Lillian Ratliff, 1-2 Feb 2016

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Lillian Ratfliff will be visiting Caltech on 1-2 Feb 2016. Please sign up below if you would like to meet with her.


1 Feb 2016 (Mon)

  • Noon: arrive at BUR. Travel to Pasadena (Caltech car)
  • 1 pm: Yisong and John Doyle (lunch) - meet at the Ath
  • 2 pm: Pietro Perona
  • 2:45 pm: open
  • 3:30 pm: open
  • 4:00 pm: Seminar - Munzer Dahleh
  • 5 pm: done for the day
  • 6:30 pm: meet in the lobby of the Ath for dinner with Nik Matni, Vanessa Jonsson, Dj Krishanmurth (location TBD)

2 Feb 2016 (Tue)

  • 7:45 am: breakfast with Richard (Ath)
  • 8:45 am: open
  • 9:30 am: Dj
  • 10:15 am: Adam W
  • 11:00 am: Nikolai M
  • 11:45 am: seminar setup
  • 12:00 pm: seminar, 104 ANB
  • 1 pm: lunch with Richard (Chandler)
  • 1:45 pm: Vanessa Jonsson
  • 2:30 pm: Joel Tropp
  • 3:15 pm: Niangjun Chen
  • 4:00 pm: open
  • 4:45 pm: open
  • 5:30 pm: done for the day
  • 6:15 pm: depart from Ath for BUR (Caltech car)

Seminar: 2 Feb (Wed), 12-1 pm, 105 ANB

TITLE: The Emerging Data Market: Adaptive Incentives for Smart, Connected Infrastructure

ABSTRACT: The next generation urban ecosystem empowered by the internet of things has at its core a shared economy where physical resources and data are easily aggregated and exchanged. In particular, advances in technology have lead to the proliferation of smart devices that provide access to streaming data and platforms for novel sharing mechanisms. This has, in turn, resulted in an emerging marketplace in which data is a commodity. At the same time, many urban constituents are increasingly becoming aware of the value of their data and its usefulness for operations. In such an environment, new learning and optimization schemes which consider users as strategic data sources and resource seekers are needed. In this talk, we will discuss the emerging data market, its incentive structure (players and their motivations), and tools for learning with strategic data sources. Focusing on the design of adaptive incentive mechanisms under adverse selection, we will construct an algorithm for online utility learning and incentive design and show convergence results for both the case where players are rational (play according to Nash) and myopic. We will see through a tutorial example how the algorithm performs, and conclude with some open questions and future directions.