Analyzing learning effects in the newsvendor model by probabilistic methods

Abstract

In this paper, we discuss discrete choice theory and show how this theory can be used to quantify learning effects in experimental studies. We argue why the ordering quantities in newsvendor experiments should follow a multinomial logit distribution. We provide a robustness analysis to explain that the standard conditions for logit distributions can be relaxed considerably. A main finding is that when optimal parameter values are inferred from the empirical data, the model predicts observed orders well. This provides empirical evidence for a multinomial logit distribution in such experiments. Finally, we analyze the learning effect using the experimental data collected by Bolton et al. (2012).