We set out an alternative, “top down”, approach to agent-based modeling. We develop an artificial intelligence (AI) algorithm to navigate the governance cycle using what we can think of as computational game theory. AI models have had formidable success in solving games like Chess, Go, and especially a bluffing game like Poker, suggesting they also have the potential to attack difficult political games. Addressing a simplified version of the government formation process as a noncooperative game, the AI algorithm deploys Monte Carlo Counterfactual Regret (MCCFR). During in massively repeated self-play, it samples paths though the vast game tree to relentlessly learn near optimal strategies.