Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems
<|reference_start|>Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems: We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states.<|reference_end|>
@article{certório2024incentive,
    title={Incentive Designs for Learning Agents to Stabilize Coupled Exogenous
  Systems},
    author={Jair Cert'orio, Nuno C. Martins, Richard J. La, Murat Arcak},
    journal={arXiv preprint arXiv:2403.18164},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18164},
    primaryClass={eess.SY cs.SY math.DS math.OC}
}