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  Incomplete-Information Games in Large Populations with Anonymity

Hellwig, M. F. (2020). Incomplete-Information Games in Large Populations with Anonymity.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-DD32-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-DD33-8
Genre: Paper

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 Creators:
Hellwig, Martin F.1, Author              
Affiliations:
1Max Planck Institute for Research on Collective Goods, Max Planck Society, ou_2173688              

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Free keywords: Incomplete-information games, large populations, belief functions, common priors, exchangeability, conditional independence, conditional exact law of large numbers
 JEL: C70 - General
 JEL: D82 - Asymmetric and Private Information; Mechanism Design
 JEL: D83 - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
 Abstract: The paper provides theoretical foundations for models of strategic interdependence under uncertainty that have a continuum of agents and a decomposition of uncertainty into a macro component and an agent-specific micro component, with a law of large numbers for the latter. The decomposition of uncertainty is implied by a condition of exchangeability of agents' types, which is imposed equivalently imposed at the level of the prior or at the level of beliefs, i.e., posteriors. Under an additional condition of anonymity in payoffs, agents' behaviors are fully determined by their macro beliefs about the cross-section distribution of types and by the cross-section distribution of other agents' strategies. Any probability distribution over cross-section distributions of types is admissible, but not every macro belief function is compatible with a common prior.

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 Dates: 2020-08-18
 Publication Status: Published online
 Pages: -
 Publishing info: Bonn : Max Planck Institute for Research on Collective Goods, Discussion Paper 2020/20
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

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