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arXiv · 2403.18164

Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems

Jair Certório·Nuno C. Martins·Richard J. La·Murat Arcak
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Abstract

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.

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Jair Certório, Nuno C. Martins, Richard J. La, Murat Arcak. 2024-03-27. Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems. https://arxiv.org/abs/2403.18164

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