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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:20240812T131812Z
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