In naming the Emergent Learning principle of Maximizing Freedom to Experiment, we were addressing both the how – what it takes to make the space to experiment and to learn from it, and the why – to unleash the wisdom and resourcefulness that will help our teams and organizations creatively respond to our important opportunities and challenges. But, also, to respond to the reticence and attendant constraints that come from our collective concerns about taking risks.

We don’t want to gloss over this moment. The whole sector is in shock. We’ve never been here before. Whether our organizations are choosing to take big, bold actions or just trying to test the waters, we want to make a pitch for the importance of conducting small experiments around how our work is changing, and learning from the results as quickly as possible. Rinse and repeat; learn and adapt. Being deliberate about experimenting where we can and learning as much as possible as quickly as possible is hugely important to mitigating risk in a volatile environment.

In any big strategy or action we take as an institution, there are many small steps that need to work in order to succeed in our larger goal. Each piece of a larger strategy might be owned by a different individual or team. During the pandemic, funders deployed rapid response grants to their grantees to help them respond to rapidly changing community needs. To deploy those funds, organizations needed to shift their existing priorities and expectations. They needed to quickly design and implement new, simpler application processes. Funding decisions needed to be made quickly and, in many cases, traditional reporting needed to adapt or be scrapped entirely. 

We talk about these small steps as the building blocks of our work. Each building block may be something we are already very skilled at, but maybe some are completely new, or are things we know how to do when the environment is stable, but need to be tweaked to match the times. Where could we stumble? If we can anticipate those important but small building blocks and deliberately experiment and learn quickly, the chances of our overall success increase and our risk falls.

The value of deliberately experimenting around crucial building blocks goes beyond mitigating risk in this particular situation. If we can draw on what we’ve learned in the past about a particular building block and then use what we learn in this moment going forward – in other words, if we can steward our learning over time, we can build our collective skill and increase the likelihood that we will succeed in the future. 

In chapter 4 on experimentation in the Guide to the Principles of Emergent Learning, Lauren Gase talks about “bounding the solution space,” which helps us be deliberate about what building block(s) we are focusing on. EL practitioners know a lot about how to turn the work into an experiment using Framing Questions, Hypotheses, BARs and AARs. Lauren offers ideas about how to bound an experiment to learn as much as possible from it. She observes that, “without boundaries, diverse experiments working to pull different levers across a giant system may be too diffuse to foster change or support learning.” If the framing question is more bounded, she argues, it can create “a shared ‘design space’ that foster[s] greater learning.”

The language we use when we talk about “freedom to experiment” is also important. Communicating clearly that we are inviting partners (team members, grantees, intermediaries) to bring their best thinking to the moment in this particular piece of work creates more agency and empowers partners to experiment together.

“Here’s what we are trying to achieve (line of sight) and here’s the space where we need everyone’s best thinking. Try something! Help us learn!” A corollary, of course, is to be open to hearing the results and being willing to adjust. Without that, it’s not an experiment!

These are the times we are in. Being deliberate about experimenting and adjusting in small ways along the path can help manage the risks inherent in responding to this moment.

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This year, in particular, will call on us to bring our whole selves to the landscapes we find ourselves in.  Regardless of your own sense of pessimism or optimism heading into January, we are stepping into “interesting times.” Around the world, people are facing so many changes in our political, economic, social and environmental landscapes. Each one could have a dramatic impact on its own, but they will also interact with each other – they will “reverberate,” as EL community member Marian Urquilla observed (see below). Some of the coming changes are predictable and some are very much not predictable. And some very predictable changes will have very unintended and unpredictable consequences. In the midst of everything else, for example, what if we find ourselves facing a new pandemic? Interesting times, to be sure.

With so much at stake, there can be a tendency to want to hunker down and circle the wagons. But that’s just one hypothesis. It is for times like this that Emergent Learning was created. In this time in particular, we need to really flex our EL muscles and ask: What will it take to bring together all of our wisdom and resourcefulness to find new (or renewed) pathways to our goals in the uncertain times ahead? 

We need to return our learning to our systems. We have a rich body of recent data to draw on. First, there was the change in priorities represented in the administration change of 2016. Many of us working in the social sector pivoted our strategies in 2016. What hypotheses did we try out and what did we learn? 2025 will be different, but it would be a mistake to not draw on what we learned from that time.

Second, literally everyone reading this has been through the completely unplanned and unpredictable events of 2020. In March 2020, without warning, our world changed dramatically due to the pandemic, followed two months later by the murder of George Floyd. In 2020, we were forced to pivot not just our strategies but everything. All of our long-held assumptions about what we do and how we do it got upended. It was painful, but it also sparked innovation. We surprised ourselves. In 2020, necessity became the mother of a lot of things. 

This is a time to call on our collective wisdom and remember how resourceful we know we can be in the face of the unknown. If we pivoted to prioritize state and local strategies in 2016, what is there to learn from that strategy? What can we learn from the many pivots we made in 2020? We need to re-run the movie (the game film, if you are a sports fan) of these two pivotal times and learn from them. What were the defining moments and how did we react to them? If we could turn the clock back, what would we have done the same? What would we have done differently? Who did we admire for their response and what can we learn from them?

We need to strengthen our line of sight. This is not a time to allow our goals to erode. What we learned during 2016 and 2020 was to stay true to our goals, but to challenge our hypotheses about what it will take to get there in changing conditions. Even if we don’t achieve what we hope for in the next few years, being deliberate about holding our vision and experimenting with how to get there will prepare us to succeed in the years that follow.

To help her clients respond to the events of 2020, Marian Urquilla created a hugely popular Strategy Triage Tool, which she has updated for 2025. This tool helps teams think carefully about each thing they are doing – to challenge their assumptions; to recommit or let go; to think about how both their hypothesis and who they work with will need to change. The goal of the tool was then and is now to “create the space needed to assess, adapt, and experiment forward. It’s a way of staying grounded in purpose while remaining flexible enough to meet the moment.” We encourage you to read about and consider using this tool to help navigate the times ahead.

We need to not be afraid of asking powerful questions; to challenge our deepest assumptions. In January of 2020, who would have thought to ask a crazy question like: What would it take to do our work if none of us could come to the office tomorrow? What are the questions we need to start asking ourselves now to be prepared for the months ahead?

We need to maximize our freedom to experiment. For years, in order to stimulate innovation, organizations have created bounded spaces – innovation centers, skunkworks, special projects, under the valid assumption that rigid organizational cultures and structures would squelch creativity. But we learned from the pandemic how to do this together outside of those protected spaces. What will it take to remain agile and adaptive today in everything we do?

In 2025, especially, we need to invite diverse voices to our tables and recognize the many different kinds of expertise available to us. In challenging times, we sometimes revert to early learned behaviors and listen only to the voices we know and trust. If we want to discover new pathways to the goals we hold dear, we will need to listen and learn and be willing to adjust our own deeply held assumptions. Beyond that, to actually achieve our goals in 2025, we will need to engage the hearts and hands of a lot of people who bring with them different values and perspectives and experiences from our own.

We got this. No, we don’t know what it will take to keep moving toward our north star goals in the face of the radical changes to come in 2025. We will get it wrong before we get it right. But we got this. We know how to learn from our experience; to keep our line of sight; to invite the hearts and hands of many people to our tables; to maximize our ability to experiment; to notice our results; to not be deterred by failure, but to learn from it and adjust, rather than hunkering down and circling the wagons.

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Emergent Learning is meant to be generative—to unleash our agency and creativity in the face of endlessly different and changing contexts. Each time we draw on the principles and practices of EL, something emerges; something that makes sense to us, given where we have been and where we are going. It’s the gift that keeps on giving.

What makes that possible? My own hypothesis about this is simplicity. The principles and practices of Emergent Learning are not complicated. They are designed to be shared. They naturally make sense to people and can be applied to many different contexts. And yet, I can say from a lot of experience, they continue to unfold in endlessly interesting ways. The art, as we always say, is in how and when and why you use them, and what you learn along the path.

I was reminded of this recently as I was reading one of a now endless number of articles about generative AI. There are some interesting parallels between Emergent Learning and generative AI. The idea of ‘rubbing stories together’ in EL is about expanding our data set as we develop hypotheses. And learning in fast iterations by being deliberate about identifying opportunities to experiment and noticing our results speeds the rate at which we learn and adapt. The notable difference between EL and AI (aside from massive computing power) is the layers of meaning we can bring to this generative process with our human history, vision and moral compass.

There is much more to say about all of that, but for now the common thread I want to focus on—simplicity—is described beautifully in an article I read in the Boston Globe about how the design of AI has evolved. One product that was created using earlier, less generative, AI models was ITA Software, which pioneered technology that lets people compare airline flights and prices online. The founder of that company, Jeremy Wertheimer, described in an interview the difference between how he built ITA and how generative AI is being built these days: 

“The initial AI program behind ITA was about 2 million lines of code. But Wertheimer said that if he were starting the company today, the entire program to sift and compare thousands of flights per day could run in just 100 lines of code — or maybe even 10.

“‘Back in the day, we called it good old-fashioned AI,’ Wertheimer, who is now a visiting scientist at the Broad Institute, said in an interview. ‘But the future is to forget all that clever coding. You want to have an incredibly simple program with enough data and enough computing power.’¹

One of the most fundamental ideas of emergence is how simple rules generate complex, contextually adaptive patterns. Think about the power of language. In English, we use 26 letters, a handful of punctuation points, and a few grammatical and semantic rules to create an infinite number of sentences that are instantly meaningful to other English speakers, even if they have never encountered that sentence before. Because we learned this set of rules as children, they become an unconscious competence that allows us to be endlessly generative in our communication with each other.

Yet the English language has evolved over time. People make language work for them in their sub-cultures. But what makes that possible is that it continues to draw on the basic letters and rules of grammar and syntax – even if they sometimes deliberately bend or subvert them. So too with Emergent Learning. In fact, while it may not look like it to newcomers, how Emergent Learning gets expressed has been evolving all along, informed by rubbing our stories of practice and experimentation together. The more we rub our diverse stories together—something we do regularly in our community calls—the more EL and how we talk about it evolves. A beautiful example of this is our just published Guide to the Principles of Emergent Learning, which includes many stories from across the EL Community. 

But if, in the process of evolving, Emergent Learning becomes complicated and stops making natural sense, it would make it harder to understand, harder to share, and harder to apply in a fit-for-purpose and just-in-time way without external assistance. We could, for example, decide there should be, say, 10 questions in an After Action Review—just enough to have to decide if we want to invest the time to do an AAR or to feel that we need someone to help facilitate it. If changes like that happen, the conditions for generativity that EL creates will begin to erode.

Early in his seminal book, The Fifth Discipline, Peter Senge wrote: “This, then, is the basic meaning of a ‘learning organization’—an organization that is continually expanding its capacity to create its future.”² He used the rest of the book to introduce and describe the five disciplines and a number of related practices—systems thinking, personal mastery, mental models, shared vision, and team learning. From an EL perspective, that book represented Peter Senge’s hypothesis about what it takes to create a learning organization. 

In the years that followed, I observed many organizations measuring their progress towards being a learning organization in a technical way—by their mastery of the practices related to those five disciplines, often at significant cost and with the assistance of many consultants or researchers. It felt unnecessarily complicated to me. I have always argued and still believe that we would be further along if the disciples of organizational learning measured themselves against that original, simple, intuitive definition: In what ways are we expanding our capacity to create the future to which we aspire? What else will it take? And then measure the usefulness of the practices they employ against their ability to help achieve that more fundamental intention.

Likewise, Emergent Learning is not about the practices—Before and After Action Reviews (BAR/AAR), EL Tables, Learning Logs—as much as it is about what they make possible. Though the practices can be used in technical ways, EL is not intended for that purpose. The intent of Emergent Learning is to learn as well together around the things we care about as we are able to learn as individuals. What will it take to do that?

If we always come back to that intent and test the ideas we are experimenting with against it, we should feel free to improvise. We don’t have to measure our success—nor should we—by our ability to facilitate EL Table conversations. There are lots of ways to create powerful hypotheses; to establish line of sight. There is a lot of great work being done to bring diverse voices to the table and to treat all expertise, including lived experience, with the respect it deserves.

How can we make our practice of EL as simple as possible, so that it continues to make intuitive sense and to create the conditions for generative ideas to arise, so that we can achieve the goals to which we collectively aspire? 

We’d love to hear what other members of the EL community think about what it takes to make (and keep) Emergent Learning emergent as it evolves.

1. Pressman, A. (2023, August 10). This tech founder built one of Boston’s biggest AI companies. But he says the West Coast approach is the future. Boston Globe. https://www.bostonglobe.com/2023/08/10/business/this-tech-founder-built-one-bostons-biggest-ai-companies-he-says-west-coast-approach-is-future/?et_rid=1582233. ︎
2. Senge, Peter. (1990) The Fifth Discipline: The art and practice of the learning organization. New York: Doubleday/Currency, P. 14. ︎

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What is the product of Emergent Learning? Many of the organizations that are beginning to adopt Emergent Learning (EL) are accustomed to thinking about products as concrete results that, ideally, can be measured. Our EL practitioners can find themselves struggling to make the roundness of EL fit into that square hole.

The product of Emergent Learning is not something that can be captured on paper or measured. The product of EL is about the learning that happens — the potential that gets created — along the path toward achieving concrete results; in the interstitial spaces that aren’t captured in a plan, a logic model, or a strategic framework. What do we need to learn in our complex environments to keep moving toward our north star? What do we do if we hit a roadblock? Are there other pathways we could take to adjust? That’s where learning happens. It is focusing on and expanding that in-between space — that emergent potential — that makes it possible for a group of partners working in a complex environment to keep moving toward their shared goals, regardless of what gets thrown at them.

In a call with our funders, we were asking how they would think about “impact” for a project like the EL Community. Deborah Bae from Robert Wood Johnson Foundation observed that it’s not the volume of change so much as it is the adaptive potential that each action creates — a lovely definition of emergence. The most useful steps on the path create the greatest potential; the largest number of options for next steps — options that could potentially shift the work in ways we never could have imagined from the start. 

One of the things we are discovering as we continue to study the impact of Emergent Learning is that, as practitioners, we need to notice the significance of our many small, often unpredicted, wins — to see them as seeds to be nurtured. As we at 4QP help practitioners write their stories of EL “micro-moves” and what happened as a result, we are aware of just how significant and full of potential some of these small wins can be — how many possible options they open up. There’s a sort of meta awareness of the significance of these wins that we as practitioners develop as we become more advanced in our practice. Gregory Bateson would say that we are learning to learn. And if and when we can get to the point of helping those around us see and understand the significance of these moments of breakthrough, learning accelerates even more. As my old Army buddy, COL Joe, would say when everyone up and down his chain of command was doing AARs, “learning goes vertical.”

Focusing on these interstitial spaces is not something we are trained to do. We have to unlearn some deeply rooted mental models about where to focus if we want to learn how to unleash the potential contained within these spaces. It’s been a journey for the EL Faculty to learn how to help others (and ourselves) unlearn these long-held mental models. Some of our practitioners will remember that we used to begin our year-long EL program by having people write one big framing question about their work. We’d workshop it and refine it in that first session. Then people would go home with the goal of practicing BARs and AARs and EL Tables. When we came back six months later for the second session, we’d sometimes hear: “I didn’t practice much because I couldn’t get the right framing question.”

The product of Emergent Learning is not about writing the perfect framing question or hypothesis or learning agenda. It’s great to have the skill to do these things, but they are a means to an end. Now, my favorite session in the EL Intensive is session 2, where we practice asking questions in a hallway conversation that starts with, “So what are you working on these days?” and helps people learn to ask EL questions in a natural way and to listen for hypotheses — all in order to help our colleagues make their thinking visible in the small but important spaces that exist in our everyday conversations; spaces where we have the opportunity to expand our thinking and our options for walking the path of complex social change.

That the focus of Emergent Learning is on the in-between spaces can be challenging in our organizations that are designed to focus on products and metrics, but it is also a benefit. It is exactly because Emergent Learning focuses on the interstitial spaces more than on what can get captured on paper that it ‘plays well with others.’ Almost every framework or approach used in the social sector — from Collective Impact to Equitable Evaluation to Systems Thinking to Trust-Based Philanthropy — has spaces in between the boxes that call, implicitly or explicitly, for learning. But these frameworks generally don’t map out what that should look like. That’s where Emergent Learning fits in — to help partners learn their way through the framework. What does centering on racial equity really look like in our everyday decision-making? What will it take for us to come to, and maintain, a common agenda? What will it really take for us to be transparent and responsive with our partners? How will we recognize if we are succeeding? Emergent Learning helps partners keep their goals as their north star and experiment along the path to adapt the framework to fit their own ever-evolving situations.

And because some of the most powerful EL micro-moves are just asking the right question when a practitioner notices that a group is unclear about its line of sight or not making its thinking visible, framework-weary groups do not need to ‘sign on’ to yet another framework in order to benefit from EL.

In her case for certification, Brittney Gaspari from The Winston-Salem Foundation described how she was using Emergent Learning to support the foundation’s work in Trust-Based Philanthropy. Brittney interviewed grantees to hear their experience. One grantee observed: “When you have the capacity to experiment and grow, you’re going to reach your goals that much faster.”

That’s the product of Emergent Learning.

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Sometimes the biggest challenge we face in getting our organizations to invest in learning is finding a source of motivation that’s great enough to overcome the inertia of the status quo. And sometimes that source ends up being something that is seen as “a failure,” and our motivating urge is to “make sure we never do that again.” Over the last few months, we’ve had several conversations with members of the Emergent Learning community who are trying to figure out how to approach learning in these potent and challenging conditions that follow project closures or failures. The urge to “make sure we never do that again” can motivate learning, but can lead to learning the wrong lesson or over-learning. In a fast-changing environment, the capacity to learn lessons is more valuable than any individual lesson learned, and avoiding yesterday’s mistakes won’t always prepare us to tackle tomorrow’s challenges. So how should we think about engaging in learning at the end of a project or program, especially one that is deemed to be a “failure”?

When we talk about what it really means to be doing Emergent Learning well, the specific tools we use are less important than when and how we use them. The purpose of Emergent Learning is to learn in real time, during a project, so that we can apply what we learn to this work the next time we have a decision to make — so that when we do get around to doing that post-project reflection, the news is good! In contrast, the learning tools organizations most commonly turn to at the end of things are post-mortems and project retrospectives. The fact that these are a team’s “last step” tends to define the kind of learning they encourage more than anything else, but each has its own specific impacts on how teams learn — or don’t. 

The Post-Mortem: In the classic post-mortem, disaster has already struck. The patient is pronounced dead, and everyone weighs in on the mistakes that contributed to their demise. As the children of disasters, post-mortems set up mental models that can hinder learning. Accountability tends to be a search for “whoever was responsible for causing the failure,” rather than a recognition that we all make well-intended decisions for a range of reasons and we are all responsible for learning about what contributed to a failure. What we tend to focus on learning about is “what not to do” in the future. We sacrifice opportunities to explore what it would take to achieve our future goals in order to focus on how to avoid our old mistakes. 

From an Emergent Learning perspective, waiting until failure strikes to develop hypotheses denies us many useful opportunities to test those hypotheses in the thick of the work, when the potential for success is still on the table. There is a time and place for a post-mortem — if broad institutional changes are needed to mitigate human error and ensure that that kind of irreparable mistake is never made again, but the cost is risk-averse teams who are burdened by infrastructure (rules, checklists, and added procedures) and less able to experiment and learn.

The Project Retrospective: Regardless of a project’s success or failure, a retrospective aims to capture learning from the project once it is completed. This approach assumes that learning is like data collection, and that the best time to learn about a project is once the work is done and we know what the final score is. 

Unfortunately, the most significant impact of waiting until the end of a project to start learning is that the humans doing the learning have to rely on memory instead of observation. Important moments blur together, and details are sacrificed for big, abstract lessons and feelings. What gets remembered may also be influenced by who has the most power or authority. A team might agree that a project was incredibly successful, or enjoy griping together about the ways in which that one frustrating partner was, in fact, frustrating, but specific lessons that can inform and be tested by tomorrow’s work are harder to come by. Relying on a project retrospective to “take care of the learning” at the end of the project can be especially challenging in longer projects, as the amount of information to process and learn from can become overwhelming. Longer projects often see team members change roles or leave the organization, taking key insights with them. Finally, as with a post-mortem, waiting until the end prevents us from using what we learn to improve the outcomes of this project. 

The After Action Review: An iterative cycle of Before Action Reviews (BARs) and After Action Reviews (AARs) addresses the shortcomings of last-step learning by providing a simple way to embed real-time learning in the midst of the work at hand. There are always small failures inside big successes, and small successes inside big failures. These are often the source of the most important (and actionable) lessons teams can learn — if they can learn them while there is still time to experiment, and before those small lessons are blurred into the final outcome.

We use AARs to review our initial hypotheses about an important component of a project, check how they hold up against real-time observations (not memories), make modifications, and most importantly, identify the next step or important opportunity we’ll be able to test new or refined hypotheses against. By focusing on the goals that today’s work carries us towards, teams can maintain a clear line of sight through the thickest tangles of uncertainties, without stifling risk-taking or experimentation. By embedding learning in the midst of a project, AARs give us the opportunity to explore new opportunities, make course corrections, and learn from each of our decisions.

– – –

But what if, like many of our colleagues, the work you have in front of you today is to collect learning from a completed — or failed — project? Luckily, learning is like planting trees: the best time to start was definitely ten years ago, but the second-best time to start is right now. 

Starting at the End: Let’s say you’re at the end of a failed project, and there is appetite — or even just awareness of a need — for learning. How can you help your team avoid risk-averse perfectionism in response to failure, “learning” fuzzy or wrong lessons from too much old information, or wasting energy pointing fingers and avoiding blame?

1. First and always, focus forward. Remind everyone that we are doing this to think together about how to be more successful next time, not to assign blame. It is even better to know what “next time” is, so you can focus your reflections forward to what the next project is likely to involve.
2. Create a timeline of a long or completed project and identify the defining moments you want to reflect on — and focus on those that are most relevant to your next project. Thinking about a whole project all at once tends to lead to big, abstract lessons, pet peeves, or finger-pointing, so break it into smaller pieces.
3. Conduct a series of short AARs around each of those defining moments. This will help your team get very specific and generate practical, realistic lessons, with a clear sense of when those lessons will next be applicable. 
4. During each AAR, “turn back the clock” and dig into why someone made the decision they did that may have contributed to a negative result. There was probably a good reason for it, even if the reason was lack of time. Follow up by asking “if we could turn the clock back, what could we have done to achieve a different result, and what lesson should we take forward?”
5. Rehearse the meeting in advance with people in positional power, and discuss how to talk about what happened. Inviting those in power to take the lead in sharing what they did that contributed to a negative result can have a tremendous impact on the tone of the meeting, and the kind of reflection the team engages in. 
6. As always, set the stage with forward-focused goals, ground rules, and expectations. 

The objective of this approach is to recreate (to the best of our ability) the learning experience that several AARs throughout the project would have provided. This helps the team hone a broad lesson like “leadership is important” or “community engagement should happen earlier” into more practical lessons that connect to specific parts of their next project. Such lessons can be communicated to others in a script like this:

“The question we came together to talk about was [Framing Question]. We reflected on our experience so far. What we noticed that worked was [story of success]. What we learned from that is [insight] and what we plan to do going forward to take that lesson with us is [hypothesis]. What we noticed that didn’t work so well was [story of failure/disappointment]. What we learned from that is [insight] and what we plan to do going forward to take that lesson with us is [hypothesis]. We plan to apply those lessons in [upcoming opportunity/opportunities].” 

At the end of something — especially something disappointing — curiosity and humility help us look to the future, and they help us learn far more from failures than “what not to do”. 

— Sam Moody is a nonprofit consultant, photographer, and Emergent Learning practitioner based in Denver, Colorado. 

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What does it mean to serve in a governance role as a board member in the social sector during tumultuous times? What should you be listening for? Asking for?

We worked with a Colonel in the US Army — a cavalry officer — who had a good nose for hubris. He told us that when he had a soldier in his unit who was seen as a star, but “kept riding the horse he came in on,” he would make sure that soldier’s career got stalled. On the other hand, when he had a green recruit who demonstrated curiosity and humility; who was willing to try things and fall on his or her face, but get up and learn from them, he would fast track that person.

Being a star in the past — having a big win under your belt — is no guarantee of success in the future. It might just be a result of being in the right place at the right time with the right strategy. The big question is this: “What did you learn from that win and how will that inform what you do next?”

Especially in tumultuous times, foundations, nonprofits and their partners need to become skilled at honing their thinking by paying attention to their results — both their wins and their losses — and learning from them. That’s a far better guarantee of future success than having met a predetermined success metric. At a 2013 American Evaluation Association conference session co-hosted by 4QP and Tanya Beer on “Rethinking Accountability,” we made this proposal:

Accountability needs to shift from achieving predetermined results on a predetermined path to demonstrating the capacity to achieve results in dynamic environments. Evaluation needs to focus on both capturing results and surfacing the thinking that is producing them and how that thinking has evolved because…

…It is that quality of thinking through complex change that is most likely to generate similar results in the future.

The takeaway for board members: Governance requires knowing what to listen for and what to ask for.

As a board member, what should you listen for? First, whether an organization has a big success or a visibly disappointing result, it’s what the team says next that you should be paying attention to. We created a ‘script’ for people who had participated in an Emergent Learning conversation to use to relate what they learned to the people they report to. It goes like this: 

“The question we came together to talk about was [Framing Question]. We reflected on our experience so far. What we noticed that worked was [story of success]. What we learned from that is [insight] and what we plan to do going forward to take that lesson with us is [hypothesis]. What we noticed that didn’t work so well was [story of failure/disappointment]. What we learned from that is [insight] and what we plan to do going forward to take that lesson with us is [hypothesis]. We plan to apply those lessons in [upcoming opportunity/opportunities].” 

We would then coach these participants to go on to describe how they plan to test out those hypotheses and communicate their intention to come back and talk about what results they got and what else they learned.

When organizational leaders come to report to you in your board meetings, if they can express something like this about what they’ve learned from their work, it should give you confidence about their future success.

Smart investors invest in the curiosity and humility — the quality of thinking — of those they invest in. When senior staff come to you showcasing some great result — essentially saying, “trust us” based on past performance, or when your gut tells you that some underperforming initiative is getting sugar coated, that’s when the warning lights should go on.

What you ask next is also important. When things don’t seem to be going well or we aren’t given enough information to know one way or the other, it’s human nature to want to gain some control; to step in and micro-manage a situation. But as a board member of an organization trying to navigate choppy waters, micro-managing reduces the agency and flexibility of that organization to respond to the unknown. Adding burdensome reporting requirements may help you feel like you are doing your governance job, but may not do anything to help the organization learn and adjust. It might even be getting in the way.

Instead, train yourself to listen for that curiosity and humility that is a true indicator of a leadership team’s preparedness to tackle an unknowable future. Whether you are hearing about a success or a disappointment, ask: “What did you learn from those results? How will that inform what you do next?” 

As a board member, think like a smart investor. Because, in these times, the only real guarantee about the future is that it cannot be predicted three years or even one year in advance.

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We at Fourth Quadrant Partners (4QP) have spent a lot of time talking about the definition of “learning” in Emergent Learning — the disciplined attention to data and insights that emerge from our work and the deliberate application of these to improving future results, and the implication that the place where learning actually happens is in the work itself.

The term “Learning Agenda,” however, means something very different to many people in the social sector. Two examples:

• “A learning agenda includes: 1) a set of questions addressing critical knowledge gaps; 2) a set of associated activities to answer them; and 3) products aimed at disseminating findings and designed with usage and application in mind.” — USAID
• “Learning agendas are a set of prioritized research questions and activities that guide an agency’s evidence-building and decision-making practices.” — Urban Institute

We believe that these definitions of “learning” convey two assumptions that can reinforce existing power structures.

The first assumption: Each one starts by listing a set of questions. Who defines the questions that are most important is unstated, but in practice, the questions are commonly defined by the entity funding an initiative. We believe that this fundamental, implicit assumption needs to change: The funder should not be the only one with the agency to decide which questions are important.

That is not to say that a funder has no right to identify questions that matter to them. But it does mean that each set of actors in a system also needs to have the right — and we would say the responsibility — to identify the questions they need to learn about, at the moment in time when they are most relevant to their work.

The idea of any single entity creating an agenda of questions to answer is problematic in many ways. In the EL community, one challenge that gets raised over and over is this: There are just too many questions! This is a natural reflection of the complexity of the challenges we’ve set out to address. How do we avoid creating a laundry list of learning questions to answer? How do we choose the most important questions to focus on?

The very notion that any single group can identify a set of questions at any one point in time has become even more questionable in the face of COVID and the increased attention to racial injustice. A list of questions that seemed important six months ago now feels out-of-date. Yet we may still be accountable to senior staff to answer them. These two disrupting themes have fundamentally upended our thinking and feelings about which questions matter most and how and when to ask them. And, perhaps most importantly, who gets to choose.

The second assumption: By these definitions, the responsibility for learning has been exported either to the learning and evaluation staff or to an external resource. They are responsible for finding “the answer” (including making meaning of any data they gather in conclusions or recommendations) and handing it back to the organization to assist with decision-making or disseminating it to the field. There is no role in this process for the people doing the work to contribute to, or make meaning of, the answer. The fact is that in complex and dynamic work, there is no single answer. We can do all the research we want. But history tells us that unless and until we — the actors in a system — try to apply those findings and recommendations to our own messy, complex environments, full of human beings, they will remain answers-in-theory, not answers-in-practice.

Distinguishing between research, evaluation, and learning questions

Within the frame of Emergent Learning, we would say that these two definitions by USAID and Urban Institute describe a research agenda. To begin to shift our habits and understandings, we think that it is important for the social sector to learn to distinguish between learning questions, evaluation questions, and research questions and where to use which ones and why.

Broadly speaking, a research question is posed by or to a researcher and involves surveying and analyzing what exists outside of our own boundaries, typically to help inform the field’s future actions or our own decisions. Broadly speaking, an evaluation question is posed by or to an evaluator and involves collecting data on the results of a team’s activity to assess performance against expected outcomes and, often, to provide feedback along the path.

A learning question, on the other hand, is posed by and to ourselves — the actual people doing the work. A learning question asks us to look forward — to think about what we’re trying to accomplish, what we know so far, and what it’s going to take to achieve the outcomes we have set for ourselves. And then it asks us to test our thinking along the path, in order to improve our results over time. That’s what we mean by “learning.”

Example: Research, Evaluation and Learning Questions for a Leadership Development Initiative

This distinction should not be new to practitioners of Emergent Learning. But this other understanding of “learning” is so fundamentally woven into the way senior leaders, boards, program and evaluation staff think about learning that it’s going to require a very deliberate and visible effort to shift our shared mental models and habits.

To help address the overwhelming number of possible questions to ask; the question of who has the agency to choose the learning question and when; and who does the learning, we created the idea of a layered learning agenda. The notion of layering, or nesting, will sound familiar to EL practitioners — it is an important part of developing line of sight. What will it take to…? And what will it take to do that? And so on.

A sample layered Learning Agenda for a leadership development program

In this simple example of a leadership development initiative, the actors who own pieces of answering this larger question include foundation leadership, foundation program staff — and their work and the questions that matter to them are probably different; grantees who have responsibility to deliver leadership programs, and the fellows who are participating in those programs and who are expected to use what they learn to lead in their communities.

At each layer, the most profound overarching learning question is this: “What will it take to [achieve our part of the larger goal]?” For example, for grantees in this initiative, the overarching question might be simply: “ What will it take to build a network of leaders who are committed to achieving racial justice in their communities?” Beyond that, the questions they ask might change over time, driven by circumstances and by the opportunities in front of them — in this case for example revising the curriculum or welcoming a new cohort or, as is the case for all of us now, figuring out how to do all of this virtually. It keeps their own work at the center.

In practice, each layer of actors could create their own detailed learning agenda at their level that might include participants, indicators, potential data sources, reporting expectations, etc. And, in reality, some layers of learning may remain tacit, but mapping out the whole learning ecosystem can help those of us who are stewarding learning to know where to focus and what questions matter the most at any moment in time.

It also meets the needs of the funder to ask the questions that matter to them, and includes the possibility of asking parallel research or evaluation questions at each layer, without shifting the responsibility for learning away from the people doing the work itself.

Taken together, a layered learning agenda can help to create a learning ecosystem with a shared line of sight — actors at each level addressing the questions they are best able to address, in a way that rolls up to a larger body of knowledge. Senior leaders often ask learning staff to demonstrate how the learning they are doing at the program level rolls up. This is one way to help demonstrate that relationship.

We offer one caveat: In fact, layering a learning agenda like this makes the focus of the agenda much simpler at each level. But to a new audience that is unfamiliar with Emergent Learning, the larger framework may appear complex and labor-intensive, so take that into consideration if you choose to share this framework with your colleagues.

Regardless of what your learning agenda looks like, being thoughtful about who the actors are and finding the overarching learning question that is most relevant to them is consonant with the principles of Emergent Learning. It can create the kind of passionate commitment to learning that extends beyond the bounds of a funded initiative — the questions don’t go away when you leave the building. And authorizing and encouraging those actors to identify the questions most relevant to them is an incredibly more efficient way to figure out which questions to ask, when, and why.

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It has been said that there will be no peace until the person who has not been wronged is as indignant as the one who has. We have taken note of a new phenomenon that gives us hope and the courage to take the stand that this time shall  be different:  that of a passionate commitment to make this pain our own, to learn about and internalize what we are witnessing, so that this stops now. Once and for all. For good.  

It has also been observed that the only emotion in which true learning can take place is love, the allowing of another to arise as a legitimate other in our experience (Humberto Maturana, “The Biology of Love”). May this learning be taken so deeply to heart in every one of us, out of this most recent human tragedy arising from America’s persistent racism, that we commit individually and together to learn how to transform our fear of ‘the other’ into love for all. 

This won’t happen in the abstract. We can only transform how we see and think and act one step at a time, every day, as we make a conscious choice about our next interaction to intend something different; to do something different. This includes dialogue, understanding and appreciating the history and direct experience from which each of us acts; doing things together that promote our knowing each other. We need to actively educate ourselves and those close to us about the history and the conditions that have contributed to these continuing injustices to identify leverage points and actions we can take in our communities to right them. We are ready and willing to bring everything we have to that aim. 

We at 4QP are taking some specific actions to contribute to this future to which we aspire:

• In our continued commitment to diversify the field of Emergent Learning practitioners, we are  experimenting with new ways to build capacity in Emergent Learning that make it more accessible to a broader range of social change agents, and particularly now for those who are directly addressing this current social transformation.
• We are already in the process of building an “advanced practice” cohort that will explore the intersection of Emergent Learning and equity.
• We are launching a Community Resource Center to make available a wide range of materials at no charge that can be used to support shared thinking and learning around the questions that matter to communities most affected by the events around them.

And yes, renewing our commitment to conserving our democracy by voting as if our lives depended on it; and also by actively participating in the exercise of these principles at every level of our civic engagement, learning our way together into the ‘more perfect union’ to which our constitution aspires.

We invite you to join us in these endeavors.

— Heidi, Jillaine, and Marilyn

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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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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 ELCP 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.

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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