Problems can be simple, complicated, or complex. Building a hospital is an example of a relatively simple challenge; given the necessary resources and expertise, we can generally predict the cost, timeline, and end result. Developing a vaccine represents a more-complicated challenge—it may take many attempts before a group develops a successful formula, but once it does, it can reproduce the vaccine and expect to see consistent results.
Complex problems are quite different, because they involve systems—ever-changing, non-linear environments. Making progress on complex problems requires that we understand the interplay between multiple independent factors that influence each other in dynamic ways. While constructing the hospital itself might be relatively straightforward, when you factor in raising the money for the project, getting buy-in from the community, staffing, and other factors, it becomes more complex. Similarly, the complicated science of developing a vaccine evolves into a complex problem when you add in the challenges of investing in R&D for a product that meets a public need but does not feed an ongoing market, or convincing some communities that taking up the vaccine is a good idea.
Improving the health of a community is a complex problem. Availability and quality of health care are important, but so are economic conditions, social norms, and a myriad other factors. The interplay and interrelatedness of these factors creates a kaleidoscope of causes and effects that we can easily capture in one “full picture”—and that can shift the momentum of a system in one direction or another in unpredictable ways. In these circumstances, each intervention is unique; programs that are successful at one point or in one place will not necessarily be successful elsewhere. Past efforts can provide guideposts, but replication is not a feasible goal, and fidelity will prove quite elusive.
Complex problems require an emergent strategy—strategy that evolves over time as initial intentions and plans collide with, and accommodate, an ever-changing reality. Using this approach, organizations learn what works in practice and accept that the strategy will change in unpredictable ways over time.
There is no systemic procedure or formula for how to figure out, or evaluate, what is going on within a complex system or emergent strategy. However, there are a few principles that can help guide practitioners as they take steps into this strange new world of evaluating complexity. We have recently laid out nine of these principles—here’s a brief look at three that are particularly critical:
- Continuous learning. Constant learning allows organizations to adapt and evolve as they implement strategies and activities. The regular flow of data and information is important to running a “learning engine,” and enabling adaptation and innovation. Information can also provide positive and negative feedback that reinforces desired patterns or dampens unproductive ones. For example, the more information a teacher has about exactly where each of his students is struggling or attaining mastery, the more he can differentiate his instruction to each of those student’s needs. He could also assess which instructional approaches worked best for helping students grasp particular concepts. New technologies are emerging that give teachers access to this kind of granular data on student performance, but differentiation also requires that teachers adopt a continuous improvement mindset, in which they see data less as an accountability mechanism, and more as a way to gain insight into what is happening in their classrooms and to inform their pedagogical strategies. Evaluations of emergent strategies can help improve and strengthen capacity for continuous learning through the collection and analysis of data, and through interpreting that data to make sense of what it means in ways that are timely and actionable.
- Attention to context. Context matters in most evaluations, but it’s particularly important to understand the nature and influence of context in emergent strategy. Because emergent strategies tend to involve many actors and organizations, unfold over a number of years, and naturally adapt in response to changing conditions, evaluations need to capture information on how the initiative and its context are “co-evolving.” In other words, the evaluation should not only study the nature of the context and its influence, but also measure the ways in which the initiative affects it.
- Focus on relationships. A defining characteristic of complex systems is that they contain a web of relationships and interdependencies. This “interstitial tissue” is what often makes or breaks a strategy, either by amplifying the power of the strategy or by getting in its way. In the context of complex problems, the relationships among people and organizations are often as important, if not more so, than the people and organizations themselves. With each relationship, it is important to understand its nature, strength, and longevity. It’s also important to know the levels of relational trust, the quality of the relationship, and how entities work together within the relationships, such as the ways and extent to which they share information, plan together, and co-construct solutions. Launching an effort to clean up a watershed, for instance, is much easier if the farmers, companies, government agencies, and other stakeholders involved have some history of productive collaboration with each other, rather than a legacy of mistrust and competition for resources.
Evaluating complexity is itself complex, and it requires an emergent strategy. Over time, as we carry out more and more evaluations of this kind, we will no doubt add clarity and depth to the propositions for how to do it well. But even today it is clear that continuous learning, attention to context, and a focus on relationships are important to the success of any strategy to improve the health of a community.
This post was first published as part of a blog series on Stanford Social Innovation Review – see it here.