"Mechanism design," says Professor Amy Greenwald of Brown CS, "is the engineering branch of game theory, where knobs can be adjusted to achieve certain goals in multi-agent systems inhabited by strategic agents. For example, a network designer might seek a design that minimizes congestion assuming selfish agents."
In a recent Research Keynote Series address ("Learning Equilibria in Simulation-Based Games ... and the Ensuing Empirical Design of Mechanisms") at the annual IEEE UEMCON (the Institute of Electrical and Electronics Engineers Ubiquitous Computing, Electronics and Mobile Communication Conference), she put forth a methodology to design these mechanisms. It applies under two key conditions:
- the mechanisms induce games that can be simulated, but that do not afford an analytic description, and
- the agents play approximate equilibria in these simulation-based games.
"Under these conditions," Amy says, "we use the probably approximately correct learning framework to construct algorithms that learn equilibria. We show experimentally that our methodology can be used to design effective mechanisms that capture stylized, but rich multiagent systems, such as advertisement exchanges, which are not generally amenable to analytical mechanism design."
Amy's research on focuses game-theoretic and economic interactions among computational agents, applied to areas like autonomous bidding in wireless spectrum auctions and ad exchanges. During the 2018-19 academic year, she was a visiting researcher at the Artificial Intelligence Research Center at the Japanese National Institute of Advanced Industrial Science and Technology in Tokyo, and in 2017, she gave a keynote address at Microsoft's Faculty Summit.
For more information, click the link that follows to contact Brown CS Communication Outreach Specialist Jesse C. Polhemus.