SEMINARS

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June 2019
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May 27, 2019 May 28, 2019 May 29, 2019 May 30, 2019 May 31, 2019 June 1, 2019 June 2, 2019
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June 10, 2019 June 11, 2019 June 12, 2019 June 13, 2019

Category: SeminarsHow much Quantum information can improve Social Welfare, by Mehdi Mhalla (LIG)

How much Quantum information can improve Social Welfare, by Mehdi Mhalla (LIG)
June 14, 2019 June 15, 2019 June 16, 2019
June 17, 2019 June 18, 2019 June 19, 2019 June 20, 2019

Category: SeminarsIf Agents Could Talk… What Should They Say? by Jason Marden (Univ of California, Santa Barbara)

If Agents Could Talk… What Should They Say? by Jason Marden (Univ of California, Santa Barbara)
June 21, 2019 June 22, 2019 June 23, 2019
June 24, 2019 June 25, 2019 June 26, 2019 June 27, 2019 June 28, 2019 June 29, 2019 June 30, 2019
  • June 13, 2019 @ Bâtiment IMAG (406) -- How much Quantum information can improve Social Welfare, by Mehdi Mhalla (LIG)

    How much Quantum information can improve Social Welfare.

    Abstract:
    It has been shown in [1] that quantum resources can allow to achieve a family of Nash equilibria that can have sometimes a better social welfare while guaranteeing privacy.
    We use graph games to propose a way to build non cooperative games from graph states and show how we can achieve an unlimited improvement with the quantum advices compared to the classical one. We also discuss the notion of guaranteed utility that these games provide.

    This is a work in progress with Berry Groisman Michael Mc Gettrick and Marcin Pawlowski.

    [1] Vincenzo Auletta, Diodato Ferraioli, Ashutosh Rai, Giannicola Scarpa, and Andreas Winter. Belief-invariant and quantum equilibria in games of incomplete information. arXiv preprint arXiv:1605.07896, 2016.

  • June 20, 2019 @ Bâtiment IMAG (406) -- If Agents Could Talk… What Should They Say? by Jason Marden (Univ of California, Santa Barbara)

    Title: If Agents Could Talk… What Should They Say?

    Abstract: The goal in networked control of multiagent systems is to derive desirable collective behavior through the design of local control algorithms. The information available to the individual agents, either through sensing or communication, invariably defines the space of admissible control laws. Hence, informational restrictions impose constraints on the achievable performance guarantees. The first part of this talk will provide one such constraint with regards to the efficiency of the resulting stable solutions for a class of distributed submodular optimization problems. Further, we will also discuss how strategic information exchange can help mitigate these degradations. The second part of this talk will focus on how agents should utilize available information to optimize the efficiency of the emergent collective behavior. In particular, we will discuss a methodology for optimizing the efficiency guarantees (i.e., price of anarchy) in distributed resource allocation problems through the design of local agent objective functions. Lastly, we will highlight some unintended consequences associated with these optimal designed agent objective functions – optimizing the performance of the worst-case equilibria (i.e., price of anarchy) often comes at the expense of the best-case equilibria (i.e., price of stability).

    Bio: Jason R. Marden is an Associate Professor in the Department of Electrical and Computer, Engineering at the University of California, Santa Barbara. Jason received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA, under the supervision of Jeff S. Shamma, where he was awarded the Outstanding Graduating PhD Student in Mechanical Engineering. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason is a recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), the AFOSR Young Investigator Award (2012), the American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015). Furthermore, Jason is also an advisor for the students selected as finalists for the best student paper award at the IEEE Conference on Decision and Control (2011, 2016, 2017). Jason's research interests focus on game theoretic methods for the control of distributed multiagent systems.

  • September 12, 2019 @ -- MFG or MDP by Ziliotto Bruno (CNRS, Paris)

    TBA

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