N&O Lecture: Joe Halpern, Cornell University

Everyone who is interested is welcome to attend the CWI Lecture of Joe Halpern, Cornell University.   The title of his lecture is:  Decision theory with resource-bounded agents. Abstract:There have been two major lines of research aimed at capturingresource-bounded players in game theory.
  • N&O Lecture: Joe Halpern, Cornell University
  • 2016-06-17T13:00:00+02:00
  • 2016-06-17T14:00:00+02:00
  • Everyone who is interested is welcome to attend the CWI Lecture of Joe Halpern, Cornell University.   The title of his lecture is:  Decision theory with resource-bounded agents. Abstract:There have been two major lines of research aimed at capturingresource-bounded players in game theory.
  • What Networks & Optimization
  • When 17-06-2016 from 13:00 to 14:00 (Europe/Amsterdam / UTC200)
  • Where Room L016, ground floor CWI
  • Web Visit external website
  • Add event to calendar iCal

Everyone who is interested is welcome to attend the CWI Lecture of Joe Halpern, Cornell University.   The title of his lecture is:  Decision theory with resource-bounded agents.

Abstract:
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.

This is joint work with Rafael Pass and Lior Seeman.
No previous background is assumed.