Working Papers

Market Design for Gaming Platforms: Using Stopping Behavior in Matching Algorithms with Ala Avoyan and Giorgi Mekerishvili (Reject & Resubmit at Management Science)


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%.

Collaboration Dynamics of R&D Teams: Theory and Experiment


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.

How the Visibility of Real-Effort Contribution Impacts Reciprocity with Sera Linardi and Xiaohong Wang


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.

Work in Progress

Belief Updating Under Uncertainty in Financially Relevant Situations


I study how people respond to the arrival of new information after they have already made an investment decision and are considering whether to pull out their investment or not. The genesis of this project came from my analysis of proprietary data from an online bookmaker. While suffering from selection issues, these data showed evidence that subjects updated their beliefs in the face of new information in a way that was both heterogeneous and systematic. To better understand how new information affects investors decision I created a new game, the “cash-out” game, and run it in the laboratory. The patterns I observe in the lab data are similar to the patterns we see in the field data. In particular, we can divide the population into three main groups. The first group is more likely to withdraws an investment if they get better news – under-interpret good news. The second group is more likely to withdraw investment after getting worth news – over-interpret bad news. In the last group, investors decide to “cash-out” with a higher chance if the news is less informative – over-interpret less informative good and bad news. This information can be used by bookmakers to better design their platforms and increase profits.