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Information Independence and Common Knowledge
Author(s) -
Gossner Olivier,
Kalai Ehud,
Weber Robert
Publication year - 2009
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta7469
Subject(s) - common knowledge (logic) , conditional independence , variable (mathematics) , independence (probability theory) , bayesian network , bayesian probability , econometrics , mathematical economics , computer science , mathematics , artificial intelligence , statistics , epistemic modal logic , multimodal logic , description logic , mathematical analysis
In Bayesian environments with private information, as described by the types of Harsanyi, how can types of agents be (statistically) disassociated from each other and how are such disassociations reflected in the agents' knowledge structure? Conditions studied are (i) subjective independence (the opponents' types are independent conditional on one's own) and (ii) type disassociation under common knowledge (the agents' types are independent, conditional on some common‐knowledge variable). Subjective independence is motivated by its implications in Bayesian games and in studies of equilibrium concepts. We find that a variable that disassociates types is more informative than any common‐knowledge variable. With three or more agents, conditions (i) and (ii) are equivalent. They also imply that any variable which is common knowledge to two agents is common knowledge to all, and imply the existence of a unique common‐knowledge variable that disassociates types, which is the one defined by Aumann.