A Procedural Model of Lottery Complexity
(Presented at: BRIC, D-TEA, FUR)
Abstract
I consider a decision-maker who uses rules to simplify lotteries in order to compare them. I characterize expected utility in this setting and highlight its complexity requirements which a purely axiomatic characterization overlooks. I relax these requirements to characterize two models of complexity aversion : outcome support size cost and entropy cost models. I consider an additional aspect of complexity: decision-makers find it easier to evaluate a lottery when outcomes are close in value. To capture this, I characterize a third model of complexity aversion. Here the DM first partitions together outcomes which are close in value and then evaluates the lottery along with the complexity of the partition. This representation offers a measure of complexity which is not restricted to the probability and support size but also accounts for the cardinal values of the outcomes. I also compare empirically the models and find support for partition complexity.
Updating Bias and Model Misspecification: Evidence from the Classroom (with Marc-Antoine Chatelain, Paul Han and Xiner Xu)
Preference for Clarity (with Billur Gorgulu)
Two-Step Bootstrap Model Selection (with Guangbin Hong)