【经管院每周系列讲座第348期】Belief Formation Under Signal Correlation




主题 Belief Formation Under Signal Correlation

主讲人Professor  Ryo Okui, Seoul National University

主持人hy5902海洋之神 Maxwell Pak副教授

时间 2019 年3月15日下午14:00-15:30


主办单位:经济与管理研究院 科研处



Professor Okui is a well-known professor in econometrics. He has published in lead economics journals, such as Journal of Econometrics and Review of Economic Studies.  He is an experienced teacher, he is employed by many universities, such as New York University, University of York York, UK and Seoul National University. He has received many awards and honors such as in September 2010, he was awarded the JSS Ogawa Award by Japan Statistical Society, and in April 2005, he was awarded The Hiram C. Haney Fellowship Award in Economics by  University of Pennsylvania.


Using a set of incentivized laboratory experiments, we characterize how people form beliefs about a random variable based on independent and correlated signals. First, we theoretically show that while pure correlation neglect always leads to overvaluing correlated signals, this depends on the exact structure of signal generation process if people misperceive precision. Specifically, they may sometimes undervalue correlated signals depending on the correlation structure even in the presence of correlation neglect. Our experimental results reveal that, while they do overvalue moderately or strongly correlated signals, they undervalue weakly correlated signals. It is consistent with the presence of both correlation neglect and overprecision — believing the signals are more precise than they actually are. Estimated parameters of our model suggest that subjects show a nearly complete level of correlation neglect and also suffer from a high level of overprecision. Additionally, we find that subjects do not fully benefit from wisdom of the crowd — they undervalue aggregated information about others’ actions in favor of their private information. This is consistent with the models of overprecision where people do not properly incorporate the variance reducing power of averages.