In Jay Ulfelder’s reactions to Marc Lynch’s reflections on the Arab Uprisings, I was struck by Ulfelder’s discussion of motivated reasoning. Ulfelder’s notes a problem: “When we try to forecast politics in real time, we tend to conflate our feelings about specific events or trends with their likelihood.” I want Egypt to become democratic after Mubarak’s fall so, gasp, my deeply-informed analysis says Egypt is likely to become democratic. [Or insert your own favorite example.]
Ulfelder proposes a solution, or at least a coherent mitigation plan:
Whenever we’re formulating an analysis or prediction, we can start by ask ourselves what result we hope to see and why, and we can think about how that desire might relate to the conclusions we’re reaching. We can try to imagine how someone with different motivations might view the same situation, or just seek out examples of those alternative views. Finally, we can weight or adjust our own analysis accordingly. Basically, we can try to replicate in our own analysis what “wisdom of crowds” systems do to great effect on a larger scale. This exercise can’t fully escape the cognitive traps to which it responds, but I think it can at least mitigate their influence.
But what if the extrinsic motivation is the main guide to how we select or interpret the factors that point us toward our conclusion? In any given political situation, scholars can point to a myriad of factors or draw on a wide range of historical precedents. How do we know which tradition is most relevant and which variables to consult? If we want the process to conclude with democracy, that suggests a certain way of looking at the problem.
In other words, maybe the scientific (analytical) process is hopelessly tainted by our own preferences and hopes. Perhaps “feelings” and analytic outcomes co-vary more than we like to admit.