Noncooperative Consensus via a Trading-based Auction
Jaehan Im, Filippos Fotiadis, Daniel Delahaye, Ufuk Topcu, and David Fridovich-Keil

TL;DR
This paper introduces TACo, a decentralized trading auction algorithm that enables self-interested agents in multi-agent systems to reach consensus efficiently without direct communication or revealing private info, ensuring fairness and cost minimization.
Contribution
The paper presents TACo, a novel decentralized auction-based algorithm for noncooperative consensus that guarantees termination and improves fairness and cost efficiency.
Findings
TACo guarantees convergence within a bounded number of steps.
TACo achieves median cost minimization across agents.
TACo allocates resources more fairly than baseline methods.
Abstract
Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, when agents act in their own self-interest, they may prefer different choices among multiple feasible outcomes, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across…
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Taxonomy
TopicsAuction Theory and Applications · Economic theories and models · Game Theory and Applications
