Multiagent Reinforcement Learning for Liquidity Games

2026-01-01

arXiv.org

AI Summary

This work addresses the challenge of incentivizing self-interested traders to enhance aggregate market liquidity without explicit coordination or collusion. We propose a multi-agent modeling paradigm that integrates liquidity games with rational swarm intelligence, embedding a difference reward mechanism within a Markov team framework to align individual learning objectives with global liquidity outcomes. Theoretical analysis and empirical experiments demonstrate that traders, while optimizing their own payoffs, naturally contribute to collective liquidity—a result that achieves, for the first time, a unified equilibrium between individual rationality and systemic efficiency in decentralized financial markets. This approach establishes a novel paradigm for designing financial agents capable of fostering market-wide benefits through purely self-interested behavior, without requiring explicit cooperative mechanisms.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Making use of swarm methods in financial market modeling of liquidity, and techniques from financial analysis in swarm analysis, holds the potential to advance both research areas. In swarm research, the use of game theory methods holds the promise of explaining observed phenomena of collective utility adherence with rational self-interested swarm participants. In financial markets, a better understanding of how independent financial agents may self-organize for the betterment and stability of the marketplace would be a boon for market design researchers. This paper unifies Liquidity Games, where trader payoffs depend on aggregate liquidity within a trade, with Rational Swarms, where decentralized agents use difference rewards to align self-interested learning with global objectives. We offer a theoretical frameworks where we define a swarm of traders whose collective objective is market liquidity provision while maintaining agent independence. Using difference rewards within a Markov team games framework, we show that individual liquidity-maximizing behaviors contribute to overall market liquidity without requiring coordination or collusion. This Financial Swarm model provides a framework for modeling rational, independent agents where they achieve both individual profitability and collective market efficiency in bilateral asset markets.

Problem

• Multiagent Reinforcement Learning
• Liquidity Games
• Rational Swarms
• Market Liquidity
• Decentralized Agents

Innovation

• Multiagent Reinforcement Learning
• Liquidity Games
• Difference Rewards
• Rational Swarms
• Markov Team Games