Soutenance de thèse de Benson Tsz Kin Leung, 18 septembre 2018

18 Septembre 2018 Recherche

Monsieur Benson Tsz Kin Leung soutiendra sa thèse de doctorat en Sciences économiques le mardi 18 septembre 2018 à 10h30 Salle  MF 429  (Manufacture des Tabacs) sur le sujet « Information Disclosure in a Duopoly».

Directeur de thèse: Jacques CREMER, Professeur, Toulouse School of Economics - CNRS.

Le jury se compose comme suit : 

-    Professeur Jakub STEINER, CERGE-EI et Université de Zurich
-    Professeur Olivier COMPTE, Paris School of Economics
-    Professeure Ingela ALGER, Toulouse School of Economics - CNRS
-    Professeur Jacques CREMER, Toulouse School of Economics - CNRS


Résumé (en anglais)

This thesis investigates several topics in the field of information economics. I analyze the behavior of the two sides of the information market.

The first chapter studies firms' disclosure decisions of product information in a duopoly setting, as well as the welfare implication of compulsory disclosure policy. I show that there is a problem of externality between the two firms: even if disclosure weakens price competition in the market and increases total industry profits, a firm could have incentive not to disclose product information because it decreases his market share. As a result, regulatory policy could increase total industry profits as it could rectify the problem of externality. Therefore, despite more information allows consumers to make a better choice between different alternatives, it might back fire as it could increase the average price in the market. I also present simple conditions on when providing more information could harm consumers, and when it will improve consumer welfare.
The second chapter studies the information processing behavior of a decision maker (DM) who has limited information processing ability. More specifically, the DM can process only a subset of all available information. Before taking an action, he chooses whether to process or ignore signals about the state of the world which he receives sequentially. I show that at the optimum, the DM processes only signals which are strong enough, but will process a weaker signal if it confirms his existing strong belief or if it supports a much more desirable state of the world. This explains some phenomena which have been well documented in the psychology literature, such as preference for strong signals, confirmation bias for individuals with strong prior and wishful thinking. Moreover, I analyze how the Internet, and in general changes in information structures, affects the processing behavior of the DM. The results shed light on different issues in the information era, including polarization and media strategy.

The third chapter is a follow-up study of the second chapter, which studies experimentally whether confirmation bias arises when individuals are exposed to information overload, or equivalently have limited ability to perfectly update their belief with all available information. In the experiment, subjects have to form beliefs as they navigate a sequence of signals within a limited period of time. We compare belief formation under two settings, where the treatment setting imposes a larger cognitive load than in the control setting. We find that subjects in the treatment setting exhibits a stronger confirmation bias than those in the control setting. Upon receiving a belief-challenging signal, subjects in the treatment group updates their belief less than those in the control group. In contrast, upon receiving a belief-confirming signal, subjects update similarly in both settings. As a result, subjects in the treatment setting are also less likely to switch sides than in the control setting: once they believe that one state is more probable than another, they are less likely to switch even if they receive enough belief-challenging signals. Not only these results show that the limited ability of belief updating plays a role in the formation of confirmation bias, it also improves our understanding on the impact of information overload, for example on polarization.