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Until recently, two big impediments limited what research economists could learn about the world with the powerful methods that mathematicians and statisticians, starting in the early nineteenth century, developed to recognize and interpret patterns in noisy data: Data sets were small and costly, and computers were slow and expensive.The mathematician John von Neumann defined a game as (1) a list of players; (2) a list of actions available to each player; (3) a list of how payoffs accruing to each player depend on the actions of all players; and (4) a timing protocol that tells who chooses what when.Like Feynman's metaphorical physicist, our task is to infer a "game" -- which for economists is the structure of a market or system of markets -- from observed data. Thus, "structural model builders" in economics seek to infer from historical patterns of behavior a set of invariant parameters for hypothetical (often historically unprecedented) situations in which a government or regulator follows a new set of rules. "Structural models" seek such invariant parameters in order to help regulators and market designers understand and predict data patterns under historically unprecedented situations.
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