Discriminating among probability weighting functions using adaptive design optimization

• Published in: Journal of risk and uncertainty Vol. 47; no. 3; pp. 255 - 289
• Main Authors: Cavagnaro, Daniel R., Pitt, Mark A., Gonzalez, Richard, Myung, Jay I.
• Format: Journal Article
• Language: English
• Published: Boston Springer Science+Business Media 01-12-2013; Springer US; Springer Nature B.V
• Subjects: Analysis; Design optimization; Discrimination; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Environmental Economics; Experiment design; Experimentation; Experiments; Heterogeneity; Mathematical independent variables; Microeconomics; Modeling; Operations Research/Decision Theory; Optimization; Parametric models; Probabilities; Probability; Prospect theory; Risk; Risk management; Sensory discrimination; Simulation; Simulations; Studies; Term weighting; Weighting functions; C52; Prospect theory; Experimental design; Active learning; Probability weighting; C91; D81; Model discrimination
• ISSN: 0895-5646; 1573-0476
• DOI: 10.1007/s11166-013-9179-3

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. In this paper, we use both simulation and choice experiments to investigate 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 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.