The term “results oriented” has become a new buzzword in international development. Its near-universal usage among funders and practitioners suggests our industry has failed to give sufficient attention to measuring meaningful outcomes along with associated costs.
The drive to do more with less has given rise to the measurement imperative — the use of rigorous quantitative methods to establish fundamental relationships between a few variables with the aim of identifying simple causal pathways to pre-established indicators of success. As a corollary, we find an increasing emphasis on accountability to better control the production of intended results as cost-effectively as possible.
Who can argue with the need for doing more with less? As serious practitioners of social and economic change, we accept this moral imperative of being held accountable — to our fellow citizens, to our funders, to our clients and to ourselves. Yet, there is something missing. It is as if we have reduced our understanding of reality and all of its complexity to fit the limitations of our methods. This view feels incomplete and needs some major re-envisioning.
Anyone who has ever implemented a project will agree that many results are neither intended nor direct. The causal pathways that underlie our work are usually far from simple. Reality often diverges significantly from the results framework to which we are held accountable. Effects are often subtle, indirect, mediated or delayed. It is difficult to know what will actually happen in a system unless the parameters are highly constrained and the all-important context is artificially removed. How do we reconcile the need for achieving results and accountability within a more complex, interrelated yet unpredictable understanding of reality?
Warren Weaver, the co-founder of information theory in the mid-20th century, argued that to find correlations and patterns, science had focused on either simple two variable problems (x and y axis) or the application of statistical methods to “disorganized complexity,” a situation in which the variables are extremely large with a high degree of randomness. Most social and natural phenomena fall somewhere between these two extremes. Weaver labeled this middle ground as “organized complexity,” defined as problems that require dealing simultaneously with many factors that are interrelated into a whole, what we now call a complex system. To understand and deal with complex systems, Weaver suggested a need for different approaches to discovery and measurement.
Weaver’s advice still stands today. I contend that we, as development practitioners, need to adopt the view of organized complexity — a system’s view — when trying to tackle most development challenges. Our current fixation on results is too focused on measuring the interaction between simple variables, as is done in drug trials and other areas of the medical sciences, or applying statistical analysis when the number of variables is very large. While these methods remain useful for certain kinds of problems, we need new frameworks and tools that can deal with complex phenomena like market system development, institutional change, poverty traps and resilience — problems that clearly fall into Weaver’s middle ground of organized complexity.
The purpose of the Advancing the FIELD conference is about doing just that: stepping back to become more thoughtful about the complex nature of human problems and developing new ways to both understand and measure organized complexity and systemwide change. Only when we stop boxing ourselves into narrowly defined measurement techniques that oversimplify complex phenomena can our industry adopt an approach to managing results and accountability that can be truly characterized as “more for less.” The task is at hand.