Negotiating Socially Optimal Allocations of Resources

A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory to assess the social welfare of such an agent society. In this paper, we study an abstract negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then analyse how these deals affect social welfare for different instances of the basic framework and different interpretations of the concept of social welfare itself. In particular, we show how certain classes of deals are both sufficient and necessary to guarantee that a socially optimal allocation of resources will be reached eventually

Tractability results for automatic contracting

Abstract. Automated negotiation techniques have received considerable attention over the past decade, and much progress has been made in developing negotiation protocols and strategies for use by software agents. However, comparatively little effort has been devoted to understanding the computational complexity of such protocols and strategies. Building on the work of Rosenschein, Zlotkin, and Sandholm, we consider the complexity of negotiation in a particular class of task-oriented domains. Specifically, we consider scenarios in which agents negotiate to achieve a more favourable redistribution of tasks amongst themselves, where the tasks involve visiting nodes in a graph. Focussing on a particular representation of the domain (as a spanning tree), we establish a number of complexity results pertaining to the complexity of negotiation in this scenario, with our main result to the effect that the problem of deciding whether a given deal could be reached by a chain of rational proposals is tractable.