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SUMMARY:Information Design under Uncertainty
DTSTART:20240209T093000
DTEND:20240209T103000
DTSTAMP:20260410T131643Z
UID:565b0f223293ab0e541be43d1ce3541306b0c0453fd27d69201ce30a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Professor Munther Dahleh\, MIT\, Cambridge\, MA\, USA\nAbstrac
 t: \nInformation design has gained in importance as sellers (data aggrega
 tors) are able to incentivize certain behaviors from competitive Buyers (f
 irms) to increase social welfare or to sell this information to increase t
 heir profits. However\, in both situations\, optimal design is limited by 
 the firms’ private information about their payoffs. To elicit such priva
 te information\, sellers design mechanisms (e.g.\, auctions) whereby the f
 irms are incentivized to both participate and to provide truthful informat
 ion to the seller. This combined Information and Mechanism Design probl
 em sits at the heart of many interesting applications involving informatio
 n sales under uncertainty. \n\nThe challenge in creating such an informat
 ion 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.\n\nIn this talk\, I consider the cas
 e with N competing firms and a monopolistic information seller. I address 
 an important property of such markets that has been given limited consider
 ation thus far\, namely the externality faced by a firm when information i
 s allocated to other\, competing firms. Addressing this is likely necessar
 y for progress towards the practical implementation of such markets. I con
 sider 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 t
 he impact of competition on the revenue collected by the seller.  In the
  second model\, I will dig deeper into the allocation of information and h
 ighlight an interesting strategy of obfuscation followed by the seller i
 n certain regimes. I will explain why buyers will continue to participate 
 in this mechanism despite such obfuscation. \nFinally\, I will discuss so
 me extensions of this work to learning in mechanisms and in dynamic mec
 hanism designs.\nThis work is done in collaboration with Alessando Bonatti
 \, Thibaut Horel\, Amir Nouripour\, Anish Agarwal\, Maryann Rui. \n\nBIO:
  \nMunther A. Dahleh received his B.S. in Electrical Engineering from TAM
 U in 1983\, Ph.D. degree from Rice University\, Houston\, TX\, in 1987 in 
 Electrical and Computer Engineering. Since then\, he has been with the Dep
 artment of Electrical Engineering and Computer Science (EECS)\, MIT\, Camb
 ridge\, 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 (I
 DSS). \n\nProf. Dahleh Leads a research group that focuses on Decisions U
 nder Uncertainty. He is interested in Networked Systems\, information desi
 gn\, and decision theory  with applications to Social and Economic Netwo
 rks\, financial networks\, Transportation Networks\, Neural Networks\, agr
 iculture\, and the Power Grid. He is also interested in causal learning fo
 r the purpose of intervention and control. His recent work focused on unde
 rstanding the economics of data as well as deriving a foundational theory 
 for data markets. He is a fellow of IEEE and IFAC. \n 
LOCATION:ME B3 31 https://plan.epfl.ch/?room==ME%20B3%2031
STATUS:CONFIRMED
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