In this paper, we investigate market design for online gaming platforms. A significant fraction of such platforms' revenue is generated by advertisements, in-app purchases, and subscriptions. Thus, it is necessary to understand which factors influence how much time users spend on the platform. We focus on one such factor - the outcome of the previous game. Using data from an online chess platform, we find strong evidence of history-dependent stopping behavior. We identify two primary types of players: those who are more likely to stop playing after a loss and those who are more likely to stop playing after a win. We propose a behavioral dynamic choice model in which the utility from playing another game is directly affected by the previous game's outcome. We structurally estimate this time non-separable preference model and then conduct counterfactual analyses to evaluate alternative market designs. In the context of online chess games, a matching algorithm that incorporates stopping behavior can substantially alter the length of play.
An important function of a crowdfunding platform is to mitigate information asymmetry between entrepreneurs and investors by transmitting private information from the former to the latter. But can the platform be trusted? Using data from Kickstarter, we estimate a dynamic model of cheap talk, develop a statistical test confirming that the platform's incentives undermine the credibility of its signals, propose regulations that would curb those incentives, and quantify their welfare consequences. These regulations enable the platform to commit to an information disclosure rule and lead to Pareto improvements. We show that the platform's long-run reputation concerns could substitute for commitment.
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.
Matching mechanisms that elicit strength-of-preference can exhibit efficiency gains over those that do not. To quantify these gains, we propose a measure of approximate ex-ante Pareto efficiency. We use this notion to quantify the efficiency improvement of the Boston mechanism and the raffles mechanism (an extension of the Boston mechanism, which we define) over the random serial dictatorship mechanism (RSD) and deferred acceptance mechanism (DA). We complement our theoretical analyses with simulation and experimental results. The simulation results show that these gains also hold for utilitarian welfare in certain parameter regimes. The experimental results focus on the raffles and DA mechanisms. Using human subjects, we find that the raffles mechanism yields higher average payoffs as predicted by the theory, despite the fact that subjects play only approximately optimal strategies.
Outcome bias is pervasive and persistent across different environments. In our noisy gift-exchange game, where agents can perform a real effort task to improve principals' lottery win probability, we replicate outcome bias in effort rewarding when effort is only numerically observable. To investigate the role of principals' beliefs on effort cost, we employed a visual treatment in which principals watch a 30-second video of the agents performing the task. We show that visually observing agents' work corrects asymmetry in rewarding effort. The post-experiment survey suggests that the mechanism through which visually observing effort reduces the outcome bias in reciprocating effort is informing principals about the cost of effort.
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 a 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 participants 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.
''An advanced city is not a place where the poor move about in cars, rather it's where even the rich use public transportation''. This is what Enrique Peñalosa, the celebrated ex-mayor of Bogota once said. However, in order to achieve this objective, one of the crucial properties that the public transportation systems need to satisfy is reliability. While reliability is often referenced with respect to on-schedule arrivals and departures, in this study we are interested in the ability of the system to satisfy the total passenger demand. This is crucial, since if the capacity of the system is not enough to satisfy all the passengers, then ridership will inevitably drop. However, quantifying this excess demand is not straightforward since public transit data, and in particular data from bus systems that we focus on in this study, only include information for people that got on the bus and not those that were left behind at a stop due to a full bus. In this work, we design a framework for estimating this excess demand. Our framework includes a Poisson regression model for the demand for a given bus route and stop and mechanism for identifying instances of potential excess demand. These instances are filtered out from the training phase of the Poisson regression. We show through simulated data that this filtering is able to remove the bias introduced by the censored data observed/logged by the system that results in underestimation of the excess demand. We then apply our approach to real data collected from the Pittsburgh Port Authority and estimate the excess demand over a one-year period.
Work in Progress: