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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Joe Halpern
DESCRIPTION:Title: Decision theory with resource-bounded agents\nSpeaker:
	 Joe Halpern\nAbstract: There have been two major lines of research aimed
	 at capturing resource-bounded players in game theory.  The first\,
	 initiated by Rubinstein\, charges an agent for doing costly computation;
	 the second\, initiated by Neyman does not charge for computation\, but
	 limits the computation that agents can do\, typically by modeling agents
	 as finite automata.  We review recent work on applying both approaches
	 in the context of decision theory. For the first approach\, we take the
	 objects of choice in a decision problem to be Turing machines\, and
	 charge players for the ``complexity'' of the Turing machine chosen
	 (e.g.\, its running time).  This approach can be used to explain
	 well-known phenomena like first-impression-matters biases (i.e.\, people
	 tend to put more weight on evidence they hear early on) and belief
	 polarization (two people with different prior beliefs\, hearing the same
	 evidence\, can end up with diametrically opposed conclusions) as the
	 outcomes of quite rational decisions. For the second approach\, we model
	 people as finite automata\, and provide a simple algorithm that\, on a
	 problem that captures a number of settings of interest\, provably
	 performs optimally as the number of states in the automaton increases. 
	 Perhaps more importantly\, it seems to capture a number of features of
	 human behavior\, as observed in experiments.\n\nThis is joint work with
	 Rafael Pass and Lior Seeman.\nNo previous background is assumed.
LOCATION:Gates 122
UID:2015-02-03
STATUS:CONFIRMED
DTSTART:20150203T170000Z
DTEND:20150203T180000Z
LAST-MODIFIED:20150130T180516Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121821Z
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