流动性游戏中的多智能体强化学习
Multiagent Reinforcement Learning for Liquidity Games
AI总结：
本文提出一种多智能体强化学习框架，用于流动性游戏，通过差异奖励实现个体自利行为与集体市场效率的统一。
摘要：
在金融市场流动性建模中利用群体方法，并将金融分析技术引入群体分析，有潜力推动两个研究领域的发展。在群体研究中，使用博弈论方法有望解释群体效用遵循理性自利群体参与者所观察到的现象。在金融市场中，更好地理解独立金融代理如何自我组织以改善和稳定市场环境，将对市场设计研究者有所帮助。本文将流动性游戏与理性群体统一起来，其中交易者的收益取决于交易中的总体流动性，而理性群体中，去中心化的代理使用差异奖励将自利学习与全球目标对齐。我们提供了一个理论框架，定义了一个群体交易者，其集体目标是提供市场流动性，同时保持代理独立性。在马尔可夫团队博弈框架中使用差异奖励，我们证明了个体流动性最大化行为有助于整体市场流动性，而无需协调或 collusion。这种金融群体模型为建模理性、独立的代理提供了一个框架，这些代理在双边资产市场中实现了个人盈利和集体市场效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
