Jing Zhou

Bio

Welcome! I received my Ph.D. in Economics at University of California, Santa Barbara. 

My research interests are in Behavioral and Experimental Economics, Decision Theory, and Information Economics. 

I use theoretical and empirical methods to study the origins of "irrational" economic decision-making and belief biases, as well as developing methodological tools to measure cognitively imprecise beliefs. 


Contact: jing734 [at] ucsb.edu

RESEARCH

Working Papers

Does Correlation Matter in Probability Matching? A Laboratory Investigation [Link]

forthcoming at the Journal of Economic Behavior and Organization 

People switch between risky options because they care about how options are correlated./For example, hedge against misperceived "risk"/Most stochastic choice models such as Non-Expected Utility preferences, trembling hand, misunderstanding of probability, similarity heuristics, cannot explain it.

Abstract

Probability Matching, a classical violation of expected utility maximization, refers to people's tendency to randomize, or even match their choice frequency to the outcome probability, when choosing over binary lotteries that differ only in their probabilities. Why? I present an experiment designed to distinguish between several broad classes of explanations: (1) models of Correlation-Invariant Stochastic Choice --- randomizing due to factors orthogonal to the correlation between lotteries, such as non-standard preferences or errors, and (2) models of Correlation-Sensitive Stochastic Choice --- deliberately randomizing due to misperceived hedging opportunities, especially when lotteries are negatively correlated. My experimental design differentiates between their testable predictions by varying the correlation between lottery outcomes. The findings indicate that the first class, despite being home to most existing theories, has limited explanatory power. Using additional treatment, I rule out Similarity Heuristics as a competing explanation with the second class. The results indicate that a vast majority of individuals deliberately randomize due to misperceived hedging opportunities.

 Preference for Sample Features and Belief Updating with Menglong Guan, ChienHsun Lin, and Ravi Vora

(Draft available upon request)

The more detailed information, the better use of information?/No, better with less informative one -- sample proportion./And better with your favorite one.

Abstract

We experimentally investigate how individuals use and value different statistical characteristics of realized signals, referred to as sample features, for belief updating. While a large literature studies how people update beliefs when receiving signals with all sample features embedded, individuals in the real world often encounter information with some certain sample feature being highlighted. We find that the updating behavior differs by sample feature and does not monotonically respond to increasing the informativeness of sample features: closest to the Bayesian benchmark when utilizing Proportion (the relative frequency of realized outcomes) for belief updating, even though it is not the most informative one. Subjects’ perceived usefulness of the sample features also diverges from the predictions of informativeness: prefer the sample features which contain Proportion over those that do not. Combining preference and belief updating performance, we show that, on average, subjects make better use of the sample features they prefer, while there exist non-negligible inconsistencies between preference and performance. Taken together, our findings indicate that the biased use of sample features in belief updating is more likely to be intentional deviations rather than inattentive heuristics.

Selected Work in Progress

Dynamic Binary Elicitation Method with Xin Jiang

(Draft in progress)

Propose a new method to elicit cognitively imprecise beliefs./Uses an incentivized way to collect extra information about cognitive uncertainty./Works equally well with the existing method in eliciting point beliefs about real-world economic variables but with more info on cognitive imprecision.

Teaching

Teaching Assistant [UC Santa Barbara]

ECON 210B.  Game Theory (2019 Winter, 2020 Winter)


ECON 9. Introduction to Economics (for non-Economics majors)

ECON 2. Principles of Economics-Macroeconomics 

ECON 10A. Intermediate Microeconomic Theory I

ECON 100B. Intermediate Microeconomic Theory II

ECON 101. Intermediate Macroeconomic Theory

ECON 134A. Financial Management

ECON 135. Monetary Economics