Job Market Paper
Do Multiple Contacts Matter?
Abstract: I experimentally investigate collusive behavior under simultaneous interaction in multiple strategic settings, a phenomenon which I call multiple contacts. When agents interact in multiple settings, even if defection or deviation from collusion in one setting can not be credibly punished in the same setting, it might be punished in other settings. This theoretically increases the incentive to collude. I study this phenomenon using a laboratory experiment using multiple symmetric or asymmetric prisoner's dilemma games. I observe a statistically significant increase in the probability of punishment in one game after defection in another game under multiple contacts, but only when the games are asymmetric in payoffs. However, I do not find any significant increase in collusion due to multiple contacts in either symmetric or asymmetric environment. In addition to this result, to find further support for the theory which suggests that agents should use different strategies under multiple contacts, I estimate the underlying strategies that subjects use in my experiment. To this end, I modify popular strategies (e.g., Grim Trigger, Tit-for-Tat, etc.) to condition on the history observed in multiple strategic settings. My strategy estimation results show that only for games with asymmetric payoffs subjects use these modified strategies in the presence of multiple contacts.
R&D Incentives in an Upstream-Downstream Structure, Indian Economic Review (2016): 43-68
(Master's Thesis, under the supervision of Dr. Tarun Kabiraj, Indian Statistical Institute, Kolkata)
Abstract: This paper studies R&D incentives of a non-producing firm in an upstream-downstream structure for three types of technologies, viz., upstream technology, downstream technology and common technology. We consider both the cases of exogenous and endogenous innovation, and the case when common technology innovation leads to spillovers. Our results are then compared and contrasted with those when an insider (i.e., upstream or downstream) firm is engaged in research. The size of the innovation can be larger compared to the third firm R&D case. While there can be a conflict between private and socially optimal choice of technology, we show that socially optimal choice is implementable.
Work in Progress
Team Innovation Contests with Cognitive Diversity
(Joint work with Prof. Brian Roberson, Purdue University)
Abstract: In this paper, we construct a framework for examining the role of team composition in a large contest between teams of diverse individuals facing an innovation problem. In the contest, prizes are awarded based on the values of the teams' innovations, where the value of an innovation depends on both the techniques or approaches (tools) that a team applies to the innovation problem and the amount of work used to develop the innovation. Within a team, the team members possess different skills or perspectives (tools) which may be applied to innovation problems. For a given innovation problem and a given level of team effort, different combinations of tools within a team may generate different values for the team innovation. In this context, we examine the issues of individual team performance as a function of a team's own composition and the overall performance of the contest as a function of the compositions of the teams. We find that the question of whether increasing diversity leads to an increase (decrease) in expected performance, for both an individual team and the overall contest, depends on the efficiency with which teams are able to effectively apply diverse sets of tools to innovation problems. Thus, our paper provides a channel -- other than a direct cost of diversity -- through which diversity can be beneficial or detrimental depending on how teams are able to effectively utilize diverse tools.
Team Composition and Cooperation in Queueing Systems
(Joint work with Dr. Yaroslav Rosokha, Purdue University, and Dr. Masha Shunko, University of Washington)
Abstract: We study a single-queue system in which heterogeneous tasks arrive stochastically and are processed by a team of either heterogeneous or homogenous servers. In particular, servers specialize in one type of task, which, in our model, translates into a lower cost of effort while processing that type of task. The effort chosen by the servers determines the speed at which the task is processed. We show that, theoretically, in the implied stochastic dynamic game, the choice of high effort can be sustained in the subgame-perfect equilibrium if the arrival rate is high enough regardless of team composition. We also show that for intermediate arrival rates homogeneous teams perform better than heterogeneous teams when the types of arriving tasks are independent or are serially positively correlated, and heterogeneous teams perform better than homogeneous teams in the presence of negative serial correlation in the types of tasks.
Quality Dispersion and Income Inequality: Evidence from U.S. Airlines
(Joint work with Dr. Joe Mazur, Purdue University and Dr. Brian Roberson, Purdue University)
Learning by Collaborating
(Joint work with Dr. Hajime Shimao, McGill University)