Heterogeneous agent modeling of the personal data economy

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在纽约大学法学院的凯瑟琳斯特兰德堡博士的赞助下，这个博士后奖学金支持一位早期职业科学家调查个人数据经济的异构代理建模。数字平台已经介入社会和经济生活的方方面面。这些服务收集的个人数据经济通过信用评分机构和公司估值与金融体系交叉。它还通过其在营销中的作用与真实的经济相交。数据的价值和影响部分来自于它如何调解和连接不同的参与者。对这种数据经济的适当监管是一个紧迫的政治问题。这项研究解决了个人数据在社会背景，部门和平台之间流动时的宏观经济性质的基本科学问题。这项工作将有助于隐私的一般经济理论以及计算经济学和金融学的方法。解决个人数据作为战略地位和可交易资产的混合角色的新建模技术将在开源软件研究工具包中传播。显著的结果将提交给联邦机构，以建议监管的数字经济。这项研究应用和扩展异构代理建模（HAM）方法用于宏观经济学和金融研究。异构Agent建模（HAM）包括理性Agent随机动态规划（SDP）和基于零智能Agent的模型（ABM）。数据经济案例激发了关于如何将SDP和ABM合成为支持内生信息流的框架的基础研究。个人数据经济中的异质行为者具有不同程度的有限理性，包括预见跨期选择后果的能力。它们的相互作用也可能导致未知或未调查的涌现特性。通过调查数据经济的特定方面，例如与信用系统的交叉点，产品开发和数据中介治理，这项工作将开发建模工具，将联合收割机HAM与Pearlian因果关系理论，部分可观察马尔可夫决策过程，以及奥斯特罗姆和威廉姆森的制度经济学。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Katherine Strandburg at New York University School of Law, this postdoctoral fellowship award supports an early career scientist investigating heterogeneous agent modeling of the personal data economy. Digital platforms have come to mediate every aspect of social and economic life. The economy of personal data collected by these services intersects the financial system through credit scoring institutions and the valuations of companies. It also intersects the real economy through its role in marketing. Data’s value and impact come in part from how it mediates and connects heterogeneous actors. The proper regulation of this data economy is an urgent political matter. This research addresses the fundamental scientific question of the macroeconomic properties of personal data as it flows between social contexts, sectors, and platforms. The work will contribute to both a general economic theory of privacy as well as methods in computational economics and finance. New modeling techniques that address personal data’s hybrid role as strategic position and tradable asset will be disseminated in open source software research toolkits. Salient results will be presented to federal agencies to advise regulation of the digital economy.This research applies and extends heterogeneous agent modeling (HAM) methods used in macroeconomics and financial research. Heterogeneous agent modeling (HAM) includes both rational agent stochastic dynamic programming (SDP) and zero-intelligence agent based models (ABM). The data economy case motivates fundamental research about how to synthesize SDP and ABM into a framework that supports endogenous information flows. Heterogeneous actors in the economy of personal data are marked by different levels of bounded rationality, including the ability to foresee consequences of intertemporal choice. Their interactions may also result in unknown or uninvestigated emergent properties. Through the investigation of particular aspects of the data economy, such as its intersections with the credit system, product development, and data intermediary governance, this work will develop modeling tools that combine HAM with insights from Pearlian causality theory, Partially Observable Markov Decision Processes, and the institutional economics of Ostrom and Williamson.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.