PhD Dissertations

Vincent ELI, Economics and Decision Sciences, 2017

Essays in normative and descriptive Decision Theory.

Advisor(s): Philippe MONGIN, Mohammed ABDELLAOUI


Decision Theory has been a very dynamic field since von Neumann and Morgenstern 1943. New decision models have opened new ways to think about our actions and every day decisions. Allais’ Paradox in 1953 forced decision theorists to be clearer about the intents their models and several authors claimed that expected utility solely has a normative intent (choices that we should make, potentially better) and not a descriptive one (choices as we make them, potentially flawed). It also allowed defining better methods of validation for a descriptive point of view. Best practices in descriptive decision theory have emerged and we have now clear-cut and vetted methods of justifying the use of a given model of decision theory for a descriptive aim. However for normative decision theory that intents to help us make better choices, we do not have a clear-cut way to determine and "prove" that a given model is the right one. This thesis provides an empirical design that provides such a methodology.

Marie LACLAU, Economics and Decision Sciences, 2012

Repeated games on networks and communication

Advisor(s): Tristan TOMALA


I study infinitely repeated games with imperfect private monitoring played on networks. Different networks may represent the structures of interaction, of monitoring, and of communication of the repeated game. Communication is costless, and may be either private or public. I study different models of repeated games, depending on the networks considered, on the nature of communication, and on the solution concept. The aim of this thesis is to establish necessary and sufficient conditions on the networks for a folk theorem to hold.

Reza LAHIDJI, Economics and Decision Sciences, 2012

Incertitude, causalité et décision : le cas des risques sociaux et du risque nucléaire en particulier

Advisor(s): Philippe MONGIN


Probability and causality are two indispensable tools for addressing situations of social risk. Causal relations are the foundation for building risk assessment models and identifying risk prevention, mitigation and compensation measures. Probability enables us to quantify risk assessments and to calibrate intervention measures. It therefore seems not only natural, but also necessary to make the role of causality and probability explicit in the definition of decision problems in situations of social risk. Such is the aim of this thesis. By reviewing the terminology of risk and the logic of public interventions in various fields of social risk, we gain a better understanding of the notion and of the issues that one faces when trying to model it. We further elaborate our analysis in the case of nuclear safety, examining in detail how methods and policies have been developed in this field and how they have evolved through time. This leads to a number of observations concerning risk and safety assessments.Generalising the concept of intervention in a Bayesian network allows us to develop a variety of causal Bayesian networks adapted to our needs. In this framework, we propose a definition of risk which seems to be relevant for a broad range of issues. We then offer simple applications of our model to specific aspects of the Fukushima accident and other nuclear safety problems. In addition to specific lessons, the analysis leads to the conclusion that a systematic approach for identifying uncertainties is needed in this area.When applied to decision theory, our tool evolves into a dynamic decision model in which acts cause consequences and are causally interconnected. The model provides a causal interpretation of Savage’s conceptual framework, solves some of its paradoxes and clarifies certain aspects. It leads us to considering uncertainty with regard to a problem’s causal structure as the source of ambiguity in decision-making, an interpretation which corresponds to a common understanding of the precautionary principle.

Laëtitia PLACIDO, Economics and Decision Sciences, 2009

From Ellsberg to Machina: Confronting decision models under ambiguity with experimental evidence

Advisor(s): Mohammed ABDELLAOUI


How do decision makers act and how should they act when confronted with uncertainty? Economic behavior under uncertainty is often influenced by the informational structure of the decision context. Remarkably, the joint presence (juxtaposition or combination) of two sources of uncertainty - namely risk (known probability) and ambiguity (unknown probability of events) - gives rise to behaviors that depart from standard models of decision making, subjective expected utility and its extension to non-additive probability, Choquet expected utility ; the essential behavioral component beyond paradoxes of uncertainty is (non neutral) attitude toward ambiguity. The studies reported in this thesis empirically investigate the heterogeneity of ambiguity attitudes in the light of the variability of the features of uncertainty. They deal with two main sorts of cases : the case where a decision maker faces two separate sources of uncertainty (two-color Ellsberg paradox) ; the case where a decision maker faces mixed sources of uncertainty (Machina paradox).

Marion OURY-PATARIN, Economics and Decision Sciences, 2008

Essais sur l’information presque complète en théorie des jeux et en économie

Advisor(s): Nicolas VIEILLE


Economic models always contain a description of the informational environnement, i.e., ananswer to the following questions: What is the knowledge of each agent about her final payments?What is her knowledge about the knowledge of the other agents? and so on... Of course, the modelerdoes not know exactly the mental representation each agent has regarding her environment.Therefore, to derive economic results, she has to abstract away from details and make strong simplifyingassumptions that are meant to be satisfied only approximately in the actual situation. Oneof the most commonly used idealizations is that of complete information. In complete informationsettings, the final payments are common knowledge among the agents. The aim of this Ph. D.thesis is to clarify the strategic implications of this assumption by relaxing it slightly and studyingmodels where information is nearly complete. It is shown that these implications are important.I also give some economic applications of these results in mechanism design and problems ofinformation transmission.