SEMINARS

Events in November–December 2022

Monday Tuesday Wednesday Thursday Friday Saturday Sunday
October 31, 2022 November 1, 2022 November 2, 2022 November 3, 2022 November 4, 2022 November 5, 2022 November 6, 2022
November 7, 2022 November 8, 2022 November 9, 2022 November 10, 2022(1 event)

Category: SeminarsVerimag seminar: Stephan Plassart

November 11, 2022 November 12, 2022 November 13, 2022
November 14, 2022 November 15, 2022 November 16, 2022 November 17, 2022(1 event)

Category: SeminarsSeminar Olivier Bilenne

November 18, 2022 November 19, 2022 November 20, 2022
November 21, 2022 November 22, 2022 November 23, 2022 November 24, 2022 November 25, 2022 November 26, 2022 November 27, 2022
November 28, 2022 November 29, 2022 November 30, 2022 December 1, 2022(1 event)

Category: SeminarsSéminaire Stephane Durand

December 2, 2022 December 3, 2022 December 4, 2022
  • October 27, 2022 @ Bâtiment IMAG (406) -- Seminar Bryce Ferguson: "Information and Influence: Overcoming and Exploiting Uncertainty in Congestion Games"
    Title: Information and Influence: Overcoming and Exploiting Uncertainty in Congestion Games
    Abstract: In large-scale, socio-technical systems (such as traffic networks, power grids, supply chains, etc.) the operating efficiency depends heavily on the actions of human users. It is well known that when users act in their own self-interest, system performance can be sub-optimal. Our capabilities in alleviating this inefficiency rely on our knowledge of user decision making and various system parameters. In this talk, I will present two settings where information affects our ability to influence system performance. In the first, we consider designing monetary incentives for players in a congestion game without exact knowledge of users’ price-sensitivity or path latency-characteristics; we provide a comparison of the effectiveness of different incentive types and quantify the value of different pieces of information. In the second, we consider a flipped paradigm, where the system operator has more information about system parameters than the users and can selectively reveal pertinent information. We show, in the context of Bayesian-congestion games, that signaling information to users has the opportunity to improve system performance but also the capability to make performance worse than if no information were shared at all. We then show the advantages of concurrently using monetary incentives and information signals by providing bounds on the benefit to system performance and methods to find optimal mechanisms of each type.
  • November 10, 2022 @ Bâtiment IMAG (206) -- Verimag seminar: Stephan Plassart

    Total Flow Analysis (TFA) is a method for conducting the worst-case analysis of time sensitive networks without cyclic dependencies. In networks with cyclic dependencies, Fixed-Point TFA introduces artificial cuts, analyses the resulting cycle-free network with TFA, and iterates. If it converges, it does provide valid performance bounds. We show that the choice of the specific cuts used by Fixed-Point TFA does not affect its convergence nor the obtained performance bounds, and that it can be replaced by an alternative algorithm that does not use any cut at all, while still applying to cyclic dependencies.

    Room: 206

  • November 17, 2022 @ -- Seminar Olivier Bilenne
    Solutions of Poisson's equation for first-policy improvement in parallel queueing systems
    This talk addresses the problem of (state-aware) job dispatching at minimum long-run average cost in a parallel queueing system with Poisson arrivals. Policy iteration is a technique for approaching optimality through improvement of an initial dispatching policy. Its implementation rests on the computation of value functions. In this context, we will consider the M/G/1-FCFS queue endowed with an arbitrary cost function for the waiting times of the incoming jobs. The associated relative value function is a solution of Poisson's equation for Markov chains, which I propose to solve in the Laplace transform domain by considering an ancillary stochastic process extended to (imaginary) negative backlog states. This construction enables us to issue closed-form solutions for simple cost functions (polynomial, exponential, and their piecewise compositions), in turn permitting the derivation of interval bounds for the relative value functions to more general cost functions. Such bounds allow for an exact implementation of the first improvement step of policy iteration in a parallel queueing system.
    One objective of the talk is to identify the main obstacles to the implementation of the policy iteration algorithm in parallel queueing systems; the purpose then to discuss the new directions that transform domain analysis might offer beyond first policy improvement.
    Further reading: Olivier Bilenne. Dispatching to parallel servers: solutions of Poisson's equation for first-policy improvement. Queueing Systems, Springer Verlag, 2021, Queueing Systems, 99 (3), pp.199-230. https://hal.archives-ouvertes.fr/hal-02925284
  • December 1, 2022 @ -- Séminaire Stephane Durand

    Jeux de contagions et d'influences: les différentes formes, les approches et le contexte

  • December 8, 2022 @ -- Seminar Mario Bravo (room 106)
  • December 8, 2022 @ Bâtiment IMAG (406) -- Séminaire GLSI / CtrlA: Quentin Guilloteau (Datamove)

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