Title: Information Markets as Decision Systems
Time & Venue: December 19, 2025 09:00 – 10:00 hrs IST; Biological Science Auditorium (Department of Biological Sciences, IISc)
Abstract:
Information design has gained in importance as sellers (data aggregators) are able to incentivize certain behaviors from competitive Buyers (firms) to increase social welfare or to sell this information to increase their profits. However, in both situations, optimal design is limited by the firms’ private information about their payoffs. To elicit such private information, sellers design mechanisms (e.g., auctions) whereby the firms are incentivized to both participate and to provide truthful information to the seller. This combined Information and Mechanism Design problem, which we refer to as Information Markets sits at the heart of many interesting applications involving smart infrastructures where information intermediaries regulate a physical layer through coordination.
The challenge in creating such an information marketplace stems from the very nature of information as an asset: (i) it can be replicated at zero marginal cost; (ii) it can be versioned with noise, (iii) its value to a firm is dependent on which other firms get access to such information (externality); and (iv) its value to a firm is heterogeneous.
We present a general framework involving N competing private firms (agents) and a monopolistic information seller. The asymmetry of information creates an opportunity to coordinate the actions of these firms in order to maximize social welfare. To illustrate the power of such formulations, we highlight applications in (i) electric vehicle charging, and (ii) eco-driving.
We also analyze the case in which the monopolistic information seller seeks to extract payments from the firms to maximize its own revenue. We contrast this allocation with the welfare-maximizing one and demonstrate how competition increases the seller’s opportunities to capture profit.
Finally, we discuss how such mechanisms for information allocation may raise concerns of anti-trust. We provide examples that connect to the current state of such regulations and propose approaches to mitigate the risks associated with information-based market power.
Speaker Bio:
Munther A. Dahleh received his B.S. in Electrical Engineering from TAMU in 1983, Ph.D. degree from Rice University, Houston, TX, in 1987 in Electrical and Computer Engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, 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 MIT Institute for Data, Systems, and Society (IDSS). He serves on multiple advisory boards including AI advisory board for Samsung and Ikigai. He is the author of the recent book: Data, Systems, and Society: Harnessing AI for Societal Good, Cambridge University Press, April 2025.
Prof. Dahleh Leads a research group that focuses on Decisions Under Uncertainty. He is interested in Networked Systems, information design, and decision theory with applications to Social and Economic Networks, financial networks, Transportation Networks, Neural Networks, agriculture, and the Power Grid. He is also interested in causal learning (machine learning, reinforcement learning) for the purpose of intervention and control. His recent work focused on understanding the economics of data as well as deriving a foundational theory for data markets. Prof. Dahleh is a leader in online education focusing on advanced data science topics as well as professional education in machine learning and AI.
Prof. Dahleh is a four time recipient of the George S. Axelby best paper award for papers published in the IEEE Transactions on Automatic Control. He is also a recipient of the Donald P. Eckman award for the best control engineer under 35. He is a fellow of IEEE and IFAC.