AI 中文总结
该研究提出动态问题设计方法，结合贝叶斯估计、粒子滤波与ε-贪心策略，高效估计人类群体偏好，其搜索效率优于随机搜索，经数值模拟验证有效。
AI 中文摘要
本文通过基于受访者的回答动态调整问题，解决高效估计人类群体偏好的问题。为此，我们提出并解决两个子问题：偏好估计与问题设计。首先，针对偏好估计，我们对受访者的偏好进行建模，并使用贝叶斯估计结合粒子滤波作为计算高效的近似方法，该子问题的主要理论贡献是用信息论方法分析偏好估计误差，推导了误差的理论下界。其次，针对问题设计，我们将设计问题表述为期望信息增益最大化问题，并采用ε-贪心策略以计算高效的方式求解该问题，我们从理论上分析了该方法的搜索效率，证明其比随机搜索效率更高。最后，我们通过数值模拟验证了所提方法的有效性。
英文摘要
This paper addresses the problem of efficiently estimating aggregate human preferences by dynamically adapting questions based on respondents'answers. To this end, we formulate and address two sub-problems: preference estimation and question design. First, regarding preference estimation, we model respondents' preferences and estimate them using Bayesian estimation, employing a particle filter as a computationally efficient approximation. The main theoretical contribution to this sub-problem is to analyze the preference estimation error using an information-theoretic approach, deriving a theoretical lower bound for the error. Second, regarding question design, we formulate the design problem as an Expected Information Gain maximization problem and employ an epsilon-greedy strategy to solve the problem in a computationally efficient way. We theoretically analyze the search efficiency of the approach, demonstrating that it achieves higher efficiency than a random search. Finally, we verify the effectiveness of the proposed method through numerical simulations.
