the available evidence fits that idea and its consequences (induction). But how do we develop ideas?2

    Abduction is the name of the logical process Peirce described for developing ideas. To think abductively means to generate and choose ideas that fit the situation at hand. A good idea should be verifiable—we should be able to use evidence to judge its fit—and should help us resolve the situation at hand. Peirce also had criteria to help choose the best ideas to test. He suggested that we should strive to conserve resources (e.g., those that most are most efficiently verifiable and usable in the situation), identify the most valuable ideas (specifically the “uberty” of an idea, or how likely it is that a possible idea might bring about an innovation), and the most relevant ideas (e.g., those that may apply beyond our current focus, too).3

    Abduction is clearly an important step in any innovative process—but it is no more important than testing and using the ideas you generate. What, then, if you don’t have enough evidence to truly test and prove your ideas?

    The process Peirce described—abduction, deduction, induction—is the ideal. However, we do not always have time and energy to follow the process diligently. Instead, we quickly make creative judgements based on a few observed qualities. This requires two related processes.4 The first Peirce called “abductory induction,” and it combines the first and last step of the inquiry process. We observe the qualities of the situation, and we generate possible ideas to resolve it based on those observations. The second process is known as “inference to the best explanation” (IBE).5 IBE is exactly what it sounds like. Given a number of possible ways of resolving a problem, choose the best one. (Peirce’s criteria, noted above, apply here.)

    So what does all this have to do with intuition?

    Intuition is the confident application of these shorthand logical approaches to creative problem solving. As Jessi and Natalie noted, we aren’t often explicitly taught about strengthening our intuition. Yet, everything we learn supports its development. The more we have to draw on in order to pull into the processes described above, the better our intuitive decisions will be.

    I say that intuition is the confident application of these processes because they only work when we follow through. In reality, we use abductory induction and IBE all the time. When we engage in creative problem solving, we’re not only using information from the evidence in front of us. We’re drawing on our lived experience and our knowledge base. Even if we don’t directly recall or reference that background information, it is drawn into the creativity of abduction and it defines the general rules and principles we use in deduction. It provides us with the heuristics we use when engaging in IBE. But if we don’t have a bias towards action and instead operate with e.g., perfectionism, we fail to actually execute on these ideas. Thus, we need to have confidence in our abductory induction and IBE processes.

    All this is simply a gentle challenge of the idea that we don’t talk about intuition. I think that all knowledge management practices and forms of education are actually fundamentally about strengthening our intuition.

    That said, Natalie’s work is fascinating. I recommend the episode of Hello Monday and plan on picking up her book!


    1. In this article, my reading of Peirce comes from the writing of William Mcauliffe↩︎

    2. Peirce was actually specifically concerned with science and hypotheses generation, selection, and testing. Here I refer to generating, selecting, testing, and using ideas to apply these concepts to problem-solving more broadly. ↩︎

    3. He also cautioned not to produce ideas that stop the inquiry process—e.g., magical thinking, or by suggesting that whatever happened must be a complete mystery. ↩︎

    4. Actually, the difference between these two processes is the subject of substantive, controversial debate. This is in part because the scholars who study inference to the best explanation have also used Peirce’s term “abduction” to describe it. This understandably caused extensive confusion, but also probably a lot of philosophical debates and scholarship, so maybe it was for the best. ↩︎

    5. Philosopher Gilbert Harman originally described and named this process… and mistakenly suggested it was the same thing as abduction. ↩︎

  • Roger Martin, Bianca Andreescu, and systemic strategy

    Published Apr 11, 2020

    According to designer/strategist Roger Martin, a strategy is an imagined possibility with which we ask the question “What has to be true for this possibility to become real?”

    In this episode IDEO U’s Creative Confidence podcast, Roger talks about how that approach helped unlock Bianca Andreescu’s success at the Grand Slam singles championship in 2019.

    One of the fascinating things about this approach is that it acknowledges the need for system-wide changes. By asking “What has to be true?”, a strategist must consider all of the conditions of a system that should shift to make the imagined possibility a reality. Of course, most approaches to strategy do require some appreciation of the state of the strategic environment (e.g., the five forces model). None, however, emphasize the need to guarantee these systemic conditions quite as explicitly as asking “What must be true?”

  • Bring It On Haters With Special Guest Ben Thompson

    Published Feb 9, 2020

    Ben Thompson, in discussion with John Gruber:

    It was mindblowing. It was absolutely incredible. The way that you could just do stuff that wasn’t really possible [on a computer]. Again, it was technically possible on a computer, but the user interface and experience was just transformative on the iPad. It was absolutely incredible.

    And Jobs knew it. It’s one of my all-time favourites Jobs moments. It’s like fifteen seconds after the demo, and it’s just like… he’s used this. He was involved in the creation of it. They had run through the demo. He knew it. And even then, he was just astonished. He’s just like ‘I can’t believe [this]…’

    […]

    It was, to my mind, the culmination of his life’s work. He comes on there, and he’s like, ‘Isn’t it incredible? Now anyone can make music.’

    I almost want to transcribe this whole episode. John Gruber and Ben Thompson discuss the potential of the iPad—and its failure to reach it.

    Ben uses the term “transformative” deliberately above. They discuss how, before the iPad, no computing experience could adapt to become wholly new tools and environments for whatever the user wanted to do. But the iPad can become a piano or a canvas or a television. In this sense, they argue that the iPad has (or had) the potential for disruptive innovation (RIP Clay Christensen)—but it’s not supposed to be a Mac.

    These two think the iPad’s lost the chance to fulfill that potential, mostly because Apple has missed the opportunity to build a vibrant developer ecosystem due to App Store policies. I hope that isn’t the case, though I think we have to look beyond the iPad to fully appreciate what might happen next. The introduction of tablets and transformative computing experiences continues to echo throughout a variety of industries. Graphic designers and illustrators have a new suite of tools to directly interact with their creations in the iPad Pro and the Surface. Similarly, tablet or hybrid devices have transformed schools—schoolchildren now have a “homework” device for all kinds of assignments. It’s true that we still need developers to imagine ever-more revolutionary applications for these devices, but there’s no denying that disruption is taking root.

    Either way, the episode is well worth a listen. Enjoy from 15:50 to ~31:22 and 1:26:59 to the end of the show if you want to focus on the iPad discussion.

  • Paul Jarvis on Hurry Slowly: Small is Beautiful

    Published Jan 23, 2020

    The ever-refreshing Paul Jarvis shares some uncommon thoughts on productivity in Jocelyn K. Glei’s Hurry Slowly podcast.

    In particular, Paul and Jocelyn discuss the importance of resilience. Citing research and his own experience, Paul points out that resilience is a more important factor in success than many others.

    Obviously, though, enabling resilience is not as easy as simply pointing out how important it is. As they discuss, resilience isn’t something innate—which means that it can only be developed through experience. And this is where things get tricky: who gets to have resilience-building experiences?

    In my research on innovation skills, I discovered that resilience was one of three key domains that wasn’t an important outcome for our public education systems. This means that resilience training isn’t necessarily a public good. Only if you’re lucky (or privileged) will you have the chance to build up your resilience muscle.

  • Keeping the buzz in buzzwords

    Published Jan 23, 2020

    A thought-terminating cliché limits conversation by capturing a complex (but potentially debatable) subject within a reductive term or phrase. Merlin Mann references this idea in episode 164 of Back to Work when discussing curiosity and buzzwords.

    Thought-terminating clichés can be used to avoid discourse on a subject: by never unpacking the components of an idea that are debatable, those components go unexplored. They can also be exploited to veil ignorance or illogic—the speaker can state the complex term and allow the implication to have impact without contextualizing/explaining it while the intimidated audience shies away from critique or questioning.

    This explanation makes the phenomena seem villainous, but many of us are prone to committing these crimes—through buzzwords! Buzzwords are terms that catch on because they represent something exciting to a discourse. Then, because they’re popular, they get used frequently, by many people. Because they are somewhat novel, these different uses attach slightly different meanings to the same word. Eventually the buzzword’s overused (reducing the novelty, and therefore the impact of its meaning) and/or overloaded with meaning.

    Most of our buzzwords were real things at one point (and sometimes they still are). When buzzwords are used effectively they allow a good conversation to move faster between speakers who have the same mental models about the buzzwords.1

    Sometimes, however, buzzwords are said to represent concepts that aren’t fully understood by everyone in the conversation. When my meaning of the word “design thinking” differs from yours, but we both refer to design thinking in conversation nonetheless, we can run into trouble.

    In these situations, buzzwords obfuscate the ideas we’re actually talking about. In my experience, we also know when we’re using buzzwords. We can guess at when others are using them, too. As a result, the conversation loses meaning, and we lose trust in the conversation.

    Buzzword meaning space. The three colored shapes are three different meanings attached to the same buzzword.

    # Dealing with buzzwords

    So what can we do?

    Well, the easy thing to do is to clarify. When you use a phrase with many potential interpretations, try to clarify how you’re using the phrase. When others use words that may have multiple meanings, ask specific questions about what they actually mean. This clarification might seem like extra work, but it only needs to happen when terms are first invoked—and it’ll prevent lost time and energy due to the consequences of thought-termination later on.

    More importantly, though, we should try to avoid thought-terminating clichés altogether. Take time to break down the concepts you’re talking about in concrete terms. Explain them in ways you haven’t heard before to avoid relying on trite metaphors and anecdotes. If you can really get at what you mean, your language will be minimally re-interpretable: that is, it should be near-impossible to understand your explanations differently from how you intended.

    As a result, your communication will become more impactful. The conversations you participate in will have more novelty, too, making it more exciting to discuss the ideas you’re sharing. This may result in more buzzwords emerging, but that’s okay—use the same approach to break those down, too.

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  • Systemic Evaluation

    Last updated Mar 7, 2023 | Originally published Mar 7, 2023

    Systemic evaluation is the developmental evaluation (Guijt et al., 2012) of systemic change.

    Techniques for systemic evaluation combine conventional principles and tools of developmental evaluation with concepts from systemic design. These techniques provide changemakers with the ability to assess the accuracy and completeness of their theories of systemic change and action (Murphy & Jones, 2020). They also allow evaluators to examine the progress of systemic strategies (Murphy et al., 2021).

  • Leverage theory

    Last updated Mar 7, 2023 | Originally published Feb 24, 2023

    We seek leverage to find the best ways of making change.

    Leverage points are places in systems where a little effort yields a big effect (Meadows, 1997). They are also ideas that help us grab on to strategic ways forward when we’re working in complexity (Klein & Wolf, 1998).

    Acting on leverage points may accelerate systemic change towards progress and reform, but acting on the wrong ones may instead accelerate systemic change towards regression and deformity. Well-designed leverage strategies may be catalyzing or even transformative, but poorly designed ones may merely be futile (figure 1).

    One way of finding leverage points is to think through your system with reference to Meadows’s (1997) 12 types:

    Table 1. Twelve types of leverage points, in order of increasing power (adapted from Meadows, 1997).

    Twelve types of leverage points, in order of increasing power Example
    12. Constants, parameters, numbers (such as subsidies, taxes, standards) Wages, interest rates
    11. The sizes of buffers and other stabilizing stocks, relative to their flows. Current levels of debt/assets
    10. The structure of material stocks and flows (such as transport networks, population age structures) An individual’s financial structure (e.g., fixed costs and incomes)
    9. The lengths of delays, relative to the rate of system change How long it takes to find a higher-paying job
    8. The strength of negative feedback loops, relative to the impacts they are trying to correct against Rising costs of living vs. fixed income
    7. The gain around driving positive feedback loops Recession causing reducing spending
    6. The structure of information flows (who does and does not have access to what kinds of information) How aware you are of impending recession/future rising costs
    5. The rules of the system (such as incentives, punishments, constraints) Who suffers as a result of poorly-managed recessions
    4. The power to add, change, evolve, or self-organize system structure Central banks, Ministries of Finance
    3. The goals of the system GDP Growth
    2. The mindset or paradigm out of which the system—its goals, structure, rules, delays, parameters—arises Growth above all
    1. The power to transcend paradigms Sustainable development, flourishing

    Another approach, which may be complementary to the above, is to model the system as a causal loop diagram (e.g., Kim, 1992) and then to conduct leverage analysis (Murphy & Jones, 2020) on the model.

    An understanding of leverage in a system allows us to generate systemic strategies (Murphy & Jones, 2020). These strategies can also be adapted into Theories of Systemic Change (Murphy & Jones, 2020).

    # Background

    Donella Meadows (1997) popularized the idea of leverage in systemic change with her essay “Leverage Points: Places to Intervene in Complex Systems.” She proposed a typology of phenomena in a system, suggesting that acting on certain types of phenomena are higher-leverage than others.

    In an article published in the Contexts journal of systemic design, I challenged Meadows’s (1997) paradigm, proposing a few other possible ways of viewing leverage. My aim was to link the search for leverage directly to the design of powerful strategies for systemic change, and to propose a few ways forward in advancing our understanding of leverage in complex systems.

  • Towards a theory of leverage for strategic systemic change

    Last updated Feb 24, 2023 | Originally published Feb 24, 2023

    My article “Leverage for Systemic Change” was recently published in the inaugural edition of Contexts, from the Systemic Design Association.

    The article ultimately proposes a few key directions for a research agenda on leverage in systemic design (see the table below).

    Table 1. A research agenda for leverage theory in systemic design

    Research area Research questions Existing research Possible studies Possible contributions
    Dimensions of leverage - Is Meadows’s (1997) typology complete?
    - What other features of the “physics” of systemic change might matter?
    - System characteristics (Abson et al., 2017)
    - Conditions for systemic change Kania, Kramer, & Senge, 2018)
    - Other types of phenomena (e.g., bottlenecks, signals; Murphy & Jones, 2020)
    - Relative leverage: chaining leverage points (Fischer & Riechers, 2019)
    - Relative leverage: the context of the changemaker (Klein & Wolf, 1998)
    - Recursive leverage
    - A systematic literature review (Okoli & Schabram, 2010) of leverage points, especially using forward citations (Haddaway et al., 2022) from (Meadows, 1997)

    - Understanding the nature of leverage and other mechanisms of change potential in systemic change
    Methods for leverage - What methodologies are best to identify and select leverage points?
    What kinds of evidence will help validate leverage?
    - How might systemic designers design theories of change (Gregor & Jones, 2007) for leverage theories?
    - How might systemic designers limit indeterminism (Lukyanenko & Parsons, 2020) in leverage theories?
    - Meadows’s (1997) typology’s order of effectiveness
    - Leverage analysis [Murphy & Jones, 2020]
    - Assessing potential for change (Birney, 2021)
    - Surveying practitioners in systemic design on how they identify, assess, and address leverage points to identify common habits and best practices - How to identify phenomena useful for leverage
    - How to evaluate and compare possible leverage points in the analysis phase
    - How to evaluate the effectiveness of chosen leverage points with evidence gathered from implementations
    Strategy with leverage - How is leverage best used in developing strategic plans for systemic change?
    - How are leverage-based strategies best presented and communicated?
    - How are leverage-based strategies best evaluated and measured?
    - Systemic strategy (Murphy & Jones, 2021)
    - The epistemic benefits of a leverage points perspective (Fischer & Riechers, 2019)
    - Identifying conditions for systemic change (Kania et al., 2018)
    - Relative leverage: chaining leverage points (Fischer & Riechers, 2019)
    - Relative leverage: the context of the changemaker (Klein & Wolf, 1998)