From Necklaces to Coalitions: Fair and Self-Interested Distribution of Coalition Value Calculations

TL;DR

This paper introduces N-DCA, a communication-free distributed algorithm for coalition value calculation that guarantees fairness, load balancing, and self-interest, with proven mathematical properties and competitive empirical performance.

Contribution

It presents the N-DCA algorithm, the first distributed coalition calculation method with formal load-balance guarantees and no inter-agent communication.

Findings

N-DCA guarantees balanced load and self-interest.

Empirical results show N-DCA is scalable and memory-efficient.

Compared to DCVC, N-DCA has similar performance with better scalability.

Abstract

A key challenge in distributed coalition formation within characteristic function games is determining how to allocate the calculation of coalition values across a set of agents. The number of possible coalitions grows exponentially with the number of agents, and existing distributed approaches may produce uneven or redundant allocations, or assign coalitions to agents that are not themselves members. In this article, we present the \emph{Necklace-based Distributed Coalition Algorithm} (N-DCA), a communication-free algorithm in which each agent independently determines its own coalition value calculation allocation using only its identifier and the total number of agents. The approach builds on the notion of Increment Arrays (IAs), for which we develop a complete mathematical framework: equivalence classes under circular shifts, periodic IAs, and a rotated designation scheme with formal load-balance guarantees (tight bounds). We establish a bijection between canonical representative IAs and two-colour combinatorial necklaces, enabling the use of efficient necklace generation algorithms to enumerate allocations in constant amortised time. N-DCA is, to the best of our knowledge, the only distributed coalition value calculation algorithm for unrestricted characteristic function games to provably satisfy five desirable properties: no inter-agent communication, equitable allocation, no redundancy, balanced load, and self-interest. An empirical evaluation against DCVC (Rahwan and Jennings 2007) demonstrates that, although DCVC is faster by a constant factor, this difference becomes negligible under realistic characteristic-function evaluation costs, while N-DCA offers advantages in working memory, scalability, and the self-interest guarantee.