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20122026
most citedZero-sum Risk-Sensitive Stochastic Games

48 citations · 357 across the 28 of their papers we have counts for

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16 papers · 1 filter

math.OC2026

Optimality of Threshold Policies for Heat Storage Optimization and a Policy Gradient Algorithm

Nicole Bäuerle, Florian Döttling

We study the optimal control of a stylized finite-horizon heat-storage system operated by a prosumer who can buy heat from and sell heat to a district heating network. Heat product…

math.OC2026

Policy stability and ultimate stationarity in discounted risk-sensitive stochastic control

Nicole Bäuerle, Marcin Pitera, Łukasz Stettner

We study discrete-time Markov Decision Processes (MDPs) on finite state-action spaces and analyze the stability of optimal policies and value functions in the long-run discounted r…

math.OC2024★ 3 cited

Blackwell optimality and policy stability for long-run risk sensitive stochastic control

Nicole Bäuerle, Marcin Pitera, Łukasz Stettner

This paper analyzes the stability of optimal policies in the long-run stochastic control framework with an averaged risk-sensitive criterion for discrete-time MDPs on finite state-…

math.OC2023★ 4 cited

Nash equilibria for relative investors with (non)linear price impact

Nicole Bäuerle, Tamara Göll

We consider the strategic interaction of investors who are able to influence a stock price process and at the same time measure their utilities relative to the other investors.…

math.OC2023★ 3 cited

Time-consistency in the mean-variance problem: A new perspective

Nicole Bäuerle, Anna Jaśkiewicz

We investigate discrete-time mean-variance portfolio selection problems viewed as a Markov decision process. We transform the problems into a new model with deterministic transitio…

math.OC2022

Finite Approximations for Mean Field Type Multi-Agent Control and Their Near Optimality

Erhan Bayraktar, Nicole Bauerle, Ali Devran Kara

We study a multi-agent mean field type control problem in discrete time where the agents aim to find a socially optimal strategy and where the state and action spaces for the agent…

math.OC2021★ 5 cited

Nash equilibria for relative investors via no-arbitrage arguments

Nicole Bäuerle, Tamara Göll

Within a common arbitrage-free semimartingale financial market we consider the problem of determining all Nash equilibrium investment strategies for agents who try to maximize…

math.OC2021★ 14 cited

Mean Field Markov Decision Processes

Nicole Bäuerle

We consider mean-field control problems in discrete time with discounted reward, infinite time horizon and compact state and action space. The existence of optimal policies is show…

math.OC2020★ 18 cited

Minimizing Spectral Risk Measures Applied to Markov Decision Processes

Nicole Bäuerle, Alexander Glauner

We study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon. The MDP i…

math.OC2020

Stochastic dynamic programming with non-linear discounting

Nicole Bäuerle, Anna Jaśkiewicz, Andrzej S. Nowak

In this paper, we study a Markov decision process with a non-linear discount function and with a Borel state space. We define a recursive discounted utility, which resembles non-ad…

math.OC2020★ 27 cited

Markov Decision Processes with Recursive Risk Measures

Nicole Bäuerle, Alexander Glauner

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost under both finite and infinite planning horizons. O…

math.OC2020★ 18 cited

Distributionally Robust Markov Decision Processes and their Connection to Risk Measures

Nicole Bäuerle, Alexander Glauner

We consider robust Markov Decision Processes with Borel state and action spaces, unbounded cost and finite time horizon. Our formulation leads to a Stackelberg game against nature.…

math.OC2020★ 21 cited

Robust Optimal Investment and Reinsurance Problems with Learning

Nicole Bäuerle, Gregor Leimcke

In this paper we consider an optimal investment and reinsurance problem with partially unknown model parameters which are allowed to be learned. The model includes multiple busines…

math.OC2019★ 8 cited

Markov Decision Processes under Ambiguity

Nicole Bäuerle, Ulrich Rieder

We consider statistical Markov Decision Processes where the decision maker is risk averse against model ambiguity. The latter is given by an unknown parameter which influences the…

math.OC2017★ 10 cited

Optimal Control of Partially Observable Piecewise Deterministic Markov Processes

Nicole Bäuerle, Dirk Lange

In this paper we consider a control problem for a Partially Observable Piecewise Deterministic Markov Process of the following type: After the jump of the process the controller re…

math.OC2016★ 48 cited

Zero-sum Risk-Sensitive Stochastic Games

Nicole Bäuerle, Ulrich Rieder

In this paper we consider two-person zero-sum risk-sensitive stochastic dynamic games with Borel state and action spaces and bounded reward. The term risk-sensitive refers to the f…