Regular readers will know that I’ve been thinking a lot about evaluation for many years now. I am not an evaluator, but almost every project I am involved in contains some element of evaluation. Sometimes this evaluation is well done, well thought through and effective and other times (the worst of times, more often than you think) the well thought through evaluation plan crumbles in the face of the HIPPO – the Highest Paid Person’s Opinion. So how do we really know what is going on?
When I stumbled across Michael Quinn Patton’s work in Developmental Evaluation, a whole bunch of new doors opened up to me. I was able to see the crude boundaries of traditional evaluation methods very clearly and was able to see that most of the work I do in the world – facilitating strategic conversations – was actually a core practice of developmental evaluation. Crudely put, traditional “merit and worth” evaluation methods work well when you have a knowable and ordered system where the actual execution can be evaluated against a set of ideal causes that lead to an ideal state. Did we build the bridge? Does it work according to the specifications of the project? Was it a good use of money? All of that can be evaluated summatively.
In the unordered systems where complexity and emergence is at play, summative evaluation cannot work at all. The problem with complex systems is that you cannot know what set of actions will lead to the result you need to get to, so evaluating efforts against an ideal state is impossible. Well, it’s POSSIBLE, but what happens is that the evaluator brings her judgements to the situation. Complex problems (or more precisely, emergent problems generated from complex systems) cannot be solved, per se. While it is possible to build a bridge, it is not possible to create a violence free society. Violent societies are emergent.
So that’s the back story. Last December I went to London to do a deep dive into how the Cynefin framework and Cognitive Edge’s work in general can inform a more sophisticated practice of developmental evaluation. After a few months of thinking about it and being in conversation with several Cognitive Edge practitioners including Ray MacNeil in Nova Scotia, I think that my problem is that that term “evaluation” can’t actually make the jump to understanding action in complex systems. Ray and I agreed that Quinn Patton’s work on Developmental Evaluation is a great departure point to inviting people to leave behind what they usually think of as evaluation and to enter into the capacities that are needed in complexity. These capacities include addressing problems obliquely rather than head on, making small safe to fail experiments, undertaking action to better understand the system rather than to effect a change, practicing true adaptive leadership which means practicing anticipatory awareness and not predictive planning, working with patterns and sense-making as you go rather than rules and accountabilities, and so on.
Last night a little twitter exchange between myself, Viv McWaters and Dave Snowden based on Dave’s recent post compelled me to explore this a bit further. What grabbed me was especially this line: “The minute we evaluate, assess, judge, interpret or whatever we start to reduce what we scan. The more we can hold open a description the more we scan, the more possibility of seeing novel solutions or interesting features.”
What is needed in this practice is monitoring. You need to monitor the system in all kinds of different ways and monitor yourself, because in a complex system you are part of it. Monitoring is a fine art, and requires us to pay attention to story, patterns, finely grained events and simple numbers that are used to measure things rather than to be targets. Monitoring temperatures helps us to understand climate change, but we don’t use temperatures as targets. Nor should we equate large scale climate change with fine grained indicators like temperature.
Action in complex systems is a never ending art of responding to the changing context. This requires us to be adopting more sophisticated monitoring tools and using individual and distributed cognition to make enough sense of things to move, all the while watching what happens when you do move. It is possible to understand retrospectively what you have done, and that is fine as long as you don’t confuse what you learn by doing that with the urge to turn it into a strategic plan going forward.
What role can “evaluation” have when your learning about the past cannot be applied to the future?
For technical problems in ordered systems, evaluation is of course important and correct. Expert judgement is required to build safe bridges, to fix broken water mains, to do the books, audit banks and get food to those who need it. But in complex systems – economies, families, communities and democracies, I’m beginning to think that we need to stop using the word evaluation and really start adopting new language like monitoring and sense-making.
Two weeks ago in our Leadership 2020 program I experimented with using a signification framework to harvest a World Cafe. We are beginning another cohort this week and so I had a chance to further refine the process and gather much more information.
We began the evening the same way, with a World Cafe aimed at exploring the shared context for the work that these folks are in. Our cohort is made up of about 2/3rds staff from community social services agencies and 1/3 staff from the Ministry of Children and Family Development. This time I used prepared post it notes for the sense making exercise, which you can see here:
Our process went like this:
- At Cafe tables of five for 20 minutes, discuss the question “What is a story of the future you are anticipating for this sector?”
- Second round, new tables, same question, 20 minutes
- About ten minutes of hearing some random insights from the group, and checking to see how those resonate.
- 2 minutes of silent reflection on the question of ‘What do you need to learn here that will help us all move forward?”
- Each participants took a pink and blue post it note. On the blue post it they wrote what they needed to learn that would be immediately applicable and on the red ones, learning that is needed to prepare for the future.
- Participants filled out the post-its and then were instructed on how to signify the data on a triangle framework that helped them signify whether what they needed to learn would help them “in their personal life,” “do their jobs” and/or “make change.”
- Participants also indicated on the post-its whether the worked for the Ministry or worked for a community organization.
At the conclusion of the exercise we had a tremendous amount of information to draw from. Our immediate use was to take a small group and use affinity grouping to identify the themes that the whole has around their learning and curiosity. We have used these themes to structure a collective story harvest exercise this morning.
But there is some much more richness that can come from this model. Here are some of the ways people are playing with the date:
- Removing all the pink post-its to see what the immediate learning needs are and vice versa.
- Looking at and comparing the learning needs between the two sectors to see where the overlaps and differences are
- examining the clusters at the extremes to see what ot tells us about personal needs, and professional needs.
- Uncovering a theory of change by looking at the post its clustered around the “Making change” point and also seeing if these theories of change are different between the community and the government.
And of course because the data has been signified on each post it, we can recreate the framework easily. The next level for me will be using this data to create a Cynefin framework using the four-points contextualization exercise. Probably won’t happen in this cohort.
Big learning is the rich amount of data that proceeds from collecting finely-grained objects, allowing for disintermediated sense-making, and seeing all these multiple ways in which signified data can be used to address complex challenges obliquely, which allows you to get out of the pattern entrainment that blinds you to the weak signals and emergent patterns that are needed to develop emergent practice. This pen and paper version is powerful on its own. You can imagine how working with SenseMaker across multiple signification frameworks can produce patterns and results that are many magnitudes richer.
Two Tim Merry references in a row. Yesterday Tim posted a video blog on planning vs. preparation. It is a useful and crude distinction about how to get ready for action in the complicated vs. complex domains of the Cynefin framework. I left a comment there about a sports metaphor that occurred to me when Tony Quinlan was teaching us about the differences between predictive anticipation (used in the complicated domain) and anticipatory awareness (used in the complex domain).
In fact this has been the theme of several conversations today. Complicated problems require Tim’s planning idea: technical skills and expertise, recipes and procedures and models of forecasting and backcasting using reliable data and information. Complex problems require what Dave Snowden has named an artisian approach which is characterized by anticipatory awareness, theory and practice (praxis) and methods of what they call “side casting” which is simply treating the problem obliquely and not head on.
When I was listening to Tony teach this last month, I thought that this distinction can be crudely illustrated with the difference between playing golf and playing football (proper football, mind. The kind where you actually use your feet.) In golf there is a defined objective and reasonably knowable context, where you can measure the distance to the hole, know your own ability with golf clubs, take weather conditions into account and plan a strategic line of attack that will get you there in the fewest strokes possible.
In football it’s completly different. The goal is the goal, or more precisely to score more goals than your opponent, but getting there requires you to have all kinds of awareness. More often than not, your best strategy might be to play the ball backwards. It may be wise to move the ball to the goal in AS MANY passes as possible, in a terribly inefficient way because doing so denies your opponent time on the ball. And the context for action is constantly changing and impossible to fully understand. And the context also adjusts as you begin to get entrained in patterns. If you stick to a long ball game, the defending team can adjust, predict your next move and foil the strategy. You have to evolve or be owned.
This is, I believe, what drives many Americans crazy about world football. There is rarely a direct path to goal and teams can go for whole games simply holding on to the ball and then make one or two key finishing moves. Some call that boring, and it is, if you are in a culture that is about achieving the goal as quickly as possible and moving on. And God knows we are in a culture that loves exactly that.
You plan golf holes by pre-selecting the clubs you will use in each shot and making small adjustments as you go. In football you prepare by doing drills that improve your anticipatory awareness, help you operate in space and become more and more physically fit, so that you have more physical options. You become resilient. Yes you can scout an opponent and plan a strategy and a tactic, but football is won on the pitch and not in the strategy room. Golf is very often won in the strategy room, as long as your execution is masterful.
It’s a crude distinction and one has to be mindful all the time of downright folly of “this vs, that”, but sometimes these kinds of distinctions are useful to illustrate a point.
As I have been diving into the worlds of complexity and especially the question of evaluation in the complex domain, one of the people on my list to meet was Dr. Brenda Zimmerman, who taught at York University. News finally came through today that she died in a car accident on December 16. Her work is summed up in this notice from the Plexus Institute:
Dr. Zimmerman is co-author of several books, including Getting to Maybe: How the World Is Changed, which she wrote with Frances Westley and Michael Quinn Patton, and Edgeware: Insights from complexity Science for HealthCare Leaders, which she wrote with Paul Plsek and Curt Lindberg. She wrote the report “Complicated and Complex Systems: What would Successful Reform Medicare Look Like?” with Sholom Glouberman, published in 2002 by the Commission on the Future of Health Care in Canada. She also wrote numerous book chapters, including “Generative Relationships: STAR,” with Bryan Hayday, in Glenda Eoyang’s book Voices from the Field, and she authored two chapters in the book On the Edge: Nursing in the Age of Complexity, by Curt Lindberg, Sue Nash and Claire Lindberg.
Curt Lindberg, a founder and former president of Plexus Institute and another of Dr. Zimmerman’s co-authors, called her death a tragic loss. Henri Lipmanowicz, a founder of Plexus and its Board Chairman Emeritus, said, “We were lucky to have known her. She was one of a kind. A beautiful and caring person.”
Michael Quinn Patton is an organizational development and evaluation consultant, and complexity scholar. “Without Brenda there would have been no Getting to Maybe book and no subsequent Developmental Evaluation book,” Patton said of the book he co-authored with Dr. Zimmerman and the pioneering book on evaluation he later wrote. He noted that Chapter 4 in Developmental Evaluation tells something of Dr. Zimmerman’s influence on his own work and on the evaluation field generally.
via Brenda Zimmerman: Complexity Scholar and Mentor to Many – Plexus Institute.
A huge loss to her family and friends of course, and to the field in general.
Hmmm…maybe I should have let the group do this…
When I popped off to London last week to take a deep dive into Cognitive Edge’s work with complexity, one of the questions I held was about working with evaluation in the complex domain.
The context for this question stems from a couple of realities. First, evaluation of social programs, social innovation and other interventions in the human services is a huge industry and it holds great sway. And it is dominated by a world view of linear rationalism that says that we can learn something by determining whether or not you achieved the goals that you set out to achieve. Second, evaluation is an incredibly privileged part of many projects and initiatives and itself becomes a strange attractor for project planning and funding approval. In order for funders to show others that their funding is making a difference, they need a “merit and worth” evaluation of their funds. The only way to do that is to gauge progress against expected results. And no non-profit in its right mind will say “we failed to achieve the goals we set out to address” even though everyone knows that “creating safe communities” for example is an aspiration out of the control of any social institution and is subject to global economic trends as much as it is subject to discrete interventions undertaken by specific projects. The fact that folks working in human services are working in a complex domain means that we can all engage in a conspiracy of false causality in order to keep the money flowing (an observation Van Jones inspired in me a while ago.) Lots of folks are making change, because they know intuitively how to do this, but they way we learn about that change is so tied to an inappropriate knowledge system, that I’m not convinced we have much of an idea what works and what doesn’t. And I’m not talking about articulating “best practices.”
The evaluation methods that are used are great in the complicated domain, where causes and effects are easy to determine and where understanding critical pathways to solutions can have a positive influence on process. in other words, where you have replicable results, linear, summative evaluation works great. Where you have a system that is complex, where there are many dynamics working at many different scales to produce the problems you are facing, an entirely different way of knowing is needed. As Dave Snowden says, there is an intimate connection between ontology, epistemology and phenomenology. In plain terms, the kind of system we are in is connected to the ways of knowing about it and the ways of interpreting that knowledge.
I’m going to make this overly simplistic: If you are working with a machine, or a mechanistic process, that unfolds along a linear trajectory, than mechanistic knowledge (problems solving) and interpretive stratgies are fantastic. For complex systems, we need knowledge that is produced FROM the system and interpreted within the system. Evaluation that is done by people “outside” of the system and that reports finding filtered through “expert” or “disinterested” lenses is not useful for a system to understand itself.
Going into the Cynefin course I was interested to learn about how developmental evaluation fit into the complex domain. What I learned was the term “disintermediated sensemaking” which is actually the radical shift I was looking for. Here is an example of what it looks like in leadership practice.
Most evaluation uses processes employing a specialized evaluator undertaking the work. The problem with this is that it places a person between the data and experience and the use of the knowledge. And it also increases the time between an experience and the meaning making of that experience, which can be a fatal lag with strategy in emergent systems. The answer to this problem is to let people in the system have direct experience of the data, and make sense of it themselves.
There are many many ways to do this, depending on what you are doing. For example:
- When clustering ideas, have the group do it. When only a few people come forward, let them start and then break them up and let others continue. Avoid premature convergence.
- When people are creating data, let them tag what it means, for example, in the decision making process we used last weekend, participants tagged their thoughts with numbers, and tagged their numbers with thoughts, which meant that they ordered their own data.
- Produce knowledge at a scale you can do something about. A system needs to be able to produce knowledge at a scale that is usable, and only the system can determine this scale. I see many strategic plans for organizations that state things like “In order to create safe communities for children we must create a system of safe and nurturing foster homes.” The job of creating safe foster homes falls into the scope of the plan, but tying that to any bigger dynamics gets us into the problem of trying to focus our work on making an impact we have no ability to influence.
- Be really clear about the data you want people to produce and have a strategy for how they will make sense of it. World Cafe processes for example, often produce scads of data on table cloths at the centre of the table, but there is often so little context for this information that it is hard to make use of. My practice these days is to invite people to use the table cloths as scratch pads, and to collect important data on post it notes or forms that the group can work with. AND to do that in a way that allows people to be tagging and coding the data themselves, so that we don’t have to have someone else figure out what they meant.
- Have leaders and teams pour over the raw data and the signification frameworks that people have used and translate it into strategy.
These just begin to scratch the surface of this inquiry in practice. Over the next little while I’m going to be giving this approach a lot of thought and try it out in practice as often as I can, and where the context warrants it.
If you would like to try an exercise to see why this matters try this. the next time you are facilitating a brainstorm session, have the group record dozens of insights on post its and place them randomly on a wall. Take a break and look over the post its. Without touching the post its, start categorizing them and record your categorization scheme. Then invite the group to have a go at it. Make sure everyone gets a chance to participate. Compare your two categorization schemes and discuss the differences. Discuss what might happen if the group were to follow the strategy implicit in your scheme vs. the strategy implicit in their scheme.