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.

翻译：将群体方法应用于金融市场流动性建模，并将金融分析技术引入群体分析，有望推动这两个研究领域的发展。在群体研究中，博弈论方法的应用有望解释理性自利群体参与者所表现出的集体效用遵循现象。在金融市场中，若能够更好地理解独立金融主体如何通过自组织促进市场改善与稳定，将为市场设计研究者带来重要启示。本文融合了流动性博弈（交易者收益取决于交易中的总流动性）与理性群体（分散智能体使用差分奖励将自利学习与全局目标对齐）两个概念。我们提出了一个理论框架，定义了一个交易者群体，其集体目标是提供市场流动性，同时保持个体独立性。通过在马尔可夫团队博弈框架中应用差分奖励，我们证明个体流动性最大化行为能够促进整体市场流动性，且无需协调或共谋。这一金融群体模型为建模理性独立主体提供了框架，在双边资产市场中实现了个体盈利性与集体市场效率的双重目标。