Leveraging Complexity Science to understand Human Behaviour

Part 5 | Rethinking Problem Solving using Complex Systems Solutioning Approach

  • Development sector programming (particularly in public health) usually begin with comprehensive advance planning which limits the entire program design with the knowledge , theories or hypotheses at the start of the program
  • Adapting the program processes (e.g. new experiments) based on learnings and ideas that emerge during the course of projects is difficult if not impossible
  • Due to the limitation of our own imaginations and individual cognition and our need for certainty, program designers prefer a linear problem solving with discrete distinctions between problem framing, research, design and implementation phases.
  • Even the multiple design approaches (e.g. double diamond 4 D’s) articulated in design literature, tends to suffer from this at least during implementation. While these approaches may have been initially developed on small scale projects with an iterative mindset, they are being applied on significant large scale complex engagements with high level of uncertainty and ambiguity without significant alterations.
  • Overall, the current approach tends to promote a deductive and a reductionist approach to problem solving that may be appropriate for Simple or even Complicated problems but not for complex problems. As a result, it limits the potential of development of emergent and non-linear solutions.
  1. Evolutionary driven Approach: Implement a dynamic and evolving Problem Solving Approach that allows of significant variation in the individual process elements (e.g. removal or addition of primary research efforts) with the assumption that the context is also continuously changing.
  2. Team of Teams mindset: Allow for parallel execution by cross-functional teams (e.g. research teams, pilot and implementation teams) that may be part of multiple organizations and communities working in the same project environment.
  3. Multiple Scale (or Scope) Operation: In addition to parallel efforts, we may want different teams to operate with different scale of population. Example, population level tools or research approaches (e.g. segmentation) vs community level solutions. This would allow for building on local understanding and implementing a non-linear problem solving approach that imposes variety at multiple scales of the program.
  4. Engagement guidelines vs comprehensive requirements: Instead of detailed specifications that limit emergence of solutions, articulate overall project constraints / boundaries, internal and external communication and risk mitigation processes.
  5. Research in Development (RinD): Support localized research in support and embedded within-on-going development process
  6. Ongoing Learning: Ensure a learning process about effective solutions through direct feedback from the environment is place.
  7. Living Theory of Change: Develop and continuously update a living Theory of Change focused on contextual elements that may be driving the behavior of the overall system and its stakeholders
  8. Platform for multiple interactions: Plan for an overall Coordination team that focuses on interaction aspects (how are people going to share and coordinate, how will new services be added etc) instead of minute details.
  9. Interventions as events: Re-conceptualize the notion of intervention , as an event/s in the history of the system that may allow for the system to self-organize and change the future trajectory of the system’s dynamics.
  10. Scalability of function: Instead of the conventional view around scalability of interventions where the form has to be standardized and replicated, focus on the function of the intervention and adapt it for different population context and complexity.



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Final Mile

Final Mile

Final Mile is a research and consulting firm solving tough and relevant behavioral problems across the globe