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Refining Nash Equilibrium by Bayesian Iterative Conjectures Approach

Authors: Jimmy Teng;

Refining Nash Equilibrium by Bayesian Iterative Conjectures Approach

Abstract

Bayesian equilibrium by iterative conjectures (BEIC) analyzes games with players forming their conjectures of what other players will do iteratively starting with first order uninformative conjectures (or prior distribution functions) and updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved, and this process of conjectures formation and updating itself is a common knowledge. The BEIC is a refinement of Nash equilibrium and narrows down the set of equilibrium, normally to a unique one. The paper compares how BEIC fares as a refinement of Nash Equilibrium with other refinements, including payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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