Learning Efficiency of Multiagent Information Structures
研究了在协调博弈中，哪种信息结构能更有效地消除一阶和高阶不确定性，从而促进有效博弈。发现学习效率与一阶不确定性消失速度一致，且信号足够多时，学习效率高的信息结构带来更高福利。
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.