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.

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.