The Building State Capability (BSC) program at the Center for International Development (CID) at Harvard University researches new strategies and tactics to build the capability of public organizations to execute and implement.
The BSC program is exploring the potential of a Problem Driven Iterative Adaptation (PDIA) approach, which rests on four core principles:
Many development challenges are complex, involving a lot of different agents and with unknown dimensions. Solutions to these challenges are often unknown, and contextually dependent. At the same time, there are political imperatives at play in many contexts which create pressure to ‘find the solution now…and then scale it up.’ Such pressure raises a question: how does a policy entrepreneur or reformer find a new solution and scale it up when dealing with complexity? This is the subject we address in the current paper, which is the fifth in a series on ‘how to’ do problem driven iterative adaptation (PDIA) (Andrews et al. 2015, 2016a, 2016b, 2016c).
The paper focuses on building broad agency solutions in the process of identifying problems and finding and fitting contextually appropriate solutions. The broad agency is, in our opinion, a most effective mechanism to ensure scaling and dynamic sustainability in the change process. As with other working papers on this topic, the contents here do not offer all answers to those asking questions about how to do development effectively. It closes by reflecting on the importance of ‘you’ (the reader, and ostensibly part of a policy change or reform team somewhere) using this and the other ideas as heuristics to rethink and reorient how you work—but with your own signature on each idea.
Many of the challenges in international development are complex in nature. They involve many actors in uncertain contexts and with unclear solutions. Our work has proposed an approach to addressing such challenges, called Problem Driven Iterative Adaptation (PDIA). This paper is the most recent in a series intended to show how one can do PDIA, building on the first paper, "Doing Problem Driven Work.” The current paper addresses a key part of the approach one moves to once a problem has been identified, performing real-time experimental iterations. This is intended as a practical paper that builds on experience and embeds exercises for readers who are actually involved in this kind of work.