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Information processing for a finite resource allocation mechanism (*)

Author

Listed:
  • James C. Moore

    (Krannert Graduate School of Management, Purdue University, W. Lafayette, IN 47907, USA)

  • H. Raghav Rao

    (Jacobs Management Center, SUNY at Buffalo, Buffalo, NY 14260, USA)

  • Andrew B. Whinston

    (Graduate School of Business, University of Texas at Austin, Austin, TX 78712, USA)

Abstract

We deal with an `organization' having m individual agents (the `agents') and one central agent or agency (the `coordinator'), and the problem of allocating a fixed supply of n commodities among the agents. The coordinator wishes to distribute the commodities in such a way as to maximize an objective function whose arguments are functions known (implicitly) to the individual agents, but not to the coordinator. The more information the coordinator obtains about the individual agents' functions, the better the allocation decision he or she can make; however, gaining such information is costly, so that the coordinator must balance the potential gain against the cost of acquiring additional information. We find a `satisficing' solution to this problem which is computable and which terminates in a finite number of steps.

Suggested Citation

  • James C. Moore & H. Raghav Rao & Andrew B. Whinston, 1996. "Information processing for a finite resource allocation mechanism (*)," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 8(2), pages 267-290.
    • Handle: RePEc:spr:joecth:v:8:y:1996:i:2:p:267-290
    Note: Received: April 26, 1993; revised version August 1, 1995
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    Cited by:

    1. James Moore, 2010. "On aggregation and welfare analysis," Review of Economic Design, Springer;Society for Economic Design, vol. 14(1), pages 95-129, March.
    2. J. C. Moore & H. Raghav Rao & A. B. Whinston, 1997. "Experimental Strategies for Preference Information Acquisition: A Lattice Path Treatment," Group Decision and Negotiation, Springer, vol. 6(2), pages 139-158, March.

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