Information Design

Ina Taneva ()

Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh

Abstract: There are two ways of creating incentives for interacting agents to behave in a desired way. One is by providing appropriate payoff incentives, which is the subject of mechanism design. The other is by choosing the information that agents observe, which we refer to as information design. We consider a model of symmetric information where a designer chooses and announces the information structure about a payoff relevant state. The interacting agents observe the signal realizations and take actions which affect the welfare of both the designer and the agents. We characterize the general finite approach to deriving the optimal information structure for the designer - the one that maximizes the designer's ex ante expected utility subject to agents playing a Bayes Nash equilibrium. We then apply the general approach to a symmetric two state, two agent, and two actions environment in a parameterized underlying game and fully characterize the optimal information structure: it is never strictly optimal for the designer to use conditionally independent private signals; the optimal information structure may be a public signal or may consist of correlated private signals. Finally, we examine how changes in the underlying game affect the designer's maximum payoff. This exercise provides a joint mechanism/information design perspective.

Keywords: informtion design; implementation; incomplete information; Bayes correlated equilibrium; sender-receiver games (search for similar items in EconPapers)
JEL-codes: C72 D72 D82 D83 (search for similar items in EconPapers)
Pages: 47
Date: 2015-02
New Economics Papers: this item is included in nep-cta, nep-gth, nep-mic and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (46)

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Journal Article: Information Design (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:esedps:256

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