Optimization and Equilibrium

A stochastic differential Colonel Blotto game in a Stackelberg contract theory setting.

Event Date: Jan 22, 2025 in Optimization and Equilibrium, Seminars

Abstract: The Colonel Blotto game is a resource allocation game where players decide where to focus their forces between different battlefields. We extend the standard Blotto game to a dynamic stochastic setting, in a time-continuous, two-player, zero-sum game. Using the dynamic programming principle, we explicitly characterize some Nash equilibrium strategies as well as the value of the game through a Hamilton-Jacobi-Bellman equation admitting a smooth solution. We formulate the game generally enough to allow for various rewards, as well as various drivers of randomness. We also present an...

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Mean field games with heterogeneity.

Event Date: Dec 16, 2024 in Optimization and Equilibrium, Seminars

Abstract: Mean field games (MFGs) are an extension of interacting particle systems, where the particles are interpreted as rational agents, offering applications in economics, social sciences, or computer science. They can be seen as the limits of large-population stochastic differential games with symmetric agents. In this work, we propose a method to incorporate heterogeneity into MFGs, thus relaxing the symmetry assumptions. We will present the concept of heterogeneous Markovian equilibria and provide a proof of their existence under standard conditions. Our definition of Nash Mean Field...

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Regret analysis for stochastic optimization problems under parametric models.

Event Date: Nov 13, 2024 in Optimization and Equilibrium, Seminars

Abstract: In this talk, we analyze the growth rate of the regret -or optimality gap- when learning the optimal actions in stochastic optimization problems, formulated in a parametric setting. More precisely, we assume access to samples from random variables whose unknown distribution belongs to a parametric family. For both smooth and non-smooth problems, we describe the asymptotic behavior of the expected optimality gap, and use it to design appropriate estimators. Different examples will be given where explicit calculations are possible.

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Dynamical systems associated with a class of quasiconvex functions.

Event Date: Oct 16, 2024 in Optimization and Equilibrium, Seminars

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Completely mixed linear games and irreducibility concepts for Z-transformations over self-dual cones.

Event Date: Sep 25, 2024 in Optimization and Equilibrium, Seminars

Abstract:    In the setting of a self-dual cone in a finite-dimensional real inner product space (in particular, over a symmetric cone in an Euclidean Jordan algebra), we consider zero-sum linear games.  Motivated by dynamical systems, we concentrate on $Z$-transformations (which are generalizations of $Z$-matrices). It is known that a $Z$-transformation with positive (game) value is completely mixed (thus yielding uniqueness of optimal strategies). The present talk deals with the case of value zero. Motivated by the matrix-game result that a $Z$-matrix with value zero is completely...

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All convex bodies in the subdifferential of a locally Lipschitz function.

Event Date: Jun 26, 2024 in Optimization and Equilibrium, Seminars

Abstract: We construct a differentiable locally Lipschitz function f in R^d with the following property: for every convex body K of R^d, there exists x in R^d such that the subdifferential of f at x coincides with K (in the sense of limiting or Clarke). We show that our technique can be further refined to recover all compact connected subsets with nonempty interior in the image of the limiting subdifferential of a locally Lipschitz function. We end this talk with a brief discussion about how large the set of functions with the aforementioned property is.

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