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A shift in perception: exploring ways to work with complexity in international development

I really enjoyed to go back this week to this paper from 2016 by ODI’s Anne Buffardi: When Theory Meets Reality.

Anne Buffardi, exploring ways to work with complexity

I went back to Anne’s paper because I am doing some work on defining some principle the can help design and set up monitoring, evaluation and learning (MEL) systems for development project/programmes that tackle complex social and governance problems.

I have been quite involved over the last few years with the discussions in various communities: PDIA, Doing Development Differently, adaptive programming, and to some extent thinking and working politically.

There are a lot of discussions and conversation about principles that can help shift the way development initiatives are designed, managed, and implemented (PDIA is a bit different as in addition to principles it proposes also concrete tools to manage uncertainty and complexity) . These principles speak to the shift of perception needed in the international development community to move from a mindset that sees problems and possible solutions in isolation from the context and systems that enable them to start dancing with the systems in which they operate.

This shift is important and necessary, but translating these principles into practical actions and ways of working is difficult. Anne mentions some of these challenges in her Methods Lab paper.

I have the same challenge in my work on problem-driven political economy and on MEL systems design. I am on board with the principles. I agree and believe in them but then struggle to apply them to, for example, coming up with a concrete outcome indicators that speak to the complexity and uncertainty that the project is trying to influence.

Anyhow, the journey continues and the paper Anne has written in 2016 has many insights that resonate with me. I am pasting below the key messages of Anne’s paper. Go online and download it. It is a great read.

  • Over the last 50 years ,repeated calls for adaptive learning in development suggests two things: many practitioners are working in complex situations that may benefit from flexible approaches, and such approaches can be difficult to apply in practice.
  • Complexity thinking can offer useful recommendations on how to take advantage of distributed capacities, joint interpretation of problems and learning through experimentation in complex development programmes.
  • However, these recommendations rely on underlying assumptions about relationships, power and flexibility that may not hold true in practice, particularly for programmes operating in a risk averse, results-driven environment.
  • This paper poses guiding questions to assess the fit and feasibility of integrating complexity- informed practices into development programmes.

Here the extract from the conclusions of the paper:

“Complexity thinking can offer useful insights on how to approach situations and challenges faced by development practitioners. However, these recommendations rely upon underlying assumptions about relationships, power and flexibility that may not always hold true in practice, where organisations have established internal and external ways of working. By posing guiding questions to assess the fit and feasibility of integrating complexity-informed practices, this paper aims to help development programmes identify which dimensions of complexity are most appropriate and realistic to address; and, how they can work within their operating constraints to take advantage of distributed capacities, joint interpretation of problems and experiential learning.

Classifying which practices are more and less feasible enables decision-making and management styles to be explicit, rather than couched in aspirational rhetoric about sharing and collaboration, learning and adaptation, which is not realistic in practice. If undertaken before programme implementation, it can help identify decision-making and communication mechanisms that can facilitate interaction among dispersed actors, create incentive structures that reward learning as well as delivery, and allocate sufficient time and resources for data collection, analysis and interpretation to enable monitoring to inform decision-making.”

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