RI: Small: Modeling Platform Competition: A Multi-Agent Systems Approach

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In many important real-world settings, the interactions of multiple agents are governed by the platform that brings them together. Even if we assume rational behavior on the parts of agents, the outcomes of such interactions depend heavily on the rules, regulations, and norms of the platform. When multiple such platforms compete in dynamic environments, and agents are constrained in terms of their knowledge and computational capabilities, it becomes very difficult to predict societal outcomes based only on traditional economic theory. The goal of this project is to employ computational methods to study platform competition in three important domains: (1) financial markets; (2) kidney exchange; (3) online episodic labor and matching markets. We will explore these domains in detail, focusing initially on the following questions: (1) In financial markets, is high-frequency trading (HFT) beneficial or costly to society? If it is costly, how can we incentivize agents to switch to market platforms that do away with the incentives for HFT? (2) When multiple living-donor paired kidney exchanges compete on how frequently they run their matching algorithms, is this socially harmful in preventing a thicker market that would potentially allow more matches? Does this disproportionately affect certain types of patients? If so, how can we incentivize exchanges or patients to wait longer? (3) In platforms that allow users to search for tangible goods/durables (e.g. cars) or intangible matches, how does the amount and type of information provided to participants affect social welfare? How do the dynamics of platform competition affect the information available to users?In this project, the PI and his students will use multiple approaches, including computational game theory, multi-agent simulation, and empirical game analysis, to model the dynamics of platform competition. Applying the computational lens to studying the dynamics of platform competition is particularly important because of three features of these environments: (1) The complex interactions that occur between participants (e.g. traders learning from market prices, platforms deciding how much information to provide to participants); (2) The inherent complexity of the decision-problems faced by the big players (the platform itself, or agents that perform an important service for the platform) like market-makers in financial markets or the clearing algorithm for kidney exchanges; (3) A focus on the dynamics of how platforms evolve under competition. This research program will serve to both elucidate important questions about the specific applications, as well as explore new methodological questions as they arise in how computational modeling of economic and social systems can contribute deeper understanding when used in conjunction with the types of modeling common to economics and operations research.