Market Design for Gaming Platforms: Using Stopping Behavior in Matching Algorithms with Ala Avoyan and Giorgi Mekerishvili
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 investigate the possibility of collaboration with asymmetric information when one agent’s ability is unobservable. I show that if an agent with observable ability is a low type and learns collaborator’s type based on history, accumulating reputation of being a high type will lead the breakup in every equilibrium. Also, if the agent with observable ability is a high type, collaboration is sustainable but unobservable low type agent shirks on the equilibrium path, so the first best outcome is not attainable. I designed an experiment to test theoretical predictions and found that reputation building might hinder collaboration.
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.