Payoff Dynamics Model and Evolutionary Dynamics Model: Feedback and Convergence to Equilibria

TL;DR

This paper introduces a unified, system-theoretic framework for analyzing the stability and convergence of strategies in large populations of bounded rational agents engaged in evolutionary and population games.

Contribution

It generalizes existing models by incorporating a broad class of payoff mechanisms within a formal dynamical systems approach, enabling rigorous stability analysis.

Findings

Provides a systematic methodology for stability characterization

Extends analysis to general classes of payoff mechanisms

Includes proofs and practical examples

Abstract

This tutorial article puts forth a framework to analyze the noncooperative strategic interactions among the members of a large population of bounded rationality agents. Our approach hinges on, unifies and generalizes existing methods and models predicated in evolutionary and population games. It does so by adopting a system-theoretic formalism that is well-suited for a broad engineering audience familiar with the basic tenets of nonlinear dynamical systems, Lyapunov stability, storage functions, and passivity. The framework is pertinent for engineering applications in which a large number of agents have the authority to select and repeatedly revise their strategies. A mechanism that is inherent to the problem at hand or is designed and implemented by a coordinator ascribes a payoff to each possible strategy. Typically, the agents will prioritize switching to strategies whose payoff is either higher than the current one or exceeds the population average. The article puts forth a systematic methodology to characterize the stability of the dynamical system that results from the feedback interaction between the payoff mechanism and the revision process. This is important because the set of stable equilibria is an accurate predictor of the population's long-term behavior. The article includes rigorous proofs and examples of application of the stability results, which also extend the state of the art because, unlike previously published work, they allow for a rather general class of dynamical payoff mechanisms. The new results and concepts proposed here are thoroughly compared to previous work, methods and applications of evolutionary and population games.