用多智能体强化学习模拟独立交易者如何自发提升市场流动性。
Multiagent Reinforcement Learning for Liquidity Games
- 设计差分奖励机制，让各交易者自主追求流动性最大化。
- 无需协调或串谋，个体理性行为自然促进整体市场效率。
- 适合研究市场设计与金融自组织系统的学者参考。
将流动性博弈（Liquidity Games）与理性蜂群（Rational Swarms）结合，构建一个由独立交易者组成的蜂群模型，其集体目标是提升市场流动性。通过在马尔可夫团队博弈框架中引入差分奖励机制，证明个体追求自身流动性最大化的策略，能有效推动整体市场流动性提升，而无需任何集中协调或串谋。该金融蜂群模型为双边资产市场中理性、独立的参与者如何同时实现个人盈利与市场整体效率提供了理论支持。
原文摘要 · Abstract (English)
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.
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