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A Personalized BDM Mechanism for Efficient Market Intervention Experiments

Imanol Arrieta Ibarra,J. Ugander

Published 2018 in ACM Conference on Economics and Computation

ABSTRACT

The BDM mechanism, introduced by Becker, DeGroot, and Marschack in the 1960's, employs a second-price auction against a random bidder to elicit the willingness to pay of a consumer. The BDM mechanism has been recently used as a treatment assignment mechanism in order to estimate the treatment effects of policy interventions while simultaneously measuring the demand for the intervention. In this work, we develop a personalized extension of the classic BDM mechanism, using modern machine learning algorithms to predict an individual's willingness to pay and personalize the "random bidder" based on covariates associated with each individual. We show through a mock experiment on Amazon Mechanical Turk that our personalized BDM mechanism results in a lower cost for the experimenter, provides better balance over covariates that are correlated with both the outcome and willingness to pay, and eliminates biases induced by ad-hoc boundaries in the classic BDM algorithm. We expect our mechanism to be of use for policy evaluation and market intervention experiments, in particular in development economics. Personalization can provide more efficient resource allocation when running experiments while maintaining statistical correctness.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    ACM Conference on Economics and Computation

  • Publication date

    2018-06-11

  • Fields of study

    Computer Science, Economics

  • Identifiers
    DOI 10.1145/3219166.3219220
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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