SMU SOE Online Seminar (Oct 15, 2020, 2.30pm-4.00pm): Robust Prediction in Games with Uncertain Parameters

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TOPIC:

ROBUST PREDICTION IN GAMES WITH UNCERTAIN PARAMETERS

ABSTRACT We consider a game with an uncertain parameter, whose support is common knowledge but there is no further common-knowledge assumption: Each player may possess any (first-order) belief about the parameter value in the support, each player may possess any (second-order) belief about the others' beliefs which focus on the support, and so on. 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 truth-telling bias or loss-aversion). An analyst who desires to make a prediction in such a situation faces the issue that he does not know the players' information structure (except for the common knowledge of the support). 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 (i) an action profile set is a robust prediction if and only if it is an incomplete-information version of a CURB set of Basu and Weibull (1991), and (ii) 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. Click here to view the CV.		This seminar will be held via Zoom. A confirmation email with the Zoom details will be sent to the registered email by 14 October 2020.
		PRESENTER Takuro Yamashita Toulouse School of Economics
		RESEARCH FIELDS Microeconomics Industrial Organization
		DATE: 15 October 2020 (Thursday)
		TIME: 2.30pm - 4.00pm

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