Seminars

Communication, consensus, and order. Who wants to speak first ?

Speaker: Lucie MENAGER
Paris II

30 May 2008 - From 16:15 to 18:00

Parikh and Krasucki [6] show that, in a group of rational agents, communication of the value of a function f leads to a consensus on the value of f, provided some conditions on the communication protocol and the function f hold. In this article,
we address the issue of the influence of the protocol on the outcome of the communication process, when agents value
information positively. We show that, if it is common knowledge in a group of agents that some of them disagree on two protocols, then the consensus value of f must be the same for both protocols.

Updating Ambiguity Averse Preferences

Speaker: Peter Klibanoff
Kellogg School of Management

28 May 2008 - From 10:00 to 12:00

Dynamic consistency leads to Bayesian updating under expected utility. We ask what it implies for the updating of more general preferences. In this paper, we characterize dynamically consistent update rules for preference models satisfying ambiguity aversion. We then apply our general results to characterize dynamically consistent updating for two important models of ambiguity averse preferences: the ambiguity averse smooth ambiguity preferences (Klibanoff, Marinacci and Mukerji [Econometrica 73 2005, pp. 1849-1892]) and the variational preferences (Maccheroni, Marinacci and Rustichini [Econometrica 74 2006, pp. 1447-1498]). The latter includes max-min expected utility (Gilboa and Schmeidler [Journal of Mathematical Economics 18 1989, pp. 141-153]) and the multiplier preferences of Hansen and Sargent [American Economic Review 91(2) 2001, pp. 60-66] as special cases. For smooth ambiguity preferences, we also identify a simple rule that is shown to be the unique dynamically consistent rule among a large class of rules that may be expressed as reweightings of Bayes' rule.

On the Principle of Consistency

Speaker: Horst Zank
University of Manchester

21 May 2008 - From 14:00 to 16:00

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A Theory of Bayesian Decision Making

Speaker: Edi Karni
John Hopkins, HEC

7 May 2008 - From 14:00 to 16:00

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This paper presents a complete, choice-based, axiomatic Bayesian decision theory. It introduces a new choice set consisting of information-contingent plans for choosing actions and bets and subjective expected utility model with effect-dependent utility functions and action-dependent subjective probabilities which, in conjunction with the updating of the probabilities using Bayes' rule, gives rise to a unique prior and a set of action-dependent posterior probabilities representing the decision maker's prior and posterior beliefs.

Multiple Expectations, Disappointment, and Risk Measures: A Unifying Model of Decision under Risk

Speaker: Philippe DELQUIE
INSEAD

16 April 2008 - From 14h00 to 16h00

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The standard theory of Disappointment is built on the assumption that an individual forms a prior expectation about a risky prospect and will experience disappointment if the outcome obtained falls below the expectation. Here we abandon the hypothesis of a well-defined prior expectation: we propose instead that disappointment may arise from comparing the outcome received with any of the prospect's missed outcomes. This alternative behavioral hypothesis yields the so-called Disappointment without Prior Expectation (DWPE) model, which bridges a number of landmark theories of choice under risk that have evolved from distinct intellectual paths. For example, DWPE leads to a Rank Dependent Utility representation. Second, DWPE is equivalent to a general class of Risk-Value models, an appealing structure because decision makers often seek to rank projects in terms of two criteria: reward and risk. The new risk measure we obtain includes some classic measures such as the Variance or Gini Mean Difference, but it is not part of the families traditionally considered. We show necessary and sufficient conditions for this risk measure to satisfy first- and second-order stochastic dominance (thus generalizing results by Yitzhaki 1982) and coherence, key properties required for prescriptive applications, e.g. in Finance, Insurance, or R&D portfolio selection. Results from calibration of the model to experimental data will be presented.


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