Learning in a Black Box ∗
نویسندگان
چکیده
Many interactive environments can be represented as games, but they are so large and complex that individual players are mostly in the dark about others’ actions and the payoff structure. This paper analyzes learning behavior in such ‘black box’ environments, where players’ only source of information is their own history of actions taken and payoffs received. The context of our analysis are decisions in voluntary contributions games with high and low rates of return. We identify four robust features of the players’ learning dynamics: search volatility, search breadth, inertia, and directional bias. These features are present when players have no information about the game, and also when they have full information. JEL classifications: C70, C73, C91, D83, H41
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