Returning Learning to the System

When a honey bee finds a patch of nectar-rich flowers, it returns to the hive, deposits its find, and does a “waggle dance” to let fellow bees know the direction and distance of those flowers from the hive. According to Complex Adaptive Systems theory, this is what a system needs to do in order to adapt. The more frequently members of a system communicate with each other about what they are seeing, what they are doing, and with what results, the more quickly that system as a whole is able to adapt to changing conditions in order to survive and thrive.

Fourth Quadrant Partners just completed A Whole Greater than Its Parts, a research study on the role of emergence in complex social change initiatives. We wanted to explore truly emergent initiatives — initiatives that are designed to allow the whole system to learn and adapt. What do they look like? What does it take to create them? And what do they make possible? We predicted that emergent initiatives would be better able to survive and thrive. They would:

  • produce non-linear results — results that are greater than the sum of the inputs
  • produce results that were more fit to their diverse and changing environments
  • expand agency and ownership and, therefore, be less dependent on sustained outside support

We put out a call in 2016. Out of 45 nominated initiatives, we selected seven to study — three initiatives that had been in existence for over ten years and four more recent initiatives. They ranged from an initiative to improve reproductive health in five countries in Africa and Asia to a place for children to gather at a local flea market in Gallup, New Mexico.*

One thing we were looking for was their version of the “here’s where I found the nectar” bee dance. People across an initiative needed a way to easily and regularly communicate to peers about what they are seeing and doing, and with what results, and a way for the community of peers to compare these stories, look for patterns, make meaning from them, and adjust their work accordingly. They needed, in other words, to return what they were learning to the system.

We saw several different kinds of learning happening — from annual peer-learning events to reflection days for community residents to storytelling and participatory evaluations. Across our seven cases, those that did the most “waggling” got the most emergent results. But even still, this quality of learning was mostly not happening often enough to be a true engine of emergence. The reasons will be familiar:

  • In the rush to deliver, one initiative team did not prioritize time to stop and reflect.
  • Local initiatives had an advantage. An initiative that was spread across several countries did the best they could, which was a lot by compared to common practice. But with the support of today’s technology, they could have connected the whole system of actors more easily and more often.
  • Interestingly, where initiative teams held a strong boundary between themselves and agents working on the ground — whether to control the level of complexity or to protect the freedom of intermediaries and grantees to make their own decisions — it reduced their ability to return learning to the whole system in a way that supported emergent results.

People spread across a system trying to create change can’t afford to wait for a once-a-year convening or a five-year evaluation report to learn from and with each other. The best example of returning learning to the system in our study is funded by Community Foundations of Texas (CFT). In Working Families Success (WFS), the foundation created a data-rich online platform and encouraged frequent interactions between social agencies to compare notes.

CFT deliberately has not positioned itself as the hub. They encourage peers to communicate with each other independent of CFT, and model a learning stance itself as they have learned and adapted their own thinking with each initiative cohort. While it’s still early, all of this investment is producing a lot of energy and culture shifts and new partnerships among local agencies. It is getting agencies to rethink long-standing programs that aren’t contributing, and to double down on others based on their own deliberate experimentation and discovery. “Rather than telling them what to do, you coach them through the decisions they need to make,” observed Wende Burton, CFT’s Community Philanthropy Director.

Funders can help return learning to the system. As the WFS initiative suggests, it may be useful to think about multiple kinds of learning supports — places to collect stories and have access to data; easy ways to ask for help from peers; frequent but fit-for-purpose learning events; and decision-making processes that incorporate reflection on past results. And when funders convene grantees and partners, focusing on this question, “What will it take to return learning to the system?” may help make visible ideas and solutions that no one person could have thought of on their own and that continue to evolve to adapt to changing and complex environments. Because, as we say in the report, there is much more to learn … always.

*The 4QP research team thanks the William and Flora Hewlett Foundation, the David and Lucile Packard Foundation, and the John S. and James L. Knight Foundation for their generous support of this research.

Originally published as a GEO Perspectives column

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When should you invest in an emergent approach?

The core idea of emergence is that it is nonlinear; it should create a whole that is greater than the sum of its parts — a compelling idea to funders who are striving to create a sustainable impact on complex problems with relatively modest investments. As we announced in a 2016 post here on the CEP blog, my colleagues and I at Fourth Quadrant Partners launched a research project (supported by the William and Flora Hewlett Foundation, the David and Lucile Packard Foundation, and the John S. and James L. Knight Foundation) by asking the question: What’s the value proposition of emergence? We wanted to know what an emergent initiative really looks like in practice and what funders should expect to get out of investing in one.

We asked readers to nominate examples of initiatives that were in some way emergent — meaning that ideas emerged from a diverse set of people doing the work (rather than being designed in advance and rolled out), the path to success could not have been completely predicted in advance, and the solutions were fit to their environment and continued to evolve over time and circumstance.

From a pool of 45 nominated initiatives, we chose seven and spent the next two years comparing and contrasting them, trying to understand: 1) if they were, in fact, emergent; 2) what that looked like in practice; and 3) what difference it made in what they were able to do.

We saw some remarkable results from a wide range of initiatives, from a multinational health initiative to very small, local initiatives that produced an outsized, sustained difference in the problems or communities they targeted. Our report and case studies are available on our website here.

But we learned from studying these cases that there are tradeoffs to consider. Based on what we learned from the initiatives we studied, here are some questions funders should consider when thinking about investing in an emergent approach.

Can you feel the complexity of the problem?

Complexity can take a number of forms. It may be obvious — such as when you’re working across widely varying geographies or trying to improve quality of life in a single neighborhood dealing with many interacting factors that feed the status quo. But in the initiatives we studied, the level of complexity itself was less important than the recognition of it.

Funders of initiatives that succeeded in getting the most emergent results had a felt experience that the problem was complex enough that they could not rely on their own expertise to develop the best solution a priori — or had tried and not succeeded in solving it using more funder-centric strategic frameworks. They had the humility to recognize that they depended on the experience and perspective of their partners on the ground doing the work, and, therefore, gave partners the latitude to experiment with different approaches.

How pressed are you to demonstrate a predetermined, measurable outcome?

Of the initiatives we studied, the one that was most urgent — a response to a crisis — was the most driven to deliver predetermined outcomes. The other initiatives we studied generally were not driving to measurable outcomes. Yet, they each had a recognizable goal and held themselves accountable to staying focused on it. They used their goals to orient themselves and learn, but were not constrained by predetermined deliverables.

Whether because of modest funding or low perceived risk, the less in the spotlight an initiative was, the more freedom funders and their partners seemed to have to draw outside the lines. And those most emergent initiatives welcomed and learned from outlier ideas and results that had not been pre-planned.

How important is it to you to prove a theory or promote your solution?

Let’s be honest. Funders often have a stake in more than just moving the needle on a social problem — they want to get credit for it. And funders or their partners are sometimes interested in demonstrating the value of their preferred approach so they can brand it. For the most emergent initiatives, moving the needle was always more important than proving a favored hypothesis. We heard from grantees how different it felt to be part of an emergent initiative in which they were not being asked to implement a “cookie-cutter” solution; but rather had their context, perspective, and experience taken seriously.

This led us to ask: Can emergence be propagated? If an initiative achieves remarkable results and an emergent design is one of the contributors, what does it take to “replicate” those results elsewhere? We will be tracking a couple of examples of initiatives that are in the process of being branded and propagated.

What’s your appetite for learning?

This may be the most critical factor in choosing to invest in emergence. Across our seven cases, we discovered that the biggest challenge — and one that each initiative would have benefitted from tackling — was the ability to return learning to the system. This is a fundamental driver of emergence. Akin to honey bees coming back to their hive and doing a “waggle dance” to communicate where they find nectar-rich flowers, initiatives needed to include some way for partners to be able to quickly and easily share with each other what they were doing, what results they were getting, and what they were learning from it.

In some cases, funders invested in learning as best they could, but could have done more. In others, learning was an afterthought. When funders stopped being hands off and actively engaged in learning from and with everyone in the system, they were setting the stage to create a whole greater than its parts.

The value proposition for emergence can be compelling. But we encourage funders to be honest with themselves about whether they are prepared to let go of the need for credit and recognize and welcome the experience and perspective of everyone in a system to help solve today’s most challenging social issues. There is much more to learn . . . always.

Originally published as a CEP blog

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The journey is the reward

Our research into the role of emergence in complex social change is finished . . . sort of. In fact, it’s really just a step on a longer journey that we talk about in our cover letter for the report (p. 4), which you can now download from our website: www.4QPartners.com.

When Steve Jobs and his team were working on their new project, the Macintosh, he would motivate his team by reminding them that “the journey is the reward.” From my conversations with people who were working at Apple at the time, the phrase took on almost mystical importance. The team applied it to everything associated with the project — the computer’s design and the way it was to be marketed, but also to how they thought about every aspect of their own work as a team. This simple idea created a coherence around the project that left space for members of the team to exercise their creativity about how to approach their work. And while today we might see the original Mac as horribly antiquated, this small computer with a graphical interface that said “hello” when you turned it on did, in fact, start a revolution in the way we work today.

One of the things we learned in our own research into the role of emergence in complex social change is that, for those initiative teams that were creating emergent results, what they were doing was not revolutionary. They used networks, data platforms, participatory meeting methodologies, and participatory evaluations. What they did that seemed to support emergence was to apply what they were thinking about and learning from their initiatives to their own work in a way that amplified their results. It unleashed agency and creativity in a way that an initiative that had been pre-designed and rolled out by some external set of funders and experts could not have mustered.

It is in this spirit that we started our research report with the statement that we have been on a long journey. Many readers of this blog will understand when we say that we essentially conducted a two-year-long Emergent Learning Table, populated with seven case studies that we continually compared and contrasted as new questions arose. As much as possible, during the project, we applied what we were learning to the opportunities of our own client work, our certification program on Emergent Learning, and to how we operate ourselves as a partnership. We just intuitively believed that we would learn more and produce better research if we were trying these ideas out ourselves along the path.

The research led us to focus in particular on how we use our own learning log and weekly learning calls — creating the space to discover and explore patterns across the research and our own work. Sometimes we would start with a research question but, just as often, we just dove in to discover what struck us. “Have we seen this somewhere else? What do we think about it?” These conversations would lead us to ask a different question or try something out we hadn’t thought of before. The next week we’d bring back what we discovered. We found ourselves asking new questions, experimenting more and returning learning to the system as much as possible. This is changing the questions that our clients and our community are asking as well, and we are keenly aware now that the better we get at doing this ourselves, the more quickly we will amplify learning across our own ecosystem.

Our research report represents where we are today on our journey. We invite your comments, questions, critiques, ideas. We also invite you to share other stories that you think might represent emergence in complex social change. As we say in the report,  there is much more to learn . . . always.

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Learning Our Way through Uncharted Waters

Well, the election is over. You could say that we’re stepping into “interesting times.” I used to work with a CEO in the corporate world who talked a lot about needing to prepare for “right-angle turns” — changes in the environment where you simply couldn’t see around the corner, but you still needed to be prepared to act and adjust, act and adjust. That pretty much sums up, I think, what’s probably in store for us in the year ahead, regardless of your politics. The one thing you can predict is that what worked last year may not work now. It’s a time to re-think assumptions.

From a learning perspective, this change in the national political landscape provides a significant opportunity, if not a mandate, to bump up your game. That’s where the field of Emergent Learning really shines. Emergent Learning is literally about learning that emerges from tackling the real-world challenges in your day-to-day work — how to make thinking visible to each other and test it out, then bring what you learn back to your colleagues to help refine your shared thinking, and so on. It’s especially valuable when you can’t take your assumptions for granted.

My partners and I have spent a lot of years of research and practice in environments full of right-angle turns, in both the corporate sector and the social sector. We’ve also been long-time students of research into how complex systems adapt. The confluence of these has led us to advocate that, if you really want to adapt as quickly as possible, you need two things:  1) really strong line of sight, so that everyone knows what success would look like, and, at the same time — and this is important — 2) to make sure that everyone has as much freedom to experiment as humanly possible. It’s not a time for putting all of your eggs into a single strategy. Times like these in particular call on the wisdom of all of us — to stay the course in terms of your big goals and, at the same time, to be very deliberate about experimenting with new moves.

This also means is that it’s not a time to defer to a single source of expertise. In a complex environment, no one person holds enough perspective to be able to come up with a complete solution on their own. It really does “take a village.” At 4QP, we talk about the importance of seeing each other as ‘experts in equal measure.’ That it is about all of us, ideally including your grantees and partners, learning our way through these potentially challenging times together. This is hard to do, but essential in an unpredictable environment like the one we are facing in 2017.

It also means that our normal cycles for strategy, for grantmaking, for evaluation, are far too long to be useful in complex environments. I wrote a research report a few years ago, “A Compass in the Woods” that described this problem. Like ants looking for food, we need to be rubbing our antennae together a lot more often.

May you succeed in turning the unknowns that lie ahead into truly transformative opportunities to make a difference in the lives of the people you serve.

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Complex Adaptive Systems: a definition

As we launch our new sponsored research project, Exploring Emergence in Complex Social Change Initiatives, we realize that the field of work on which it is based might be new to many of our colleagues. While much has been written about the field of Complex Adaptive Systems, much of it is written for scientists and often incomprehensible to non-scientists. Here is our attempt to define this important work in layman’s terms.

A complex system consists of many, diverse parts, all of which interact with each other and, in so doing, create patterns that are more sophisticated than any one part operating on its own. Human languages are capable of an endless variety of meaningful communication, using a fairly small set of letters and punctuation marks, with some rules about how to combine them into words and how to combine words into sentences. Snowflakes are formed in beautiful patterns, all of which are made through the random interaction of ice crystals. Both snowflakes and human communication are endlessly varied, without having to be consciously designed in advance. Human beings are also examples of complex systems. We are composed of many, diverse cells, each of which have limited capabilities. But through the cells’ many interactions, the behavior we are capable of is endlessly rich and complex. If our cells did not interact, if ice crystals did not adhere to each other, this quality of rich behavior would be impossible. These richer patterns of behavior are said to “emerge” from these many random interactions.

As a system, we are also adaptive. Unlike a snowflake, our collection of cells is capable of having a goal — survival, reproduction, comfort, wealth — and to adapt to feedback from our environment in order to achieve it. The same can be said for our immune system or an ant colony. Collectively, through the constant interaction of individual entities, or “agents,” as they are called by complexity scientists, the larger system of which they are a part, is capable of responding to our environments in ways that take us closer to a goal than any individual agent would be capable of on its own.

A complex adaptive system is non-linear. It can be distinguished from a machine, which exists because it had a designer who could predict in advance how a particular combination of components would operate together to produce a specific behavior. Complex adaptive systems are not predictable in the same way. Researchers at the Santa Fe Institute, an important center for complexity science, have done fascinating research using complex adaptive systems as a frame, for example, to understand how cities and economies behave.

As we describe in the announcement to our research on Emergence, the social sector has begun to understand that the systems it hopes to impact are also highly complex, which suggests that we need to think differently about what it takes to achieve the kinds of impacts we aspire to create.

For example, complexity scientists emphasize that agents in a complex adaptive system behave according to simple rules and it is through the simplicity of those rules that rich patterns of behavior emerge. A frequently cited example is how birds flock. The rules that generate that behavior are very simple and do not include identifying a leader, famously modeled by Craig Reynolds in his BOIDS simulation. The corollary to that, described by Stephen Johnson in his book Emergence, is especially relevant to philanthropists: “Emergent systems can grow unwieldy when their component parts become excessively complicated. Better to build a densely interconnected system with simple elements, and let the more sophisticated behavior trickle up.”

We anticipate that our research will help us understand better how to think about the process and benefits of emergence and help funders, grantees and other partners in the social sector understand what they can do to improve their impact as they work to achieve complex goals in complex systems.

For more information on Complex Adaptive Systems, we recommend Emergence by Steven Johnson as a comprehensible and enjoyable description of how this theory can be applied to understanding everything from ants to brains to cities to software design. A Hidden Order by John Holland gives an in-depth description of the principles of complex adaptive systems theory.

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Emergent Strategy, iPhones and Social Impact

I am just back from the 2015 Center for Effective Philanthropy (CEP) Conference, where Phil Buchanan invited Patti Patrizi and me to bring together — for the first time — our many collective years of thinking and doing work in the realm of Emergent Strategy and Emergent Learning.

Patti talked about the great article she and colleagues Liz Thompson, Julia Coffman and Tanya Beer wrote for Foundation Review in 2013, “Eyes Wide Open: Learning as Strategy Under Conditions of Complexity and Uncertainty.” She talked about how, in complex environments, conventional strategic processes first developed for more predictable environments lead to common “traps.” Philanthropic leaders search for certainty through simple, linear logic models; become overly dependent on measures that are developed too quickly to actually measure anything of importance; and, perhaps as a result, “outsource” learning to experts like us, at the expense of respecting their own good thinking.

She and I agree that, in complex and evolving environments, as she described it, “strategy cannot be engineered a priori” because no one has the crystal ball it would take to completely predict a future that is yet to unfold in complex ways; and, finally, that it requires trial and error by a whole community — i.e., an emergent strategy — to achieve real social impact. She shared the great story of Robert Wood Johnson Foundation and their effort to address end-of-life care. After investing in one big bet and visibly failing, they humbly acknowledged that they were going to need to learn their way to a solution. After lots of smaller experiments, including some stops and starts, and through collaborations with other partners like Open Society Foundations, they ultimately helped spur and grow the now successful field of palliative care — a result that no one could have engineered in advance.

As we were putting our talk together, I was reminded of my favorite example of emergence (in the complexity science sense). I love this example because it is a great big experiment in emergence that all of us not only know about but in which we are all active participants.

This is how I described it in our session:

“Steve Jobs was brilliant, but if the iPhone could only ever do those things that Jobs and his team thought up themselves, it would not be the powerful tool we use today. In essence, they designed a platform, which created a whole new field of play, which is itself emerging. Because all of these designers are learning simultaneously how to design for it and users are learning how to interact with it, our collective ecosystem is making it possible for designers to create even more innovative apps that no one could have thought of, much less being capable of designing or using, even two years ago. And no one person can predict how we will use them two years from now.”

Imagine creating that kind of social impact from your investments.

For readers who are familiar with the tools of Emergent Learning, you will know that we talk about them as a “platform.” This is intentional. Just as how Apple designed the iPhone created a “field of play” that resulted in the emergence of powerful new apps, we intend for the tools of Emergent Learning to help foundations set the stage for the emergence of powerful new solutions to complex social problems.

In our session, Patti and I challenged foundations to have the courage and humility to shift how they approach their initiatives. We challenged them to shift from funding, say, “Education and Displacement” initiatives with complex frameworks and expert models they expect grantees to implement, to asking a Framing Question like, “What will it take to ensure that displaced youth living in refugee camps achieve the same level of education as their peers?” A question like this invites to the table the collective wisdom and experience of everyone in the system; it gives everyone the latitude to experiment with solutions simultaneously — like the community of mobile app developers — to see what rises to the top and becomes the foundation of true social impact.

— Marilyn Darling

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A 4QP talk at Systems Thinking in Action

2013 was a stand-out year for us at 4QP.

Our first annual Emergent Learning Community of Practitioners completes their year-long Certification Program later this month. As we predicted, their experiences and perspective have expanded our own understanding and practice of the craft.

Our rich conversations over the year reminded us of the theory in which this work is rooted. We had the opportunity this fall at the Systems Thinking in Action Conference to talk about one of those roots: Complex Adaptive Systems theory.

We often say that there are two ways to think about the “Emergent” in Emergent Learning:

Most immediately, Emergent Learning is learning that emerges from the work itself, in the course of doing the work, with as little overhead as possible, so that groups of people accelerate their results together by being deliberate about testing their thinking in real time.

On a more macro level, complex behavior (think “mastery”) emerges from lots of individual interactions among “agents” (think “people”). We talk about “rubbing our stories together.” These interactions generate rules that make it possible to interact in more catalytic and sophisticated ways to achieve results in very dynamic environments.

We owe a debt of gratitude to John Holland, a pioneer in the field of Complex Adaptive Systems theory, for validating our intuitions about why and how this happens, and what it takes to adapt as quickly as what’s called for by the environments in which we operate. (We recommend his book, A Hidden Order, for its description of how this process works.)

You can see us describe Holland’s ideas and link them to Emergent Learning and its principles and tools in this video from the STIA conference.

Our wish for you is that 2014 brings you many wonderful opportunities to learn something new and empowering!

-Marilyn, Heidi and Jillaine

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Why I Love Hypotheses

If you work with us for more than 10 minutes, you know that we are big fans of action hypotheses – those “if/then” statements about what we expect to happen if an action is taken.

I see them everywhere. Or, to be more accurate, I see mostly half of a hypothesis everywhere. In a recent Boston Globe editorial (9/5/13), Harvard Economist Edward Glaeser observes that “Crime rates fall when there are more police officers. Boosting the number of cops in neighborhoods helps ensure that crime stops long before an arrest.” You may agree or disagree, but by putting out his argument so clearly, the reader is invited to do just that – to walk around in the idea and try it on for size.

But I also see hypotheses in strategies and action plans; in outcome statements and theories of change and logic models and “lessons learned.” And that’s where I see half-hypotheses most often:  “Ensure that all decisions are data-driven.” Sounds good, but why? “To be successful, initiatives must establish a high level of community engagement.” Successful in what way? “We need to strive for equality.” What would that look like and what is it going to take to get there?

Half-hypotheses like these can cause a lot of grief for people trying to achieve big, complex change goals in environments with lots of moving parts. People can think they agree about what “high community engagement” looks like or what it’s supposed to achieve, but really be working from very different playbooks. Half-hypotheses can shift the definition of “success” to be about completing a task, rather than achieving an outcome. Half-hypotheses can result in the over-institutionalization of “best practices” (e.g., data-driven decision-making). The “then” is simply assumed to be good in all situations. These assumed best practices can take lots of time to implement and can sometimes make it difficult to explore outside of the boundaries into creative territory.

In the world of action – where we do something because we expect a result, hypotheses are a fundamental building block of our thinking process. We couldn’t operate without them. You can argue about whether something is a mid-term outcome or a short-term result; an input or an output; a vision or a mission, a strategy or a tactic. But to us, it’s all hypotheses all the way down. If you look at the world that way, then learning how to use hypotheses well is a very simple and elegant way to improve your ability to think strategically and take effective action – especially when it involves working as a team with other people or organizations.

So the next time you hear a “we must…” or “we need to…,” ask what that will help us accomplish (to get at the “then” part of the hypothesis). If you hear people asking you to get behind a big, audacious goal, ask what it will take for us to get there (to get at the “if”).

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Accountability for What? Results Produced or Producing Results?

People solving social problems in innovative ways naturally want to know that their time and grantmaking dollars are making a difference and that their efforts moving forward will produce an even better payoff. Hence the investment in evaluation. But social innovation and evaluation can be uncomfortable bedfellows. Traditional evaluation fails social innovation on three counts:

  1. It evaluates results against outcomes and indicators established at the beginning of the initiative. But the pace inherent in innovation requires that people adjust their thinking as the work unfolds. When this happens, the initiative and the evaluation can diverge, making activities related to the evaluation feel out of sync and constraining.
  2. Because evaluation reports are typically written for audiences external to/separate from the actual work, it insists on a high level of fidelity of data. As a result, such reporting too often excludes important but non-linear, non-triangulated outlier results that the people doing the work need to consider as they adjust and improve. Reports then risk being “white washed” and too generalized to be of much value.
  3. Finally, the traditional cycle of annual or bi-annual reporting is too slow to inform the dynamic environments in which innovative change agents operate. Reports come too late to help track and test thinking at defining moments when it would be most useful.

What’s the alternative? We and others have been working on this problem. The field of Developmental Evaluation is devoted to re-thinking evaluation in complex and dynamic social change initiatives. But we at 4QP have been thinking about this question from a completely different context that we believe has some lessons for the field.

Several years ago, 4QP partner Marilyn and her former colleague Charles Parry had the opportunity to study an urban police department’s adoption of New York’s CompStat model. CompStat relies on very simple trend data regarding a bucket of crimes –burglaries, car thefts, aggravated assaults. Every three months, each district leader would discuss their district’s trend data with peers and the commissioner. If burglaries were up, they were expected to do their best to understand why and talk about what they planned to do to address the problem. They knew that, a few meetings later, they’d be in front of their peers again and they wanted to be able to demonstrate that their thinking and actions succeeded in improving the trend line.

Meanwhile, their peers were free, when the need arose, to “steal” and refine these innovations to improve trend data in their own districts. This impressive, self-reinforcing platform for learning as an institution made room for humility and curiosity, even in the face of accountability and competition. (An unexpected result included requests by beat cops for better data and analysis tools.)

This story illustrates how people on the ground can strengthen their capacity to produce results by reflecting deliberately on very simple and frequent data reporting, which both stimulates and captures outlier innovations. It helps them strengthen their thinking and, therefore, their capacity to produce results in the future, even as their environments change.

Our big takeaway? Evaluation of social innovation should focus not just on accountability for results, but for surfacing and testing the thinking that produced results. It is that capacity to think through how to achieve outcomes in complex and dynamic situations that will ensure greater payoff in the future.

This fast-cycle learning is what we aim to support with Emergent Learning. It is not easy. But when everyone around you is doing this kind of quick, fit-for-purpose reflection on results, innovation starts to become “just how we do our work here.”

 

4QP and Tanya Beer of the Center for Evaluation Innovation will be co-facilitating a discussion of this topic at this fall’s American Evaluation Association conference. Please join us.

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Getting Lessons “Right” vs. Getting Lessons Used

I haven’t been to the JFK Presidential Library in Boston in a long time. I was inspired to make a visit recently to see a special exhibit, “To the Brink: JFK and the Cuban Missile Crisis.” I’ve always been intrigued by what the key players were thinking during one of the closest calls in our nation’s history.

I had read a Boston Globe article this fall about the Cuban Missile Crisis that had me thinking: Is it more important to make sure that the lessons we learn are the “right” lessons, or is it more important that we put to use whatever lessons we have been able to gather?

In this Boston Globe article (10/21/12), Jordan Michael Smith described how JFK formulated his response to the Cuban Missile Crisis by recalling, and insisting that all of his Navy officers read, The Guns of August. The book described how WWI came to pass even though no one wanted a war. “Every country on the continent miscalculated, underestimating the economic and military costs of a potential war, the likelihood of one breaking out, the possibility of a single event spiraling out of control, and their opponents’ willingness to fight.”

Kennedy used that lesson to reign in his Joint Chiefs of Staff who recommended a full scale attack and invasion of Cuba. History tells us that it was the right decision. But it turns out that that lesson was wrong. Subsequent research has shown that Germany did, indeed, want the war to happen. As Smith observes, “past events are so complex and so specific to their contexts that they don’t necessarily yield a single correct lesson.” He goes on to draw the conclusion that “the value of history to leaders depends more on who applies it than on how well they really grasp the past.”

This story reinforces at least a couple of important principles we hold dear in Emergent Learning:

  1. It’s hard to learn good lessons post-facto from big, complex events or pieces of work. Everyone comes to their own conclusions, based on their perspective and biases.
  2. Lessons get learned when they get used, not when they get written down.

Our clients often ask “How important is it to conduct an After Action Review immediately, while the experience is fresh?” The obvious answer is “the sooner the better.” But our answer is more nuanced: If you will only give yourselves an hour or two to engage in learning from the past to improve future performance, we would prefer that you spend that time reflecting just before the next piece of action than after the last one – especially if there will be a long time gap in between.

We have clients who tell us about running what seems like the same post-mortem conversation with teams year after year. They bemoan the fact that their organizations don’t seem to be able to break through and learn the lessons that have been so clearly identified.

Ultimately, more and better learning happens by applying what may be sketchy recollections of past events than by compiling a really complete and accurate analysis and report that sits on the shelf and never gets used. Even if the lesson is wrong or the story is off the mark, as Smith suggests about The Guns of August, it leads you to ask a question you may not have thought of otherwise and to see and consider an idea that may not have otherwise been on the table.

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