Welcome! I am a Ph.D. candidate 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.
In my job market paper, I design a series of theory-inspired lab experiments grounded in three broad classes of economic theories to understand a classical behavioral puzzle in risk management -- probability matching.
I'll be on the 2023-2024 job market.
Contact: jing734 [at] ucsb.edu
Find my CV here.
What Drives Probability Matching? Latest version
(Job Market Paper)
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.
Probability matching, a classical violation of expected utility maximization, refers to people’s tendency to make stochastic choice, 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 three broad classes of explanations: models of Correlation-Invariant Stochastic Choice (mixing due to factors orthogonal to how outcomes are jointly determined, such as non-standard preferences or errors), models of Correlation-Sensitive Stochastic Choice (e.g., deliberately mixing to hedge against misperceived risk), and Framing Effects (indecisiveness due to frame-sensitive heuristics e.g., similarity heuristic: attending to dissimilar but irrelevant attributes (outcomes), while ignoring relevant attributes (probabilities)). My experimental design uses a diagnostic approach, differentiating between their testable predictions over a series of treatments. The results suggest that a substantial proportion of mixing behavior aligns with models of Correlation-Sensitive Stochastic Choice, while the other classes have limited explanatory power.
Note: A shorter version titled "Does Correlation Matter in Probability Matching? A Laboratory Investigation" is Revise & Resubmit (second round) at the Journal of Economic Behavior and Organization [SSRN Link]
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.
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 Belief 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.
Information Visualization and Belief Updating
(Data collection in progress)
Does the way information are visualized or presented matter for belief updating?/If it matters, why?/Difficulty in interpreting info VS difficulty in processing info?
Teaching Assistant [UC Santa Barbara]
PhD-level core courses:
ECON 210B. Game Theory (2019 Winter, 2020 Winter)
ECON 9. Introduction to Economics
ECON 2. Principles of Economics-Macro
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