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STATUS:CONFIRMED
LAST-MODIFIED:20120912T122329
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UID:ATEvent-eed436ece7e23bbcfe0d4623c4969225
SUMMARY:ARC Colloquium: Yashodhan Kanoria\, Stanford University
DESCRIPTION:Abstract:\nIn many contexts\, agents 'learn' behavior from interaction with friends/neighbors on a network. We call this phenomenon 'social learning'. We will focus on models of repeated interaction\, with agents 'voting' in a series of rounds on some issue of interest. Votes in the initial round are based on 'private signals'\, whereas votes in future rounds incorporate knowledge of previous votes cast by friends.\nWe consider two different models of iterative learning. A very simple model is `majority dynamics' where agents choose their vote based on the majority of neighbors' votes in the previous round. We analyze this model on regular trees. At the other extreme is iterative Bayesian learning: a fully rational model introduced by Gale and Kariv (2003). We introduce new algorithms for this model\, challenging a widespread belief that it is computationally intractable. We develop a novel technique -- the 'dynamic cavity method'\, which serves as a key tool for both models.\nBased on joint work with Andrea Montanari (Ann. App. Prob. 2011) and Omer Tamuz (submitted).\n
DTSTART:20120213T133000
DTEND:20120213T133000
CREATED:20120912T122329
DTSTAMP:20120912T122329
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