This project is funded by the National Science Foundation (NSF CRII Award #2153184).
The project aims to adapt the Empirical Game-Theoretic Analysis (EGTA) framework to multi-level game model forms: strategic interactions that can be represented in the form of a directed, rooted tree. Extensive-form games (EFGs) are a classic example of this class of games that capture temporal patterns in agent activity, information revelation, and possible stochastic events (acts of Nature). Current EGTA practice abstracts all such temporal patterns away in a simulator (e.g., an agent-based model) that is queried to obtain payoff data for strategy combinations over players but induces a coarser game model that is essentially normal-form and does not reflect such patterns. We are taking the next step in EGTA design by explicitly incorporating features of the underlying game tree into the empirical game model itself and tackling the resulting conceptual and computational design challenges such as striking a balance between model granularity (which leads to better approximation) and per-iteration computational burden. Advances in this project can significantly improve our understanding of systems that comprise interacting AI agents employing highly sophisticated strategies (such as deep reinforcement learning algorithms), and in turn inform robust design of such agents.