As interactions between humans and AI become more prevalent, it is critical to have better predictors of human behavior in these interactions. We investigated how changes in the AI's adaptive algorithm impact behavior predictions in two-player continuous games. In our experiments, the AI adapted its actions using a gradient descent algorithm under different adaptation rates while human participants were provided cost feedback. The cost feedback was provided by one of two types of visual displays: (a) cost at the current joint action vector, or (b) cost in a local neighborhood of the current joint action vector. Our results demonstrate that AI adaptation rate can significantly affect human behavior, having the ability to shift the outcome between two game theoretic equilibrium. We observed that slow adaptation rates shift the outcome towards the Nash equilibrium, while fast rates shift the outcome towards the human-led Stackelberg equilibrium. The addition of localized cost information had the effect of shifting outcomes towards Nash, compared to the outcomes from cost information at only the current joint action vector. Future work will investigate other effects that influence the convergence of gradient descent games.

翻译：随着人类与人工智能之间的交互日益普遍，在这些交互中建立更准确的人类行为预测因子变得至关重要。本研究探究了人工智能自适应算法的变化如何影响双人连续博弈中的行为预测。在我们的实验中，人工智能采用梯度下降算法在不同适应速率下调整其行动，同时向人类参与者提供成本反馈。成本反馈通过以下两种视觉显示方式之一呈现：(a) 当前联合行动向量处的成本，或(b) 当前联合行动向量局部邻域内的成本。我们的研究结果表明，人工智能的适应速率能显著影响人类行为，并能够使博弈结果在两种博弈论均衡之间转移。我们观察到，较慢的适应速率会使结果趋向纳什均衡，而较快的速率则会使结果趋向人类主导的斯塔克尔伯格均衡。与仅显示当前联合行动向量成本信息的情况相比，局部成本信息的加入具有使结果向纳什均衡偏移的效果。未来工作将研究影响梯度下降博弈收敛的其他因素。