Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theoretical analysis of the $\textit{trust game}$, the canonical task for studying trust in behavioral and brain sciences, along with simulation results supporting our analysis. Specifically, leveraging reinforcement learning (RL) to train our AI agents, we systematically investigate learning trust under various parameterizations of this task. Our theoretical analysis, corroborated by the simulations results presented, provides a mathematical basis for the emergence of trust in the trust game.

翻译：信任被广泛视为人类道德的基石，塑造了人类社会互动的诸多方面。本研究对$\textit{信任博弈}$——行为与脑科学中研究信任的经典任务——进行了理论分析，并辅以支持该分析的仿真结果。具体而言，我们利用强化学习（RL）训练AI智能体，系统探究了该任务在不同参数化设置下的信任学习过程。经仿真结果验证的理论分析，为信任博弈中信任涌现提供了数学基础。