DIMENSIONALITY AND DISAGREEMENT: ASYMPTOTIC BELIEF DIVERGENCE IN RESPONSE TO COMMON INFORMATION
研究了有限理性个体在多维学习中的信念收敛与分歧，发现当观测数据频繁出现识别问题时，即使面对相同信息，不同先验的个体也可能永久分歧，且信息越多分歧越强。
Abstract We provide a model of boundedly rational, multidimensional learning and characterize when beliefs will converge to the truth. Agents maintain beliefs as marginal probabilities instead of joint probabilities, and agents' information is of lower dimension than the model. As a result, for some observations, agents may face an identification problem affecting the role of data in inference. Beliefs converge to the truth when these observations are rare, but beliefs diverge when observations presenting an identification problem are frequent. Robustly, two agents with differing priors who observe identical, unambiguous information may disagree forever, with stronger disagreement the more information received.