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Learning Efficiency of Multiagent Information Structures
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AI
DOI：10.1086/725171.png)
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摘要
En 中文
Which information structures are more effective at eliminating first- and higher-order uncertainty and hence at facilitating efficient play in coordination games? We consider a learning setting where players observe many private signals about the state. First, we characterize multiagent learning efficiency, that is, the rate at which players approximate common knowledge. We find that this coincides with the rate at which first-order uncertainty disappears, as higher-order uncertainty vanishes faster than first-order uncertainty. Second, we show that with enough signal draws, information structures with higher learning efficiency induce higher equilibrium welfare. We highlight information design implications for games in data-rich environments.
Keyword:
LARGE DEMAND
SOCIAL VALUE
COMMON
EQUILIBRIUM
ROBUSTNESS
GAMES
LAW
期刊
IF:
6.3
论文数:
2.6K
被引数:
3.2W
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引用论文
GENERALIZED BELIEF OPERATOR AND ROBUSTNESS IN BINARY-ACTION SUPERMODULAR GAMES.png)
ECONOMETRICA
ECONOMETRICAIF7.1
PREAI
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