La science économique au service de la société

Program content

PNG - 13.4 Mo

This PSE summer school presents topics in behavioural game theory, bounded rationality and decision theory. It is motivated by the desire to move beyond the standard paradigm with rational expectations and expected utility theory, and present the implications of this for the understanding of economic phenomena sometimes viewed as paradoxical in the standard paradigm.

Behavioral game theory (Evan Friedman, PSE) – 10h
Analogy-based expectation equilibrium (Philippe Jehiel, PSE and UCL) – 5h
Games with strategy restrictions (Olivier Compte, PSE) – 5h
Prospect theory and ambiguity (Jean-Marc Tallon, PSE) – 5h

Workshop : present your paper
Participants will have the opportunity to submit a paper to be presented within this programme. The three selected papers will be presented in front of participants and faculty in a slot reserved for such presentations.

Games with strategy restrictions – Olivier Compte

The Bayesian paradigm used in economic modelling often assumes that agents behave optimally as if they knew all details of the model (preferences, distributions etc). In this mini-course, we explore how strategy restrictions can help assess the robustness of models that use the Bayesian assumption, and also how these strategy restrictions, viewed as a family of available heuristics, can be used as a modelling device to limit the sophistication of the agents we model, including analyzing agents with differentiated levels of sophistication or different ways agents would frame their own environment. Recent work in this vein will be presented, as well as an analysis of how this line of research departs from other work in the bounded rationality literature.


  • The role of strategy restrictions. Examples
  • Some recent applications
  • Comparison with other approaches to bounded rationality

Selected key references
- Ignorance and Uncertainty, Olivier Compte and Andy Postlewaite, Econometric Society Monograph, Cambridge University Press 2019

Behavioral Game Theory – Evan Friedman

The goal of this course is to offer an introduction to the branch of behavioral game theory (BGT) based on bounded rationality (as opposed to, e.g., social preferences). We first motivate BGT by reviewing classic experiments that document deviations from Nash equilibrium in simple games of complete information. We discuss alternative concepts that can explain these anomalies, emphasizing how such models are commonly used in the analysis of experimental data. We then consider games with incomplete information and study models in which players neglect correlation between variables or misperceive their strategic environment. Finally, we consider concepts for multi-stage games in which players use simplified strategies that can be implemented by algorithms or “automata”.


  • Why behavioral game theory ? Experimental evidence
  • Games of complete information : equilibrium with stochastic elements, iterated reasoning
  • Games of incomplete information and model misspecification
  • Multi-stage games and automata theory

Selected key references
- Goeree, J. and C. Holt (2001), « Ten Little Treasures of Game Theory and Ten Intuitive Contradictions », American Economic Review.

- McKelvey, R. and T. Palfrey (1995), « Quantal Response Equilibrium in Normal Form Games », Games and Economic Behavior.

- Goeree, J., C. Holt and T. Palfrey (2005), « Regular Quantal Response Equilibrium », Experimental Economics.

- Osborne, M. and A. Rubinstein (2003), « Sampling Equilibrium, with an Application to Strategic Voting », Games and Economic Behavior.

- Friedman, E. (2022), « Stochastic Equilibria : Noise in Actions or Beliefs ? », American Economic Journal : Microeconomics.

- Nagel, R. (1995), « Unraveling in Guessing Games : an Experimental Study », American Economic Review.

- Alaoui, L. and A. Penta (2016), « Endogenous Depth of Reasoning », The Review of Economic Studies.

- Esponda, I. and D. Pouzo (2016), « Berk-Nash equilibrium : A framework for modeling ».

- Spiegler, R. (2016), « Bayesian Networks and Boundedly Rational Expectations », Quarterly Journal of Economics.

- Rubinstein, A. (1986), « Finite automata play the repeated prisoner’s dilemma », Journal of Economic Theory

Analogy-based expectation equilibrium – Philippe Jehiel

The goal of this course is to expose students with a game theoretic model developed over the last twenty years that aims at relaxing the degree of fineness with which economic agents understand the reaction of their environment. The course will present the basics of the analogy-based expectation equilibrium as initially introduced in Jehiel (2005) as well as various applications covering bargaining, cooperation, investment strategy among others. At the end of the course, students should be equipped to apply the approach to whatever field of interest.


  • The analogy-based expectation equilibrium : Theory
  • The analogy-based expectation equilibrium : Applications

Selected key references
- Jehiel, P, (2005), « Analogy-based Expectation Equilibrium », Journal of Economic Theory.

- Jehiel, P and F. Koessler, (2008), « Revisiting Games of Incomplete Information with Analogy-based Expectations », Games and Economic Behavior.

- Ettinger, D and P. Jehiel, P, (2010), « A Theory of Deception », AEJ : micro.

- S. Huck, P. Jehiel and T. Rutter, (2011), « Learning spillover and analogy-based expectations : A multi-game experiment », Games and Economic Behavior.

- Jehiel P, (2011), « Manipulative auction design », Theoretical Economics.

- Jehiel P, (2018), « Investment strategy and selection neglect : An equilibrium perspective on overoptimism », American Economic Review.

- Jehiel P, (2022), « Analogy-based expectation equilibrium and related concepts : Theory, applications, and beyond », prepared for the World Congress of the Econometric Society 2020.

Prospect theory and Ambiguity – Jean-Marc Tallon

Invoking probabilities when thinking about an uncertain future is rather common and yet, coming up with probabilistic representations on the one hand and using probabilities to take decisions on the other hand is extremely complex. In this mini-course, we will explore recent approaches to these issues, focusing on so-called “ambiguity” models.


  • Coming up and working with probabilities.
  • Decision under ambiguity : what do experiments teach us ?
  • Modeling decision under ambiguity

Selected key references
- I.Gilboa, Theory of Decision under Uncertainty, Cambridge University Press, 2009.

- P.Wakker, Prospect Theory for risk and ambiguity, Cambridge University Press, 2010.

- Handbook of Economics of Risk and Uncertainty, Machina and Viscusi (ed), North Holland, 2014.

Contents – Bounded rationality in decisions and games