Emulating Rational Decisions With Traditional And Contemporary AI

Abstract

With the introduction of Large Language Models (LLMs) into society, they have been used for increasingly more complex and important tasks in various governmental and corporate settings. This has further been exacerbated with the introduction of ”thinking” models, which are designed and geared towards thinking and logical reasoning. With this in mind, this thesis aims to be a study of conflict resolution on a national scale, with different factors that affect decision-making involved, such as the level of cooperation, existing relationships, and types of governance. Each country is first grouped according to the type of governance. This allows us to extract government-specific actions that have been used by governments in the past to achieve their goals. Each country is then given a set of actions, relationships, and resources to use throughout the conflict resolution. The authors aim to reach the Nash Equilibrium as the resolution point, where no party can change the outcome of the conflict by modifying only their own decisions. This serves as an adequate completion point because hypothetically the only step that can be taken after reaching the Nash Equilibrium is to declare war, something that is to be avoided as much as possible. The thesis finally conducts versus matches between the MCTS algorithm and LLMs, with each party being either cooperative or uncooperative