发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究竞争模型下动机推理如何影响信念更新，通过实验发现非对称收益使最佳拟合更新者信念向偏好状态偏移约8个百分点，而贝叶斯更新者几乎不受影响，揭示了偏好驱动偏差的机制及其在政治与金融领域的启示。
AI 中文摘要
人们常常面临多种模型竞争解释同一观察结果的环境。本文考察了人们在此类环境中如何更新信念，以及对于与收益相关状态的偏好如何塑造模型选择与信念更新。本文首先构建了一个框架，其中偏好驱动的偏差扭曲了感知到的模型，对贝叶斯更新和最佳拟合更新的影响方式不同。在一项实验室实验中，大多数参与者被归类为贝叶斯更新者，他们对各模型取平均；而相当一部分少数派被归类为最佳拟合更新者，他们选择最符合观察信号的模型。参与者内部在对称收益与非对称收益条件之间的比较表明，非对称收益使报告信念向偏好状态偏移，尤其是在被归类为最佳拟合更新者的参与者中。相对于对称收益，非对称收益使最佳拟合更新者对偏好状态的报告信念增加约8个百分点，而对贝叶斯更新者的估计效应接近于零。这些发现有助于我们更好地理解基于模型的学习，并对政治极化与金融投资等领域具有启示意义，在这些领域中，竞争性叙事与强烈偏好往往并存。
英文摘要
People often face environments where multiple models compete to explain the same observations. This paper examines how people update beliefs in such settings and how preferences over payoff-relevant states shape model selection and belief updating. This paper first develops a framework where preference-driven bias distorts the perceived model, affecting Bayesian and best-fit updating differently. In a laboratory experiment, most participants are classified as Bayesian updaters, who average across models, while a substantial minority are classified as best-fit updaters, who select the model that best fits the observed signal. Within-participant comparisons between the symmetric payoff and asymmetric payoff conditions indicate that asymmetric payoffs shift reported beliefs toward the preferred state, particularly among participants classified as best-fit updaters. Relative to symmetric payoffs, asymmetric payoffs increase the reported belief of the preferred state by about 8 percentage points among best-fit updaters, while the estimated effect among Bayesian updaters is close to zero. These findings help us better understand model-based learning and have implications for domains such as political polarization and financial investment, where competing narratives and strong preferences often coexist.
