Cavagnaro, D., Pitt, M., Gonzalez, R., \& Myung, J. (2013). Discriminating among probability weighting functions using adaptive design optimization. {\it Journal of Risk and Uncertainty, 47,} 255-289. 10.1007/s11166-013-9179-3  PDF

Abstract

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been pro- posed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to inves- tigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empir- ical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear-in-Log-Odds) emerging as the most com- mon best-fitting models. The findings shed light on assumptions underlying these models.