收益与感知在具有Q学习的N人信任博弈中调节环境反馈

中文摘要 AI 辅助

信任通过学习得以发展，而集体行为可以改变后续决策所处的环境。强化学习模型描述了适应性，生态进化模型描述了行为与环境之间的反馈，但内生环境如何通过物质激励和感知状态改变信任仍不清楚。我们将一个由异质表格Q学习智能体组成的固定角色、两群体层次信任博弈与一个中心化的内生环境耦合。对于主要的反馈比较，我们从相同的学习状态初始化固定收益基线和反馈分支，以控制先前的学习。然后，我们比较当收益参数或观察到的环境层级变化时的响应。随着收益乘数因子增加，固定收益博弈中的成功信任从低水平跨越到较高的有限时域水平，由风险寻求型投资者引领。环境反馈在感知层级边界附近促进了信任，并在高回报运行点强化了其下降。在高回报运行点，收益反馈强化了学习驱动的信任下降。更强的驱动力和更长的时域加深了这一负面效应。与固定收益信任博弈相比，环境反馈将过去的集体结果带入未来的收益和观察到的状态。

英文摘要

Trust develops through learning, while collective behavior can alter the environment in which later decisions are made. Reinforcement-learning models describe adaptation, and eco-evolutionary models describe behavior-environment feedback, but how an endogenous environment changes trust through material incentives and perceived states remains unclear. We couple a fixed-role, two-population hierarchical trust game populated by heterogeneous tabular $Q$-learning agents to a centered endogenous environment. For the main feedback comparisons, we initialize the fixed-payoff baseline and feedback branches from the same learned state to control for prior learning. We then compare responses when payoff parameters or the observed environmental tier vary. As the payoff multiplication factor increased, successful trust in the fixed-payoff game crossed from a low to a higher finite-horizon level, led by risk-seeking investors. Environmental feedback promoted trust near perception-tier boundaries and reinforced its decline at the high-return operating point. At the high-return operating point, payoff feedback reinforced the learning-driven decline in trust. Stronger drive and longer horizons deepened this negative effect. Compared with a fixed-payoff trust game, environmental feedback carries past collective outcomes into future payoffs and observed states.

发表机构

• College of Mathematics and Computer, Guilin Normal College（桂林师范学院数学与计算机学院）
• College of Science, Northwest A&F University（西北农林科技大学理学院）

机构由 AI 辅助整理，请以论文原文为准。