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Persuasion With Limited Data: A Case-Based Approach

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Listed:
  • Shiri Alon
  • Sarah Auster
  • Gabi Gayer
  • Stefania Minardi

Abstract

to adopt a new action. The receiver assesses the profitability of adopting the action by following a classical statistics approach: she forms an estimate via the similarity-weighted empirical frequencies of outcomes in past cases, sharing some attributes with the problem at hand. The sender has control over the characteristics of the sampled cases and discloses the outcomes of his study truthfully. We characterize the sender’s optimal sampling strategy as the outcome of a greedy algorithm. The sender provides more relevant data—consisting of observations sharing relatively more characteristics with the current problem—when the sampling capacity is low, when a large amount of initial public data is available, and when the estimated benefit of adoption according to this public data is low. Competition between senders curbs incentives for biasing the receiver’s estimate and leads to more balanced datasets.

Suggested Citation

  • Shiri Alon & Sarah Auster & Gabi Gayer & Stefania Minardi, 2023. "Persuasion With Limited Data: A Case-Based Approach," CRC TR 224 Discussion Paper Series crctr224_2023_443, University of Bonn and University of Mannheim, Germany.
    • Handle: RePEc:bon:boncrc:crctr224_2023_443
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    File URL: https://www.crctr224.de/research/discussion-papers/archive/dp443
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    References listed on IDEAS

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    More about this item

    Keywords

    Persuasion; case-based inference; similarity-weighted frequencies;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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