Yucheng Liang

Assitant Professor

Carnegie Mellon University


Bio

Working Papers

Bio

I am an Assistant Professor at Tepper School of Business, Carnegie Mellon University, with a courtesy appointment at the Department of Social and Decision Sciences. I am also a Research Affiliate at briq.

I use theoretical and empirical methods to study the foundations and applications of behavioral economics.

Curriculum Vitae (Updated July 2021)

Email: ycliang@cmu.edu


Working Papers

The Inference-Forecast Gap in Belief Updating

with Tony Q. Fan and Cameron Peng

Current version: November 2021

Individual forecasts of economic variables show widespread overreaction to news, but laboratory experiments on belief updating typically find underinference from signals. We provide new experimental evidence to connect these two seemingly inconsistent phenomena. Building on a classic experimental paradigm, we study how people make inferences and revise forecasts in the same information environment. Subjects underreact to signals when inferring about underlying states, but overreact to signals when revising forecasts about future outcomes. This gap in belief updating is largely driven by the use of different simplifying heuristics for the two tasks. Additional treatments link our results to the difficulty of recognizing the conceptual connection between making inferences and revising forecasts.

[Experimental Instructions]

Learning from Unknown Information Sources

Current version: July 2021

When an agent receives information from a source whose accuracy might be either high or low, standard theory dictates that she update as if the source has medium accuracy. In a lab experiment, subjects deviate from this benchmark by reacting less to uncertain sources, especially when the sources release good news. This pattern is validated using observational data on stock price reactions to analyst earnings forecasts, where analysts with no forecast records are classified as uncertain sources. A theory of belief updating where agents are insensitive and averse to uncertainty in information accuracy can explain these results.

[Experimental Instructions] [Old Version (2020)]

30-minute presentation as the Jaffray Lecture at the Risk, Uncertainty and Decision (RUD) 2020 Conference

Social Comparison and the Value of Performance Trajectory Information: A Field Experiment in the Workplace

with Hugh Xiaolong Wu and Shannon X. Liu

Current version: April 2021

New workers often compare themselves to their high-achieving senior coworkers, but they often do so without knowing how senior workers performed in the early stages of their careers. This upward social comparison under incomplete information can have adverse effects on new workers’ well-being and employee turnover. We study whether providing performance trajectory information to new workers mitigates the negative consequences of performance comparison. In a large-scale randomized control trial at a leading multinational spa chain in China, we sent workers twice-weekly messages on the performance trajectories of their high-performing senior coworkers. This information treatment reduces the attrition rate of new workers by 12%, and the effect is most pronounced for the more productive workers. The lower attrition rate is mostly driven by an improvement in new workers' stress levels and mental health due to the lowering of their beliefs about senior coworkers' past performance. Overall, this study demonstrates that showing junior workers the "Curricula Vitae" of senior workers mitigates social comparison costs within firms.

Information-Dependent Expected Utility

Current version: February 2017

In decision problems under uncertainty, the subjective evaluation of an outcome can depend on the information content of its realization. To accommodate this dependence, we introduce and axiomatize a model of information-dependent expected utility by allowing the utility of an outcome to flexibly depend on its information content in an (Anscombe-Aumann) act. Subjective beliefs are identified in a special class of our model where the utility of an outcome can be decomposed as the sum of consumption utility and information utility. Our model allows for both information seeking and information averse preferences, as well as a comparative theory of information preferences. For information seeking preferences, we introduce a Hidden Acts representation where the value of information is as if induced from the expected utility of the optimal choice in a fictitious future decision problem given that information.