Common Introduction to all sections

This is part 1 of 10 blog posts I’m writing to convey the information that I present in various workshops and lectures that I deliver about complexity. I’m an evaluator so I think in terms of evaluation, but I’m convinced that what I’m saying is equally applicable for planning.

I wrote each post to stand on its own, but I designed the collection to provide a wide-ranging view of how research and theory in the domain of “complexity” can contribute to the ability of evaluators to show stakeholders what their programs are producing, and why. I’m going to try to produce a YouTube video on each section. When (if?) I do, I’ll edit the post to include the YouTube URL.

Part Title Approximate post date
1 Complex systems or complex behavior? up
2 Complexity has awkward implications for program designers and evaluators 6/14
3 Ignoring complexity can make sense 6/21
4 Complex behavior can be evaluated using comfortable, familiar methodologies 6/28
5 A pitch for sparse models 7/1
6 Joint optimization of unrelated outcomes 7/8
7 Why should evaluators care about emergence? 7/16
8 Why might it be useful to think of programs and their outcomes in terms of attractors? 7/19
9 A few very successful programs, or many, connected, somewhat successful programs? 7/24
10 Evaluating for complexity when programs are not designed that way 7/31

Complex systems or complex behavior?

There are two reasons why I am uncomfortable talking about complex systems. One reason is that I have never been able to find an unambiguous definition that everyone (or at least most people) agree on, and which also captures the range of topics that I think are useful in Evaluation. The second reason is that even if I knew what a complex system was, I would have no idea what to do with it when designing or conducting an evaluation.

What I do find useful is a focus on what complex systems do, on how they behave. Those behaviors are something I can do something with.  To telegraph an example I’ll use in Part 7, (Why should evaluators care about emergence?), when there is emergent behavior, a whole cannot be understood in terms of its parts. Were I to suspect such behavior, my program models would be less granular, my methodology would address different constructs, and my data interpretation would ignore fine level detail.

Table 1: Cross reference, complexity themes and complex behaviors that are useful in evaluation
  Theme in Complexity Science
Complex behavior that may be useful in evaluation Pattern Predictability How  change happens
Attractors      
Emergence      
Sensitive dependence      
Unpredictable outcome chains      
Network effects among outcomes      
Joint optimization of uncorrelated outcomes      

Not all complex behaviors are useful in evaluation, but some are. Also, appreciating the application of complexity in evaluation extends to themes that cut across much of the writings that appear in fields such as biology, meteorology, physics, mathematics, economics, and many others. For doing evaluation, I find it useful to think in terms of three themes: 1) pattern, 2) predictability, and 3) how change happens. When I do evaluation, I try to think about how invoking complex behaviors can help me understand a program in terms of those three themes. Table 1 shows the cross-references. In any given evaluation some cells will have content, and some will be empty.

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