Game theory is an important tool used to analyze strategic interactions across many disciplines, including economics, political science, operations research and computer science. However, when it comes to increasingly complex decision-making with incomplete information, there are no generally effective analysis techniques available.
Yevgeniy Vorobeychik, an associate professor of computer science and engineering at the McKelvey School of Engineering at Washington University in St. Louis, recently won a $400,000 grant from the National Science Foundation to develop equilibrium approximation algorithms that leverage modern artificial intelligence (AI) to expand the kinds of large-scale problems that researchers can tackle.
Vorobeychik seeks to advance mathematical methods of game-theoretic analysis, particularly by automatically discovering and using symmetries and scarcity in multiparty interactions with incomplete information.
