Auction Equilibrium Strategies for Task Allocation in Uncertain Environments

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Abstract

In this paper we address a model of self interested information agents competing to perform tasks. The agents are situated in an uncertain environment while different tasks dynamically arrive from a central manager. The agents differ in their capabilities to perform a task under different world states. Previous models concerning cooperative agents aiming for a joint goal are not applicable in such environments, since self interested agents have a motivation to deviate from the joint allocation strategy, in order to increase their own benefits. Given the allocation protocol set by the central manager, a stable solution, is a set of strategies, derived from an equilibrium where no agent can benefit from changing its strategy given the other agents’ strategies. Specifically we focus on a protocol in which, upon arrival of a new task, the central manager starts a reverse auction among the agents, and the agent who bids the lowest cost wins. We introduce the model, formulate its equations and suggest equilibrium strategies for the agents. By identifying specific characteristics of the equilibria, we manage to suggest an efficient algorithm for enhancing the agents’ calculation of the equilibrium strategies. A comparison with the central allocation mechanism, and the effect of environmental settings on the perceived equilibrium are given using several sample environments.