Feasible Joint Posterior Beliefs

贝叶斯更新在规格错误下的经验内容

The Empirical Content of Bayesian Updating under Misspecification

本文研究了在模型可能不准确的情况下，贝叶斯更新的一致性条件，指出后验分布需满足可数划分条件，并探讨了在有限状态空间和紧致状态空间下的不同限制，以及高斯环境下后验不确定性不超出先验的结论。

Posterior Probabilities: Nonmonotonicity, Asymptotic Rates, Log-Concavity, and Turán's Inequality

In the standard Bayesian framework data are assumed to be generated by a distribution parametrized by $θ$ in a parameter space $Θ$, over which a prior distribution $π$ is given. A Bayesian statistician quantifies the belief that the true parameter is $θ_{0}$ in $Θ$ by its posterior probability given the observed data. We investigate the behavior of the posterior belief in $θ_{0}$ when the data are generated under some parameter $θ_{1},$ which may or may not be the same as $θ_{0}.$ Starting from stochastic orders, specifically, likelihood ratio dominance, that obtain for resulting distributions…

Markovian Persuasion with Two States

This paper addresses the question of how to best communicate information over time in order to influence an agent's belief and induced actions in a model with a binary state of the world that evolves according to a Markov process, and with a finite number of actions. We characterize the sender's optimal message strategy in the limit, as the length of each period decreases to zero. The optimal strategy is not myopic. Depending on the agent's beliefs, sometimes no information is revealed, and sometimes the agent's belief is split into two well-chosen posterior beliefs.

后验均值可分离的信息获取成本

Posterior-Mean Separable Costs of Information Acquisition

本文针对决策者收益仅依赖后验均值信念的显示性偏好问题，提出可检验条件以识别学习成本在后验均值分布上线性依赖的假设是否成立，从而支持应用信息设计技术求解。

Conservative Updating

This paper provides a behavioral analysis of conservatism in beliefs. I introduce a new axiom, Dynamic Conservatism, that relaxes Dynamic Consistency when information and prior beliefs "conflict." When the agent is a subjective expected utility maximizer, Dynamic Conservatism implies that conditional beliefs are a convex combination of the prior and the Bayesian posterior. Conservatism may result in belief dynamics consistent with confirmation bias, representativeness, and the good news-bad news effect, suggesting a deeper behavioral connection between these biases. An index of conservatism an…

多行业均衡中的学习路径：在不确定的规模回报下信念动力学

Learning Paths to Multi-Sector Equilibrium: Belief Dynamics Under Uncertain Returns to Scale

本文研究了在不确定规模回报下，企业通过信念更新学习多行业均衡动态，发现长记忆学习方法在效率上不如短记忆方法。

隐私结构与布莱克威尔前沿

Privacy Structure and Blackwell Frontier

本文刻画了基于图推理隐私约束下的可行后验分布集合，通过枚举极值点实现，建立极值后验与强连通半链的联系，提出半链构建方法并优化差分隐私下双划分半链的刻画。

不完全信任下的信念更新

Belief Updating without Complete Trust

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。

贝叶斯主义的经验内容

The Empirical Content of Bayesianism

本文刻画可观测信念符合贝叶斯更新的充要条件，并指出既有经验内容结论依赖于对主观信号分布的额外正确性假设。

在模型不规范情况下的动态决策：一种随机稳定性方法

Dynamic Decision-Making under Model Misspecification: A Stochastic Stability Approach

本文研究了在模型不规范情况下汤普森采样算法的行为和性能，提出了一种统一的随机稳定性框架，为贝叶斯学习与进化动态的结合提供了新的视角。

对抗自然：最小化遗憾与鲁棒性的价格

Learning Against Nature: Minimax Regret and the Price of Robustness

本文提出了一种对抗性数据生成过程的稳健学习框架，通过最小化遗憾标准，揭示了鲁棒性学习的渐近价格及低估推理偏差。

A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack-Leibler divergence between posterior decisions and prio…