AIC Seminar Series

Mechanisms (like auctions) serve to aggregate agents' individual preferences into a socially desirable outcome, while de-motivating them from altering the final selection by reporting exaggerated desires. Established results in Game Theory show that no mechanism can eliminate untruthfulness in all but a special, unrealistic subset of settings. However, the emerging field of automated mechanism design seeks to generate a specialized, untamperable, mechanism for any given setting via straightforward linear programming. However, initial efforts assume a finite enummeration of possible outcomes and agent preferences. Exponential auction was developed to address one of these concerns, extending the space of outcomes to the real numbers, as might be useful in typical setting involving prices and times. In doing so, it relies on a novel method of execution that is nondeterministically pegged to an exponential random variable. If there is interest the speaker will also devote a few minutes to a couple of other Stanford research projects dealing with the Semantic Web and the integration of qualitative information into statistical machine learning.

Eric Hsu has been a Research Engineer at SRI since 1999, working under Charlie Ortiz and others. He is on leave to attend Stanford until Winter.

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