Abstract. \;Randomized acts play a marginal role in traditional Bayesian decision theory\, essentially only one of tie-breaking. Meanwhile\, rationales for randomized decisions have been offered in a number of areas\, including game theory\, experimental design\, and machine learning. A common and plausible way of accommodating some (but not all) of these ideas from a Bayesian perspective is by appeal to a decision maker&rsquo\;s bounded computational resources. Making this suggestion both precise and compelling is surprisingly difficult. We propose a distinction between interesting and uninteresting cases where randomization can help a decision maker\, with the eventual aim of achieving a unified story about the rational role of randomization. The interesting cases\, we claim\, all arise from constraints on memory.
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\nWe will be having dinner right after the meeting at the faculty house. Please let me know if you will be joining us so that I can make an appropriate reservation (please be advised that at this point the \;university \;only agrees to cover the expenses of the speaker and the rapporteur and that the cost for all others is $30\, payable by cash or check). Please let Robby (jrf2162@columbia.edu) know if you want to join us.
ORGANIZER;CN=Robby Finley;CN=Haim Gaifman;CN=Yang Liu;CN=Rohit Parikh: METHOD:PUBLISH END:VEVENT END:VCALENDAR