1 - Governance, Complexity, Computation, and Rationality

Summary

We outline the core argument of the book and steps taken to establish this. We begin by sketching component parts of the governance cycle: election, government formation, and government survival. Noting that the analysis of this complex system is intractable for traditions deductive methods of formal modeling, we preview two different computational methods for analyzing it. First, we model “functionally rational” artificial agents who use simple but effective rules of thumbs to navigate their high stakes but complex environment (ABM). Second, we specify an artificial intelligence (AI) algorithm which, by massively repeated self-play, teaches itself to find near-optimal strategies for playing what is in effect a traditional, but intractable, noncooperative game. We conclude by sketching the empirical approach we use to first calibrate and exercise the models on training data and then test them on out-of-sample test data.