把人类投资心理偏差融入强化学习，让算法更像真人交易
Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
- 在奖励函数和决策中引入过度自信与损失厌恶等心理偏差
- 模型表现出类似人类的交易行为，但风险调整后收益未显著提升
- 揭示了将心理因素纳入金融AI的核心挑战，适合研究行为金融与AI融合者
金融市场受人类行为影响，其决策常偏离理性，存在认知偏差。传统强化学习（RL）模型假设决策者为理性主体，可能忽略心理因素的影响。本研究将过度自信、损失厌恶等认知偏差引入强化学习框架，用于金融交易决策，假设此类模型能表现出类人交易行为，并获得优于标准RL代理的风险调整后收益。通过模拟与真实市场环境评估，尽管结果不明确或未达预期，但研究深入揭示了将人类心理特征融入强化学习所面临的挑战，为构建稳健的金融人工智能系统提供了宝贵经验。
原文摘要 · Abstract (English)
Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.
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