Information Design under Uncertainty

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 sits at the heart of many interesting applications involving information sales under uncertainty. 

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) its value to a firm is dependent on which other firms get access to such information; and (iii) its value to a firm is heterogeneous.

In this talk, I consider the case with N competing firms and a monopolistic information seller. I address an important property of such markets that has been given limited consideration thus far, namely the externality faced by a firm when information is allocated to other, competing firms. Addressing this is likely necessary for progress towards the practical implementation of such markets. I consider two models for such externality; the first is a direct linear model of utilities, and the second is a downstream game that the firms compete in. I will describe the mechanisms for both models and I will highlight the impact of competition on the revenue collected by the seller.  In the second model, I will dig deeper into the allocation of information and highlight an interesting strategy of obfuscation followed by the seller in certain regimes. I will explain why buyers will continue to participate in this mechanism despite such obfuscation. 
Finally, I will discuss some extensions of this work to learning in mechanisms and in dynamic mechanism designs.
This work is done in collaboration with Alessando Bonatti, Thibaut Horel, Amir Nouripour, Anish Agarwal, Maryann Rui. 

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). 

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 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. He is a fellow of IEEE and IFAC.