Recently published, Journal of Economic Behavior & Organization
View abstract We conduct a market experiment with the opportunity for sellers to send a nonbinding advertisement of their product quality, and examine the effects of including a reputation aggregation system for sellers in these markets. In order to closely match the setting of real life markets, we conduct a laboratory experiment designed to emulate an online marketplace. We find substantial efficiency gains from the addition of the ratings system, but not enough to obtain fully efficient market outcomes. These efficiency gains come primarily through a decrease in false advertising behavior by the sellers, as they compete to build reputations, raising the overall levels of trust in the market. We structurally examine the formation of reputations by the sellers (with and without ratings) and the effect of these reputations on the decisions of buyers and sellers in the market. Using a bipartite network of transaction data, we quantify the effects of ratings in encouraging trustworthiness and supporting diverse, connected, and high quality markets.
View abstract We examine behavior in a voluntary resource sharing game that incorporates endogenous network formation; an incentive problem that is increasingly common in contemporary digital economies. By varying the information structure in a controlled laboratory experiment, we examine the underlying mechanisms of reciprocity that generate emergent patterns in linking and contribution decisions. Specifically, we vary whether players are given information about which other players in their group are sharing with them. Reduced-form estimates find significant effects of this information treatment on a number of key outcomes such as efficiency and balanced decentralization. To further understand the driving causes of these observed changes in behavior, we develop and estimate a discrete-choice framework, using computationally efficient panel methods to identify the structure of social preferences in this setting. We find that subjects react to this new information by focusing reciprocity, which helps players coordinate to reach mutually beneficial outcomes. We also find that direct and indirect reciprocity are not perfect substitutes, and this interaction further helps players support efficient outcomes.
Recently published, Proceedings of the 61st IEEE Conference on Decision and Control
View abstract We study the optimal control of the mean and variance of the network state vector. We develop an algorithm that uses projected gradient descent to optimize the control input placement, subject to constraints on the state that must be achieved at a given time threshold; seeking to design an input that moves the moment at minimum cost. First, we solve the state-selection problem for a number of variants of the first and second moment, and find solutions related to the eigenvalues of the systems’ Gramian matrices. We then nest this state selection into projected gradient descent to design optimal inputs.