arXiv research

We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.

This paper tackles learning Stackelberg equilibrium in asymmetric games efficiently from noisy samples.

Study optimal investment-reinsurance strategies in equity-linked insurance products using Stackelberg game theory.

Study on local convergence of min-max algorithms to differential equilibria on Riemannian manifolds.

This paper analyzes a game between insurer and reinsurer under ambiguity and risk aversion, optimizing reinsurance and investment strategies.

Two-layer model studies reinsurance contracts and competition between insurer and reinsurers.

Endogenous reinsurance pricing in large insurance markets

An informed broker optimizes trading strategies in a market influenced by many traders.

Study of insurance market equilibria with risk-averse policyholders.

Investors with asymmetric information play a game to optimize their portfolios.

New RL algorithms find SNE in Markov games with myopic followers.

Brokers and an informed trader compete for liquidity, affecting trading costs and inventory risk.

This paper investigates a hybrid stochastic differential reinsurance and investment game between one reinsurer and two insurers, including a stochastic Stackelberg differential subgame and a non-zero-sum stochastic differential subgame. The reinsurer, as the leader of the Stackelberg game, can price reinsurance premium…

The paper analyzes strategic interactions in a multi-agent reinsurance chain using game theory.

New RL algorithms learn QSE from strategic feedbacks with sample efficiency.

Paper proposes a game-theoretic approach to generate unlearnable examples.

Optimal penalties for RECs balance environmental and revenue impacts.

Game theory model for optimal trading with end-of-day constraints.

Study of $2 imes 2$ zero-sum games with noisy observations and commitments.

Neural operators approximate Stackelberg game solutions.

We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we…

Paper proposes incentive mechanism to encourage participation in federated learning.

Paper analyzes convergence of GDA for nonconvex-nonconcave minimax problems.

The paper introduces Robust Correlated Equilibrium for games with time-varying costs and proposes an algorithm to achieve it.

The paper analyzes reinsurance strategies in a competitive multi-agent system.

Optimal dynamic allocation of carbon allowances reduces emissions efficiently.

Study on optimal trading in a finite population with market frictions and asymmetric information.

A new algorithm learns policies from batch data in hierarchical RL.

Paper proposes a new method to optimize robot body structure and control policy.

Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.

SLHF uses sequential game theory to optimize preferences from human feedback.

For an investor with constant absolute risk aversion and a long horizon, who trades in a market with constant investment opportunities and small proportional transaction costs, we obtain explicitly the optimal investment policy, its implied welfare, liquidity premium, and trading volume. We identify these quantities as…

RL models improve target control in SSGs for security applications.

We consider a stochastic game between a trader and a central bank in a target zone market with a lower currency peg. This currency peg is maintained by the central bank through the generation of permanent price impact, thereby aggregating an ever increasing risky position in foreign reserves. We describe this situation…

Study on reinsurance decisions using mean-variance criterion with irreversible contracts.

MAXMINLCB optimizes unknown target functions with preference feedback using a Stackelberg game approach.

Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorit…

DREAM learns optimal strategies in imperfect games without needing a simulator.

We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify t…

Path-independent equilibrium models improve network performance on harder problems.

This paper improves sample efficiency for learning equilibria in multi-player games.

This paper introduces Schur-constant equilibrium distribution models of dimension n for arithmetic non-negative random variables. Such a model is defined through the (several orders) equilibrium distributions of a univariate survival function. First, the bivariate case is considered and analyzed in depth, stressing the…

We study optimal behavior of energy producers under a CO_2 emission abatement program. We focus on a two-player discrete-time model where each producer is sequentially optimizing her emission and production schedules. The game-theoretic aspect is captured through a reduced-form price-impact model for the CO_2 allowance…

Modeling DEX liquidity with heterogeneous LPs and MEV bots.

Models analyze strategic risk-taking in continuous action games.

Correlations and other collective phenomena in a schematic model of heterogeneous binary agents (individual spin-glass samples) are considered on the complete graph and also on 2d and 3d regular lattices. The system's stochastic dynamics is studied by numerical simulations. The dynamics is so slow that one can meaningf…

Study efficient offline RL in Markov games with general models.

Optimal algorithm for two-player zero-sum games with linear parameterization.