We build an agent-based model (ABM) of how senior politicians navigate the complex governance cycle using relatively simple heuristics. They first test whether they can form a single party minority government. If not, they seek coalition partners and negotiate with these. They treat “Gamson’s Law” – government parties get perks payoffs in proportion to their seat shares – as common knowledge. When different politicians attach different importance to the same issue, "logrolling" allows them to realize gains from trade and agree a joint policy position even when they have divergent policy preferences. We allow for the realistic possibility that multiple proposals for government are under consideration at the same time. Nonetheless, there may often be a “Condorcet winner” among the set of proposals, which beats all others in pairwise comparisons. Finally, we specify a model of government survival, which assumes incumbent governments are subject to a stream of unbiased random shocks which may perturb model parameters so much that legislators now prefer some alternative to the incumbent. For any given government, our model allows us to estimate the probability of this happening.

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.

While heavy-duty computational methods have revolutionized much empirical work in political science, computational analysis has yet to have much any impact on theoretical accounts of politics – in contrast to the situation in many of the natural sciences. We set here out to map a path forward in computational social science. Analyzing the complex and deductively intractable “governance cycle” that plays out in the high-dimensional issue spaces of parliamentary systems, we use two different computational approaches. One models functionally rational politicians who deploy rules of thumb to navigate their complex environment. The other deploys an artificial intelligence algorithm which systematic learns, from massively repeated self-play, to find near-optimal strategies. Future work made possible by greater computational firepower would enable better AI, more realistic ABMs, and the modeling of logrolling under the conditions of incomplete information which characterize most real-world bargaining and negotiation.

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.