Explorations in Human Decision Making

Abstract

Human decision making, even when it involves simple everyday choices has been revealed to have layers of complexity to it in recent years (P. W. Glimcher & Fehr, 2013; Rangel et al., 2008; Rushworth & Walton, 2009). The works contained in this thesis aim at exploring some of those layers, from designing procedures to improve decisions, conducting a meta-analysis on probability weighting, and looking at being hungry and how it affects decisions.

In the first chapter, we designed three choice procedures to improve choice quality without reducing the options available or using nudges. Using a within-subject experiment, we tested how our procedures compare to simply picking the preferred option. We find that each of our choice procedures increased the probability that a participant will correctly choose their independently identified highest valued option from the choice set.

In the second chapter, we present a meta-analysis of the sensitivity parameter, γ , of probability weighting functions used in models of decision-making under risk. Using 721 parameter estimates from 176 empirical papers, we examined the meta-analytic means, sources of heterogeneity, and domain dependence of γ across gains and losses, as well as when papers did not differentiate between the two (undifferentiated). We found that the meta-analytic mean of γ is significantly below one in all domains and found that estimates are lowest for gains, highest for losses, with undifferentiated estimates lying between the two.

In the third chapter, using the decision noise framework proposed by (Shen et al., 2025) we examined whether short term hunger influences simple food related decision-making through changes in early noise and or late noise. Contrary to our first hypotheses, we found that short term hunger does not reduce early noise, but we did find that it does not affect late noise which supported our second hypothesis.