Thursday, April 8, 1999
4:15 PM (refreshments 4:00)
Room NE43-518
EECS Special Seminar
Abstract
This talk concerns the strategic behavior of automated agents in the framework of network game theory, with particular focus on the collective behavior that arises via learning. Of particular interest are questions regarding the nature of equilibria in network games, such as efficiency and fairness, and perhaps more importantly, whether equilibrium is in fact the outcome of simple learning processes. Bearing these questions in mind, this talk conveys ideas on the theory and simulation of learning in network games, with regard to two sample applications. The first application is network optimization and control, presented as an abstraction known as the Santa Fe bar problem, for which it is shown that rational learning does NOT converge to Nash equilibrium, the classic game-theoretic solution concept. The second application is the economics of shopbots -- agents that automatically search the Internet for price and product information -- in which learning yields behaviors ranging from price wars to tacit collusion. This work forms part of a larger research program that advocates learning and games as a framework in which to model the interactions of computational agents in network domains.
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Modified: Mar 27, 1999|
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