Ian Gemp, Google DeepMind

Abstract

Game Theory is the study of mathematical models of strategic interactions among rational agents. As humans, language is a key medium of interaction. As such, assuming we can build a suitable model of the players, strategies, and payoffs associated with these interactions (i.e., a binding to the conventional symbolic language of game theory), we can then ask existing algorithms to provide us with optimal, rational solutions (e.g., equilibria), thereby giving us, for example, predictors of stable, rational conversational strategies in dialogue. Large language models have arguably reached a point where grounding to semantics and concepts is strong and accurate enough to rely on as a simulacra of human (+AI) dialogue. These foundation models allow us to begin to think of game theoretic algorithms in the space of natural language. They can also help us quickly prototype new game models via the expressivity of natural language, potentially leading to an explosion in the current repository of games one could study and test our solution concepts on. I’ll present one such binding from dialogue to game theory that we are considering as well as flavors of generalizing existing algorithms to this setting.

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