6 papers
Robust -learning for mean-field control under Wasserstein uncertainty in common noise
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
In this article, we present a robust -learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law. The algorithm combi…
Robust mean-field control under common noise uncertainty
Mathieu Laurière, Ariel Neufeld, Kyunghyun Park
We propose and analyze a framework for discrete-time robust mean-field control problems under common noise uncertainty. In this framework, the mean-field interaction describes the…
Scaling limits of multi-period distributionally robust optimization problems
Max Nendel, Ariel Neufeld, Kyunghyun Park +1
We examine the scaling limit of multi-period distributionally robust optimization (DRO) problems via a semigroup approach. Each period involves a worst-case maximization over distr…
Markov-Nash equilibria in mean-field games under model uncertainty
Johannes Langner, Ariel Neufeld, Kyunghyun Park
We propose and analyze a framework for mean-field Markov games under model uncertainty. In this framework, a state-measure flow describing the collective behavior of a population a…
Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis
Daniel Bartl, Ariel Neufeld, Kyunghyun Park
We examine nonlinear Kolmogorov partial differential equations (PDEs). Here the nonlinear part of the PDE comes from its Hamiltonian where one maximizes over all possible drift and…
Sensitivity of robust optimization problems under drift and volatility uncertainty
Daniel Bartl, Ariel Neufeld, Kyunghyun Park
We examine optimization problems in which an investor has the opportunity to trade in stocks with the goal of maximizing her worst-case cost of cumulative gains and losses. Her…