Matching

Philip Brookins and Paan Jindapon. 2/20/2021. “Risk preference heterogeneity in group contests.” Journal of Mathematical Economics. Publisher's VersionAbstract
We analyze the first model of a group contest with players that are heterogeneous in their risk preferences. In our model, individuals’ preferences are represented by a utility function exhibiting a generalized form of constant absolute risk aversion, allowing us to consider any combination of risk-averse, risk-neutral, and risk-loving players. We begin by proving equilibrium existence and uniqueness under both linear and convex investment costs. Then, we explore how the sorting of a compatible set of players by their risk attitudes into competing groups affects aggregate investment. With linear costs, a balanced sorting (i.e., minimizing the variance in risk attitudes across groups) always produces an aggregate investment level that is at least as high as an unbalanced sorting (i.e., maximizing the variance in risk attitudes across groups). Under convex costs, however, identifying which sorting is optimal is more nuanced and depends on preference and cost parameters.
Kevin Boudreau, Tom Brady, Ina Ganguli, Patrick Gaule, Eva Guinan, Tony Hollenberg, and Karim R. Lakhani. 2017. “A Field Experiment on Search Costs and the Formation of Scientific Collaborations.” The Review of Economics and Statistics, 99, 4, Pp. 565-576. Publisher's VersionAbstract

Scientists typically self-organize into teams, matching with others to collaborate in the production of new knowledge. We present the results of a field experiment conducted at Harvard Medical School to understand the extent to which search costs affect matching among scientific collaborators. We generated exogenous variation in search costs for pairs of potential collaborators by randomly assigning individuals to 90-minute structured information-sharing sessions as part of a grant funding opportunity for biomedical researchers. We estimate that the treatment increases the baseline probability of grant co-application of a given pair of researchers by 75% (increasing the likelihood of a pair collaborating from 0.16 percent to 0.28 percent), with effects higher among those in the same specialization. The findings indicate that matching between scientists is subject to considerable frictions, even in the case of geographically-proximate scientists working in the same institutional context with ample access to common information and funding opportunities.

Andrea Blasco, Kevin Boudreau, Karim R. Lakhani, Michael Menietti, and Christoph Riedl. 2013. “Do Crowds Have the Wisdom to Self-Organize?”.Abstract

The “self-organizing” of online crowds — or workers, more generally — into teams is a non-trivial problem of coordination and matching, in a context in which other parties are simultaneously competing for partners. Here, we experimentally investigate the capacity for workers in online crowds to self-organize into teams, within a scientific crowdsourcing contest. We compare matching outcomes and performance to those in a comparison group in which we eliminate the coordination and matching problem altogether (by directly assigning individuals to Pareto efficient teams). Online crowd members do remarkably well relative to the benchmark achieving 13% more functioning teams. Teams also tended to be more effective, by several measures. (We found no evidence these levels depending on the size of the self-organizing pool of workers.) Conditional on having formed, the self-organizing teams also benefit from several advantages in performance.

Field_Experiment_on_Search_Costs.pdf

A Field Experiment on Search Costs and the Formation of Scientific Collaborations