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<p>The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. We use our framework to show how identification can be strengthened as one imposes the assumptions behind more restrictive forms of Bayesian learning.</p>

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Rationalizable learning

  • 期刊论文

  • 发表日期：

    2024年12月31日

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  • Andrew Caplin,
  • Daniel Martin,
  • Philip Marx

摘要

The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. We use our framework to show how identification can be strengthened as one imposes the assumptions behind more restrictive forms of Bayesian learning.