New Game Theory for New Agents: Foundations and Learning Algorithms for Decision-Making Mixed-Agents

Project Overview

Modern strategic environments include a diverse set of participants: humans, semi-autonomous systems guided by humans, and autonomous AIs. Complex and multiscale interactions between participants, ubiquitous uncertainty regarding the environment and other participants, and computational limitations and behavioral biases intrinsic to the participants in these environments complicates prediction of outcomes and the design of better systems. Our aim is to develop new approaches resulting in accurate predictions for such environments, as well as leading to the design of learning algorithms and machines that perform better in strategic settings, aid in understanding the underlying causes of observed outcomes, and ultimately result in better designed systems. To achieve our aim for this new learning-driven paradigm of systems of heterogeneous agents, we will tackle afresh the grand challenges of game theory–predicting behavior and outcomes in complex strategic settings, designing learning algorithms that work well in such complex settings, designing systems that ensure good outcomes with heterogeneous agents, and guiding behavior to outcomes with desirable properties–by building a new game theory for new agents.

Sponsored by Office of Naval Research (ONR)