Information Aggregation Under Ambiguity: Theory and Experimental Evidence
研究了动态交易模型中模糊厌恶交易者如何影响信息聚合，发现可分离证券在模糊性下失效且易被操纵，并提出了强可分离证券作为稳健替代方案，通过实验验证了理论预测。
Abstract We study information aggregation in a dynamic trading model. We show theoretically that separable securities, introduced by Ostrovsky in the context of Expected Utility, no longer aggregate information if some traders have imprecise beliefs and are ambiguity averse. Moreover, these securities are prone to manipulation as the degree of information aggregation can be influenced by the initial price set by the uninformed market maker. These observations are also confirmed in our laboratory experiment using prediction markets. We define a new class of strongly separable securities, which are robust to the above considerations and show that they characterize information aggregation in both strategic and non-strategic environments. We derive several testable predictions, which we are able to confirm in the laboratory. Finally, we show theoretically that strongly separable securities are both sufficient and necessary for information aggregation but, strikingly, there does not exist a security that is strongly separable for all information structures.