Research

Published Paper

We propose and axiomatize the Rank-Dependent Inequality-Averse  model. The model highlights an important linkage, Guilt Moderation, between different other-regarding behaviors: when choices are risky, decision maker feels less guilt by assigning  more weight to the fairer outcomes, creating a tendency to exhibit self-centered (or altruistic) behavior when outcomes are mixed with a fairer (or unfairer) outcome. Our model provides a unifying explanation for two seemingly distinct reversal behaviors in moral wiggle room and ex-ante fairness for you. Moreover, we characterize guilt moderation with the reversal behaviors and risk preference for others.  Lastly, the model sheds light on self-other risk attitudes gap and increased envy in wage transparency.

Working Paper

Recommendations play an undeniable role in decision-making. The empirical literature argues that recommendation can influence demand  through  two distinct channels: i) by enlarging awareness (attention channel), or ii) by altering preferences (utility channel). In this paper, we develop a framework to study these two channels. We illustrate when and how one can distinguish through which channel the recommendations affect choices. We offer both deterministic and probabilistic models. While deterministic models aim to identify the basic observable behavioral differences between these two channels, our probabilistic models are suitable for econometric estimation, which is crucial for studying aggregate behavior used in empirical work.  Our parametric models offer unique identification under minimal data requirements. This enables  us to make out-of-sample predictions for counter factual analysis for policy design purposes. In addition, we offer simple and intuitive behavioral postulates characterizing each model so that one can test our models.


We introduce an Attention Overload Model that captures the idea that alternatives compete for the decision maker’s attention, and hence the attention frequency each alternative receives decreases as the choice problem becomes larger. Using this nonparametric restriction on the random attention formation, we show that a fruitful revealed preference theory can be developed, and provide testable implications on the observed choice behavior that can be used to partially identify the decision maker’s preference. Furthermore, we provide novel partial identification results on the underlying attention frequency, thereby offering the first nonparametric identification result of (a feature of) the random attention formation mechanism in the literature. Building on our partial identification results, for both preferences and attention frequency, we develop econometric methods for estimation and inference. Importantly, our econometric procedures remain valid even in settings with large number of alternatives and choice problems, an important feature of the economic environment we consider. We also provide a software package in R implementing our empirical methods, and illustrate them in a simulation study.


We propose and characterize the General Reciprocity Model in a framework of context-dependent choice. In the model, the second mover can establish their own rules or expectations regarding when or why to reciprocate. The model disentangles, while preserving, the unconditional baseline social preference from reciprocity: reciprocity occurs when people deviate from their baseline preference due to the context in which the first mover's choice is made. The revealed reciprocity result of the model coincides with the workhorse criterion to identify reciprocity in an experiment. Therefore, our model enables us to investigate the behavioral implication underlying this intuition. Moreover, we utilize the idea of Diminishing Sensitivity under Contextual Shrinkage to help identify reciprocity and baseline preference even when the ideal ``no context'' data are unavailable. Applications to several previous experiments on reciprocity are discussed for illustrating the identifications from the model.

Work In Progress