A New Framework for Aggregating Utility

Kenny Easwaran (Texas A&M University, ), Kenny Easwaran

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’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.

----------------------------------------------

We 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 ([email protected]) know if you want to join us.