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Incentives for Efficient Black-box Resource Allocation

Alfredo Garcia, Professor, Industrial and Systems Engineering, University of Florida

We consider the problem of efficiently allocating the resources of a “black-box” (i.e. a system whose capacity can only be determined via a computational oracle or simulation) to a set of users who have private information on their valuation for capacity. Users must report information to the manager of resources so that an optimal allocation of available capacity can be identified. Since there is no explicit knowledge of capacity constraints, a “black-box” model has to be consulted to determine the feasibility of a given allocation profile. Depending upon how the capacity is allocated, consumers may misrepresent their reported information for their own gain thus potentially inducing an inefficient allocation. To prevent manipulation, allocation outcomes must be incentive compatible meaning that it is in the best interest of each user to report input information truthfully. We propose an iterative mechanism design in which users self-schedule onto the available resources and incentive payments are adjusted so as to ensure incentive compatibility and that an efficient allocation of capacity is identified. We consider applications in spectrum sharing for a variety of signaling technologies and demand response in electricity markets.


Dr. Garcia received an undergraduate degree in electrical engineering from the Universidad de los Andes, Colombia, in 1991, the Diplome d'Etudes Approfondies D.E.A. in control systems from the Université Paul Sabatier, Toulouse, France, in 1992, and the Ph.D. degree in operations research from the University of Michigan, Ann Arbor, in 1997. During 1997-2001 he worked in the development and application of optimization and game theoretic models for the evaluation of regulatory policy for electricity and natural gas in Latin America and the US. During 2001-2014 he was a member of the faculty, at the University of Virginia. His research interests include game theory and dynamic optimization with applications in power and communication networks.