Ulric B. and Evelyn L. Bray Social Sciences Seminar
Abstract: We provide an axiomatic foundation for a class of neural-network models applied to decision-making under risk, called neural-network expected utility (NEU) models. Motivated by classic experimental findings, we weaken the independence axiom in a novel way. We show how to use simple neurons, referred to as behavioral neurons, in NEU models to capture behavioral effects, such as the certainty effect and reference dependence. Empirically, we show that some simple NEU model with natural interpretation predicts better than existing theories, such as expected utility theory and cumulative prospect theory out of sample, and that behavioral neurons help improve NEU models' performance.
Written with Chen Zhao, Zhaoran Wang, and Sung-Lin Hsieh. Professor Ke will be joined by guests Pietro Ortoleva and Paulo Natenzon.