Random Choice and Learning

By Paulo Natenzon. Journal of Political Economy, 2019

99 citations recorded by the source corpus.

Context-dependent individual choice challenges the principle of utility maximization. I explain context dependence as the optimal response of an imperfectly informed agent to the ease of comparison of the options. I introduce a discrete choice model, the Bayesian probit, which allows the analyst to identify stable preferences from context-dependent choice data. My model accommodates observed behavioral phenomena—including the attraction and compromise effects—that lie beyond the scope of any random utility model. I use data from frog mating choices to illustrate how the model can outperform the random utility framework in goodness of fit and out-of-sample prediction.

Subfields: Economics and Econometrics, General Decision Sciences, Safety Research

View Paper: https://www.semanticscholar.org/paper/ef950c1282fa9079207fe70acc8ff7d4ed393360

Citations in the corpus

• Stochastic Choice and Preferences for Randomization (2017, unknown): “…Wu and Gonzalez (1996), Ratcliff and McKoon (2008),Wilcox (2011), Gul, Natenzon, and Pesendorfer (2014),Woodford (2014), Fudenberg and Strzalecki (2015), and Natenzon (2015); see also Johnson and Ratcliff (2013) for reviews. stochastic choice and preferences for randomization 47 in this case.”