Decision Sciences


Experimental investigations dating from the early 1950s have revealed a variety of violations of expected utility, the standard model of rational choice under uncertainty. Since the end of the 70's, an enormous amount of effort was devoted to elaborating descriptively viable (fitting the facts) and formally sound generalizations of expected utility. These alternatives are named “non-expected utility” models. The course will focus on prospect theory, one of the most popular descriptive generalization of expected utility. A significant body of (empirical and theoretical) work now incorporates insights from prospect theory into traditional models in Economics and Finance. 

The course has two objectives: (i) providing a formal setup that can help easily understanding where prospect theory deviates from the standard model of rational choice under risk (known probabilities) and ambiguity (unknown probabilities); and (ii) giving the basic tools allowing to test and / or measure individual preferences under risk and ambiguity while allowing for behavioral discrepancies from the standard model. 

Learning Outcomes

·       Modeling behavior under risk and ambiguity without committing to the rational choice model

·       Measure attitudes towards risk and ambiguity

·       Debiasing belief elicitation

·       How to account for differences between different sources of uncertainty


The course consists of six modules. 

1.     Formal setup (Preference relations on Cartesian Products, monotonicity, coordinate independence, trading-off outcomes, additive representation of preferences) 

2.     Consistent tradeoffs under uncertainty in the presence of subjective weighting of uncertainty (How to measure utility using tradeoffs with known and unknown probabilities; how to test consistency of utility measurement)

3.     The main differences between the standard model of rational choice and prospect theory (Final asset positions versus gains and losses, reference point, loss aversion, Rabin's paradox; prospect theory and investment behavior) 

4.     Prospect theory under risk (Behavioral foundations and empirical elicitation of preferences under risk, i.e., utility for gains and losses, probability weighting functions and loss aversion) 

5.     Prospect theory under ambiguity (Behavioral foundations and empirical elicitation of decision weights when probabilities are unknown) 

6.     Empirical Evidence on ambiguity under Prospect Theory (How to elicit subjective probability distributions and attitudes towards uncertainty under Prospect Theory, attitudes towards different sources of uncertainty)


Simon, French (1988): Decision Theory: An Introduction to the Mathematics of Rationality (Mathematics and its Applications), Ellis Horwood.
Tversky, Amos & Daniel Kahneman (1992) “Advances in Prospect Theory: Cumulative Representation of Uncertainty,” Journal of Risk and Uncertainty 5, 297–323.
Wakker, Peter (1989): Additive Representations of Preferences: A New Foundation of Decision Analysis, Springer.
Wakker, Peter (2010): Prospect Theory for Risk and Ambiguity, Cambridge.