We set out the case for computational social science as opposed to traditional “pencil and paper” formal methods. The substantive theme of this book is the governance cycle in parliamentary democracies, but the ideas we put forward can be applied to many other areas of study.

We describe the institutional environment for the governance cycle in parliamentary democracies and the preferences of senior politicians over key political payoffs. We are not concerned here with electoral politics, so treat an election as a “black box” which, in expectation, administers unbiased random shock to party seat shares. Elections trigger government formation. The government, once formed is subject to a steam of unbiased shocks, some of which may perturb either the environment or the preferences of senior politicians sufficiently to cause them now to prefer some alternative to the incumbent government. The more susceptible an incumbent to such shocks, according to the model, the less stable it is likely to be. Politicians’ policy preferences are described in terms of their ideal positions on a large number of binary issues, and the relative importance they attach to each issue. The utility they derive from any government is described as a convex combination of the distance between their policy preferences and the agreed government policy position, which may involve “agreeing to disagree” on some issues; and their share of the fixed perks of office.

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.