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• W2181585890 abstract "A presumed benefit of group decision-making is to select the best alternative by aggregating privately dispersed information. In reality, people often learn what to make of their private information based on previous experience. When the consequences of alternatives that were not chosen in the past (i.e. counterfactuals) are not observed, learning takes place from a biased sample. We investigate the extent to which information aggregation is precluded in such a learning environment. We apply the notion of a behavioral equilibrium (Esponda, 2008) to a benchmark voting game in order to formalize the assumption that players fail to account for selection bias. We present a dynamic framework that provides explicit learning foundations for our solution concept and clarifies the nature of the selection problem and our behavioral assumptions. We then characterize equilibrium in games with a large number of players, provide necessary and sucient conditions for information to be aggregated (and therefore for biases to be inconsequential in large games), and characterize optimal voting rules. Our results provide a more nuanced view of the benefits of using group decision-making for the purpose of information aggregation." @default.
• W2181585890 created "2016-06-24" @default.
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• W2181585890 date "2010-01-01" @default.
• W2181585890 modified "2023-09-27" @default.
• W2181585890 title "Information aggregation, learning, and non-strategic behavior in voting environments" @default.
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• W2181585890 hasPublicationYear "2010" @default.
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