Ph.D. Candidate in Economics, Princeton University
I am a Ph.D. Candidate in the Department of Economics at Princeton University. I am primarily interested in microeconomic theory and experimental economics.
I will be on the 2020/2021 job market.
Does the order and timing of information arrival affect beliefs formed within a group? We address this question by extending the DeGroot social learning model to allow for sequential information arrival. We find that the final beliefs can be altered by varying only the sequencing of information arrival, keeping the information content unchanged. We identify the optimal and pessimal information release sequences that yield the highest and lowest attainable consensus, respectively. In doing so, we bound the variation in final beliefs that can be attributed to the variation in the sequencing of information. We show that groups in which all members are equally influential are those most susceptible to information sequencing. Finally, with regard to information aggregation, as the number of group members grows, the sequential arrival of information compromises the group's beliefs: in all but particular cases, beliefs converge away from the truth.
We test whether the order and timing of information arrival affect beliefs formed within a group. In a lab experiment, participants estimate a parameter of interest using a common and a private signal, as well as past guesses of group members. By varying the sequencing of information arrival, we find that the order and timing of information affect the groups' beliefs, even when the information content is unchanged. We estimate a Hybrid Model that nests the Bayesian and the sequential DeGroot model while allowing for intermediate levels of sophistication. The Bayesian benchmark does a poor job predicting participants' belief dynamics. In contrast, a version of the sequential DeGroot model that allows for flexible weights on others' previous actions explains participants' behavior well. Finally, participants place excessively high and fairly constant weights on their own private signals, regardless of the signal's arrival time.
We study information aggregation when an observer is ambiguous about the precisions of her information sources. The observer estimates a payoff relevant state by minimizing quadratic loss according to MaxMin Expected Utility, and updates her beliefs prior by prior, which induces ambiguity regarding the state. We show that this ambiguity does not vanish even if the number of information sources grows to infinity, and characterize the asymptotic set of posteriors the observer entertains. When the information sources are unbiased signals, the observer learns the state correctly. In contrast, when the observer has access only to other agent's guesses, her estimate converges away from the truth with probability one.
Many committees—juries, political task forces, etc.—spend time gathering costly information in order to reach a decision. We report results from lab experiments on such information-collection processes. We consider decisions governed by individuals and groups and compare how different voting rules affect outcomes. We also contrast static information collection, as in classical hypothesis testing, with dynamic collection, as in sequential hypothesis testing. Generally, outcomes approximate the theoretical benchmark, and sequential information collection is welfare enhancing relative to static collection. Nonetheless, several important departures emerge. Static information collection is excessive, and sequential information collection is non-stationary, producing declining decision accuracies over time. Furthermore, groups using majority rule often reach especially hasty and inaccurate decisions.
An information intermediary, such as a news outlet, observes multiple independently developing news topics and must decide which one to use as its headline and when to release the story. The information intermediary weighs the population demand for this information, which decreases in time, and the precision of the information, which in expectation increases in time across all topics. We model each topic's precision as an independent Brownian motion and assume that eventually, the demand for information in the topic drops to zero. We translate the optimization of this problem to an optimal stopping problem over the multi-dimensional process with discounting and a deadline. We find that optimal information release standards decrease in time. For a specific demand function, we find an explicit boundary that determines the news intermediary's optimal behavior.
We analyze a model with sequential markets under the presence of learning-by-doing and learning spillovers. We focus on the role that product differentiation plays in firm competition under the presence of these joint economies, and how it affects optimal quantities, prices, and profits. We also analyze the welfare implications of both learning-by-doing and learning spillovers. We obtain the following main results: (1) A low level of learning efficiency enhances both profits and consumer surplus, while high learning efficiency levels may lead to a decline in profits or even market centralization. (2) The presence of spillovers increases both profits and consumer surplus, and in some cases, has the potential to prevent market centralization. (3) The fraction of the inefficiency originating from learning-by-doing decreases as competition becomes fiercer, while the fraction of the inefficiency originating from spillovers remains a substantial part of the total inefficiency regardless of the level of product differentiation.