Abstracts of current working papers
This version: August 2013.
Abstract. This paper proposes a dynamic model of bargaining to analyze decentralized markets where buyers and sellers obtain information about past deals through their social network. We show that groups with high density and/or low variability in the number of connections across individuals allow their members to obtain a better deal. We test the theoretical predictions with a lab experiment where we investigate 4 treatments that vary the network that groups of 6 subjects are assigned to. The results of the experiment lend support to the theoretical predictions: subjects converge to a high equilibrium demand if they are assigned to a network that is dense and/or has low variability in number of connections across members.
Keywords: network, communication, experiment, noncooperative bargaining. JEL: C73, C78, C91, C92, D83.
This version: July 2013.
Abstract. Individuals learn by chit-chatting with others as a by-product of their online and offline activities. Social plugins are an example in the online context: they embed information from a friend, acquaintance or even a stranger on a webpage and the information is usually independent of the content of the webpage. We formulate a novel framework to investigate how the speed of learning by chit-chat depends on the structure of the environment. A network represents the environment that individuals navigate to interact with each other. We derive an exact formula to compute how the expected time between meetings depends on the underlying network structure and we use this quantity to investigate the speed of learning in the society. Comparative statics show that the speed of learning is sensitive to a mean-preserving spread of the degree distribution (MPS). Specifically, if the number of individuals is low (high), then a MPS of the network increases (decreases) the speed of learning. The speed of learning is the same for all regular networks independent of network connectivity. An extension explores the effectiveness of one agent, the influencer, at influencing the learning process.
Keywords: JEL: D83, D85.
This version: June 2012.
Whatever it takes: Rivalry and unethical behavior (joint with Gavin Kilduff, Adam Galinsky and James J. Reade)
Abstract. We investigate rivalry as a driver of unethical behavior. We first distinguish it from general competition, both conceptually and in terms of its consequences for behavior. Then, across four experiments and one archival study, we find evidence that rivalry fuels greater unethical behavior than general competition. Specifically, rivalry was associated with increased Machiavellianism, over-reporting of performance, willingness to employ unethical negotiation tactics, and unsportsmanlike behavior. Further, several of these effects carried over to subsequent situations that occurred outside of the rivalrous relationship itself, suggesting that rivalry activates a mindset that can subsequently influence unrelated decisions and behaviors. These findings highlight the importance of rivalry as a widespread, powerful, and yet largely unstudied phenomenon with significant organizational implications. Further, they help to inform when and why unethical behavior occurs within organizations, and ultimately suggest that the nature of competition is dependent upon actors’ relationships and prior interactions.
This version: November 2012.
Network cognition (joint with Roberta Dessi and Sanjeev Goyal)
Abstract. We study individual ability to memorize and recall information about friendship networks using a combination of experiments and survey-based data. In the experiment subjects are shown a network, in which their location is exogenously assigned, and they are then asked questions about the network after it disappears. We find that subjects exhibit three main cognitive biases: (i) they underestimate the mean degree compared to the actual network; (ii) they overestimate the number of rare degrees; (iii) they underestimate the number of frequent degrees. We then analyze survey data from two `real' friendship networks from a Silicon Valley firm and from a University Research Center. We find, somewhat remarkably, that individuals in these real networks also exhibit these biases. The experiments yield three further findings: (iv) network cognition is affected by the subject's location, (v) the accuracy of network cognition varies with the nature of the network, and (vi) network cognition has a significant effect on economic decisions.
Keywords: friendship networks, cognitive biases