Thursday, April 10, 2014, 11:00am
Grad Student, MIT
"Algorithms for Strategic Agents"
Abstract: In traditional algorithm design, no incentives come into play: the input is given, and your algorithm must produce a correct output. How much harder is it to solve the same problem when the input is not given directly, but instead reported by strategic agents (who may choose to lie) whose incentives may not align with your own? This talk will present a recent series of results on black-box reductions from mechanism to algorithm design. That is, we show how to solve optimization problems while properly incentivizing strategic agents using only black-box access to an algorithm for (a modified version of) the same optimization problem when the input is directly given.
Bio: Matt Weinberg is a PhD candidate at the Massachusetts Institute of Technology, where he is advised by Costis Daskalakis. His research lies mostly at the intersection of Computer Science and Economics, including Algorithmic Game Theory, Online Algorithms, and Applied Probability. He obtained his B.A. in Math from Cornell University.
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