The Institute for Mathematical Behavioral Sciences presents
“Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization”
with Dan Cavagnaro, Lecturer, Department of Information Systems & Decision Sciences, Mihaylo College of Business & Economics, California State University Fullerton
May 2, 2013
Social Science Plaza A, Room 2112
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 proposed, their qualitative similarities make it challenging to discriminate among them empirically. This talk investigates 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. Simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models.
For further information, please contact Janet Phelps, email@example.com or 949-824-8651.