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Pricing for customers with probabilistic valuations as a continuous knapsack problem

Michael Benisch,James Andrews,N. Sadeh

Published 2006 in International Conference on Evolutionary Computation

ABSTRACT

In this paper, we examine the problem of choosing discriminatory prices for customers with probabilistic valuations and a seller with indistinguishable copies of a good. We show that under certain assumptions this problem can be reduced to the continuous knapsack problem (CKP). We present a new fast ε-optimal algorithm for solving CKP instances with asymmetric concave reward functions. We also show that our algorithm can be extended beyond the CKP setting to handle pricing problems with overlapping goods (e.g.goods with common components or common resource requirements), rather than indistinguishable goods.We provide a framework for learning distributions over customer valuations from historical data that are accurate and compatible with our CKP algorithm, and we validate our techniques with experiments on pricing instances derived from the Trading Agent Competition in Supply Chain Management (TAC SCM). Our results confirm that our algorithm converges to an ε-optimal solution more quickly in practice than an adaptation of a previously proposed greedy heuristic.

PUBLICATION RECORD

  • Publication year

    2006

  • Venue

    International Conference on Evolutionary Computation

  • Publication date

    2006-08-13

  • Fields of study

    Mathematics, Computer Science, Economics

  • Identifiers
    DOI 10.1145/1151454.1151475
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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