Common Priors and Markov Chains
The type function of an agent, in a type space, associates with each state a probability distribution on the type space. Thus, a type function can be considered as a Markov chain on the state space. A common prior for the space turns out to be a probability distribution which is invariant under the type functions of all agents. Using the Markovian structure of type spaces we show that a necessary and sufficient condition for the existence of a common prior is that for each random variable it is common knowledge that all its joint averagings converge to the same value.
|Date of creation:||21 Oct 1996|
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|Note:||Type of Document - postscript; prepared on unix; pages: 8|
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- Harsanyi, John C., 1994.
"Games with Incomplete Information,"
Nobel Prize in Economics documents
1994-1, Nobel Prize Committee.
- Giacomo Bonanno & Klaus Nehring, .
"Fundamental Agreement: A New Foundation For The Harsanyi Doctrine,"
Department of Economics
96-02, California Davis - Department of Economics.
- Klaus Nehring & Giacomo Bonanno & Massimiliano Marcellino, 2003. "Fundamental Agreement: A new foundation for the Harsanyi Doctrine," Working Papers 962, University of California, Davis, Department of Economics.
- Morris, Stephen, 1994. "Trade with Heterogeneous Prior Beliefs and Asymmetric Information," Econometrica, Econometric Society, vol. 62(6), pages 1327-47, November.
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