Heterogeneous agent modeling of the personal data economy

Benthall, Sebastian, West Harrison NY

Investigators

Abstract

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.