Multi-agent market simulation is an effective tool to investigate the impact
of various trading strategies in financial markets. One way of designing a
trading agent in simulated markets is through reinforcement learning where the
agent is trained to optimize its cumulative rewards (e.g., maximizing profits,
minimizing risk, improving equitability). While the agent learns a rational
policy that optimizes the reward function, in reality, human investors are
sub-rational with their decisions often differing from the optimal. In this
work, we model human sub-rationality as resulting from two possible causes:
psychological bias and computational limitation. We first examine the
relationship between investor profits and their degree of sub-rationality, and
create hand-crafted market scenarios to intuitively explain the sub-rational
human behaviors. Through experiments, we show that our models successfully
capture human sub-rationality as observed in the behavioral finance literature.
We also examine the impact of sub-rational human investors on market
observables such as traded volumes, spread and volatility. We believe our work
will benefit research in behavioral finance and provide a better understanding
of human trading behavior.

本文首先以强化学习为基础，设计一个模型来模拟多人市场模拟中的交易代理。然后，将人类的次理性分为两种类型：心理偏见和计算能力问题，探讨了人类次理性对交易市场不同方面的影响。作者认为本研究将有助于进一步了解人类交易行为和行为金融学领域的发展。