Data Auctions with Externalities

Data Auctions with Externalities

Presenter:Munther A. Dahleh, Massachusetts Institute of Technology

Abstract: The design of data markets has gained in importance as firms increasingly use predictions from machine learning models to make their operations more effective, yet need to externally acquire the necessary training data to fit such models. This is particularly true in the context of the Internet where an ever-increasing amount of user data is being collected and exchanged. The challenge in creating such a marketplace stems from the very nature of data as an asset: (i) it can be replicated at zero marginal cost; (ii) its value to a firm is inherently combinatorial (i.e. the value of a particular dataset depends on what other (potentially correlated) datasets are available); (iii) its value to a firm is dependent on which other firms get access to the same data; (iv) prediction tasks and the value of an increase in prediction accuracy vary widely between different firms, and so it is not obvious how to set prices for a collection of datasets with correlated signals; (v) finally, the authenticity and truthfulness of data is difficult to verify a priori without first applying to a prediction task.

In this work, we consider the case with N competing firms and a monopolistic data seller. We demonstrate that modeling the utility of firms solely through the increase in prediction accuracy experienced reduces the complex, combinatorial problem of allocating and pricing multiple data sets to an auction of a single digital (freely replicable) good. We address an important property of such markets that has been given limited consideration thus far, namely the externality faced by a firm when data is allocated to other, competing firms. Addressing this is likely necessary for progress towards the practical implementation of such markets. Using the modeling abstraction, we obtain forms of the welfare-maximizing and revenue-maximizing auctions for such settings. We highlight how the form of the firms’ private information – whether they know the externalities they exert on others or that others exert on them – affects the structure of the optimal mechanisms. We find that in all cases, the optimal allocation rules turn out to be single thresholds (one per firm), in which the seller allocates all information or none of it to a firm. We demonstrate how externality affects both allocation of information and revenue generated through simple examples.

This work is done in collaboration with Anish Agarwal, Thibaut Horel, Maryann Rui.

Speaker bio: Munther A. Dahleh received his PhD from Rice University in 1987 in electrical and computer engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, where he is now the William A. Coolidge Professor of EECS. He is also a faculty affiliate of the Sloan School of Management. He is the founding director of the newly formed MIT Institute for Data, Systems, and Society (IDSS). Previously, he held the positions of associate department head of EECS, acting director of the Engineering Systems Division, and acting director of the Laboratory for Information and Decision Systems. He was a visiting professor at the Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, for the spring of 1993. Dahleh is a consultant for various research institutes and companies.

Dahleh leads a research group that focuses on decisions under uncertainty. He is interested in networked systems with applications to social and economic networks, financial networks, transportation networks, neural networks, and the power grid. Specifically, he focuses on the development of foundational theory necessary to understand, monitor, and control systemic risk in interconnected systems. He is also interested in learning high dimensional unstructured stochastic dynamic systems from finite data. Dahleh's recent work focused on understanding the economics of data as well as deriving a foundational theory for data markets. His work draws from various fields including algorithmic game theory, optimal control, distributed online optimization, information theory, and distributed learning. His collaborations include faculty from all five schools at MIT.

Dahleh is the co-author (with Ignacio Diaz-Bobillo) of the book Control of Uncertain Systems: A Linear Programming Approach, published by Prentice-Hall, and the co-author (with Nicola Elia) of the book Computational Methods for Controller Design, published by Springer. He is a four-time recipient of the George Axelby outstanding paper award for best paper in IEEE Transactions on Automatic Control. He is also the recipient of the Donald P. Eckman award from the American Control Council in 1993 for the best control engineer under 35. He is a fellow of IEEE and IFAC. He has given many keynote lectures at major conferences.

Faculty host: Mesrob I. Ohannessian, mesrob@uic.edu

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