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File:
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[pdf]
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Title:
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Auctions with Artificial Adaptive Agents
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Authors:
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James Andreoni and John H. Miller
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Key Words:
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Auctions, Artificial Adaptive Agents, Genetic Algorithm, Bidding Behavior, Learning
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Abstract:
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Experiments on auctions find that subjects make systematic bidding errors that cannot
be explained within the context of Nash equilibrium bidding models. Experimenters and
others have conjectured that learning by subjects could lead to errors consistent with
those observed. Here, we create and analyze a model of adaptive learning and demonstrate
that such a model can capture the bidding patterns evident among human subjects in
experimental auctions. Moreover, our model provides a variety of insights into the
nature of learning across different auction institutions.
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