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by Rinaldo A. Jose, Rinaldo A. Jose, Patrick T. Harker, Patrick T. Harker, Lyle, Lyle, H. Ungar, H. Ungar
http://opim.wharton.upenn.edu/~harker/cef97.pdf
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Abstract:
Abstract. The growing interest in distributed and agent-based computing has drawn researchers to auctions and market-based approaches for optimally coordinating self-interested agents that is, pricing system resources so that the amounts the agents buy correspond to what a central planner would optimally select. Typically, researchers use linear prices in theoretical and applied auction work. However, linear prices fail to coordinate certain classes of agents for example, agents which are locally constrained. Jennergren noticed this failure in linear programs (LPs) and suggested a form of augmented pricing as a solution. Although his proof only applies to agents described by LPs, augmented pricing can still be e ective when agents are described by nonlinear programs (NLPs). In this paper, we extend Jennergren's result by showing that augmented prices can optimally coordinate agents if they have concave objectives and compact, convex feasible sets; we illustrate our points using numerical examples. The authors express their gratitude to the NSF (SBR 9708965) for partial support of this project.
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