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

RI：小型：建模平台竞赛：多代理系统方法

• 批准号: 1527037
• 负责人: Sanmay Das
• 金额: $ 42.96万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527037&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Platform; Competition

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.

在许多重要的现实环境中，多个代理的相互作用受到将它们汇集在一起​​的平台的管辖。即使我们在代理的各个部分都采用理性行为，这种交互的结果在很大程度上取决于平台的规则，法规和规范。当多个这样的平台在动态环境中竞争，并且代理受到知识和计算能力的限制时，只有基于传统经济理论来预测社会成果变得非常困难。该项目的目的是采用计算方法来研究三个重要领域的平台竞争：（1）金融市场； （2）肾脏交换； （3）在线情节劳动和匹配市场。我们将详细探讨这些领域，最初关注以下问题：（1）在金融市场中，高频交易（HFT）对社会有益还是昂贵？如果这是昂贵的，我们如何激励代理商转向消除HFT激励措施的市场平台？ （2）当多个活人配对的肾脏交换竞争他们的匹配算法的频率时，这种社会有害在防止较厚的市场上有害，这可能会允许更多匹配？这会过分影响某些类型的患者吗？如果是这样，我们如何激励交流或患者等待更长的时间？ （3）在允许用户搜索有形商品/耐用物（例如汽车）或无形匹配的平台中，提供给参与者的信息的数量和类型如何影响社会福利？平台竞争的动态如何影响用户可用的信息？在这个项目中，PI和他的学生将使用多种方法，包括计算游戏理论，多代理模拟和经验游戏分析，以模拟平台竞争的动态。由于这些环境的三个特征，将计算镜头应用于研究平台竞争的动态尤其重要：（1）参与者之间发生的复杂互动（例如，交易者从市场价格中学习，平台决定提供多少信息提供给参与者） ; （2）大玩家（平台本身或为平台执行重要服务的代理商）所面临的决策问题的固有复杂性，例如金融市场上的市值或肾脏交换的清算算法； （3）关注平台如何在竞争下发展的动态。该研究计划将有助于阐明有关特定应用的重要问题，并探索新的方法论问题，因为它们在经济和社会系统的计算建模如何与经济学共同的建模类型一起使用时如何有助于更深入地理解。和运营研究。