As AI increasingly integrates with human decision-making, we must carefully consider interactions between the two. In particular, current approaches focus on optimizing individual agent actions but often overlook the nuances of collective intelligence. Group dynamics might require that one agent (e.g., the AI system) compensate for biases and errors in another agent (e.g., the human), but this compensation should be carefully developed. We provide a theoretical framework for algorithmic compensation that synthesizes game theory and reinforcement learning principles to demonstrate the natural emergence of deceptive outcomes from the continuous learning dynamics of agents. We provide simulation results involving Markov Decision Processes (MDP) learning to interact. This work then underpins our ethical analysis of the conditions in which AI agents should adapt to biases and behaviors of other agents in dynamic and complex decision-making environments. Overall, our approach addresses the nuanced role of strategic deception of humans, challenging previous assumptions about its detrimental effects. We assert that compensation for others' biases can enhance coordination and ethical alignment: strategic deception, when ethically managed, can positively shape human-AI interactions.

翻译：随着人工智能日益融入人类决策过程，我们必须审慎考量二者间的交互机制。当前研究主要聚焦于优化个体智能体行为，却往往忽视了集体智能的微妙特征。群体动态可能要求某一智能体（如AI系统）补偿另一智能体（如人类）的偏差与错误，但这种补偿机制需经过精心设计。我们提出了一个融合博弈论与强化学习原理的算法补偿理论框架，揭示了智能体持续学习动态中欺骗性结果的自然涌现机制。我们提供了涉及马尔可夫决策过程（MDP）交互学习的仿真结果。本研究进而支撑了我们对AI智能体在动态复杂决策环境中应如何适应其他智能体偏差与行为的伦理分析。总体而言，我们的方法探讨了人类策略性欺骗的微妙角色，挑战了此前关于其负面影响的假设。我们主张：对他者偏差的补偿能增强协调性与伦理一致性——若策略性欺骗获得伦理约束，即可积极塑造人机交互关系。