Séminaire Décision, Interaction et Marchés

Winter 2017

March 28th: Martin Meier (IHS, Vienna)
Perfect Quasi-Perfect Equilibrium (with L. Blume)

 

April 25th:

11h-12h
Leonardo Pejsachowicz (Princeton U.),
Breadth versus Depth (with M. Richet and S. Geng)
Abstract:
We consider a fundamental trade-off in search: when choosing between multiple unknown alternatives, is it better to learn a little about all of them (breadth) or a lot about a single one (depth)? Generally, we find that breadth is optimal for “small” problems and that depth is optimal for “large” ones. We find that in a political setting where voters learn about candidates and find a rational justification for the stylized fact that voters tend to learn only about their preferred candidate. Finally, we consider extensions to fat-tails, correlation, and a strategic IO setting and find that in all extensions, breadth is superior.

12h-13h
Chantal Marlats (LEMMA, U. Paris II),
Reputation effects in stochastic games with two long-lived players.

 

June 6th: Chris Chambers (University of California San Diego),
Preference Identification (with F. Echenique and N. Lambert).
Abstract:
A subject has a privately known preference over the objects of a collection.  An experimenter produces pairs of these objects. For each pair, the subject is asked to choose from amongst her preferred objects.  We give sufficient conditions on the collection, preferences, and pairs of objects so that observing finitely many choices allows to learn the subject's preference with arbitrary precision. We show that, in general, large but finite data does not permit to learn the subject's preference precisely. We apply our results to Anscombe-Aumann expected utility.

 

June 20th: Jörg Öchssler (Universität Heidelberg)
Measuring skill and chance in games
Abstract:
Online gaming on the internet has become a multi-billion dollar industry. The question whether a game predominantly depends on skill or chance has important legal and regulatory implications. In many jurisdictions games of chance are prohibited or tightly regulated. Classifying mixed games, i.e. games that incorporate both strategic actions of players as well as the use of random devices, is a much-debated issue. A generally accepted criterion has not been developed yet. We pursue an empirical approach by capturing the heterogeneity of playing strengths of competitors and define the “best-fit” ELO algorithm to measure this heterogeneity. Subsequently, we apply the method to large datasets and propose a scale of skill and chance based on Chess data. Measuring two-player competitions of various games (i.e. Poker “Sit and Go”, Tetris, Sudoku) and positioning those on the scale, it turns out that Poker is ranked noticeably close to a game of pure chance. Therefore, we suggest to regard Poker as a game that predominantly depends on chance.

 

June 22nd: Adam Dominiak (Virginia Tech)
Epistemic Foundations of Equilibria under Ambiguity (with Jürgen Eichberger)

Abstract:


In this paper, we develop an interactive epistemology perspective justifying strategic

ambiguity and various equilibrium concepts for games with non-additive beliefs.

To accommodate strategic ambiguity in games, we introduce an extended version of

interactive belief systems in which some types might not know the action they play.

Yet, each type knows his theory, i.e., his probability distribution over the entire state

space. It is shown that player's beliefs about his opponents' behavior are non-additive

if he considers possible that his opponents are undetermined (i.e., his theory assigns

a positive probability to opponents' types who do not know what actions they play).

In this framework, we establish epistemic conditions under which beliefs constitute

an equilibrium under ambiguity for games with two and more than two players, respectively.

Our epistemic conditions for Nash-Equilibrium appear as a special case

and thus generalize the celebrated results of Aumann and Brandenburger (1995).