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Eventi del

24 2020 12:45 - 14:00

Theory and Experiments Series

Robust Prediction in Games with Uncertain Parameters

Takuro Yamashita, Toulouse School of Economics


We consider games with uncertain parameters, where each player may possess any (possibly higher-order) belief about the parameter values. For example, firms may agree on a set of demand functions based on publicly available data, but not a single demand function. As another example, bidders in a first-price auction may imagine that rival bidders are potentially biased due to some behavioral reasons (such as truthful-bidding bias). An analyst who desires to make a theoretical prediction does not know the players' information structure. We define a robust prediction as a set of action profiles such that, given any information structure among the players, there is an equilibrium given that information structure whose equilibrium action profiles are in this set. We show that there is a canonical type space whose equilibrium action profile set is a robust prediction. We argue that the "equilibrium selection'' nature of our robust prediction concept may be advantageous in some contexts, such as when the analyst has some idea about "reasonable'' equilibria in the game of interest, or when the goal is mechanism design robust to parameter uncertainty.


by invitation: for information or to receive the invitation link contact erika.somma@unibocconi.it