不完全信任下的信念更新
Belief Updating without Complete Trust
- Boston University（波士顿大学）
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将不完全信任的非贝叶斯智能体表征为对信号信息结构存在主观不确定性的贝叶斯主体，利用射影几何基本定理证明该表征，并分析信任缺失对更新偏差的合理化作用。
AI中文摘要：
我们将非贝叶斯智能体表征为不完全信任所接收信息的主体。完全信任的行为表现体现在贝叶斯更新的齐次性：若通过缩小似然向量使信号变得极为罕见，后验信念不会改变。我们表明，仅放弃该属性并保留贝叶斯其他所有行为属性，会得到一种独特表征：该主体仍是贝叶斯的，但对产生信号的信息结构存在主观不确定性。该表征结果通过射影几何基本定理证明。我们分析了各类更新偏差如何因信任缺失而合理化。
英文摘要：
We represent a non-Bayesian agent as one who does not completely trust the information they receive. The behavioral expression of complete trust lies in a homogeneity property of Bayesian updating: posterior beliefs do not change if a signal is made arbitrarily rare by scaling down its likelihood vector. We show that simply dropping this property and retaining all other Bayesian behavioral properties yields a unique representation where the agent is still Bayesian but has subjective uncertainty over the information structure generating the signal. The representation result is proved using the Fundamental Theorem of Projective Geometry. We analyze how various updating biases may be rationalized by a lack of trust.
