We investigate market design for online gaming platforms. Since a large part of such platforms’ income is generated by advertisement, it is essential to know what influences how long users stay on the platform. We provide evidence of history-dependent stopping behavior in non-monetary environments. We find that there are two types of people: those who are more likely to stop playing after a loss; and others, who are more likely to keep playing until a win. We find that an individual’s type is time-invariant over the years. We propose a behavioral dynamic choice model where utility from playing another game is directly affected by the outcome of the previous game. We structurally estimate this time non-separable preference model and conduct counterfactual analyses to evaluate alternative market designs. We find that in the context of online chess games a matching algorithm that incorporates stopping behavior can increase the length of play by 5.44%.
I explore the dynamics of collaboration between two agents when one is incumbent with well-known ability (resources) and another is an entrant with unobservable ability (resources). If the incumbent is a low-ability type and learns the collaborator’s type based on history, then accumulating the reputation of being a high-ability type will lead to a breakup of the partnership in every equilibrium. If the incumbent is a high-ability type, collaboration is sustainable. However, a low-ability entrant shirks on the equilibrium path, so the first-best outcome is not attainable. I conduct an experiment and find that reputation-building might hinder collaboration.
Are we more generous the more we can see of how others have worked for our benefit? In our noisy gift-exchange game, recipients can perform a real-effort task to improve donors’ lottery win probability. Donors observe the outcome of the lottery, then decide on their giving. Donors in the Numerical treatment see numerical measures of their recipient’s effort, while those in the Visual treatment additionally see a 30-second video of the recipients performing the task. Echoing the outcome bias literature, the Numerical treatment showed that recipients’ efforts are rewarded more generously when donors won the lottery. The Visual treatment corrects this asymmetry in rewarding effort but does not increase total giving. Post-experiment surveys suggest that this is because the video not only increases donors’ familiarity with the recipients’ work but also their perception that the task was not taxing, which decreases reciprocity.
How to get-toilet-paper.com? Provision of Information as a Public Good with Mallory Avery, Kristi Bushman, Alexandros Labrinidis, Sera Linardi, and Konstantinos Pelechrinis (Appeared at MD4SG - video)
In this paper, we describe the implementation of an information sharing platform, got-toilet-paper.com. We create this web page in response to the COVID-19 pandemic to help the Pittsburgh, PA community share information about congestion and product shortages in supermarkets. We show that the public good problem of the platform makes it difficult for the platform to operate. In particular, there is sizable demand for the information, but supply satisfies only a small fraction of demand. We provide a theoretical model and show that the first best outcomes cannot be obtained in a free market and the best symmetric equilibrium outcome decreases as the number of participant increases. Also, the best symmetric equilibrium has two problems, cost inefficiency and positive probability of termination. We discuss two potential solutions. The first is a uniform random sharing mechanism, which implies randomly selecting one person every period who will be responsible for information sharing. It is ex-post individually rational but hard to implement. The second solution is the one that we began implementing. It implies selecting a person at the beginning and make her responsible to share information every period, while reimbursing her cost. We discuss the reasons for high demand and low supply both qualitatively and quantitatively.
Work in Progress: