Equilibria under Dynamic Benchmark Consistency in Non-Stationary Multi-Agent Systems

TL;DR

This paper investigates how multi-agent systems in dynamic, non-stationary environments can achieve equilibrium-like behavior by competing against evolving benchmarks, leading to improved welfare guarantees.

Contribution

It introduces a novel analysis of dynamic benchmark consistent policies and their ability to approximate evolving equilibrium sequences in non-stationary multi-agent systems.

Findings

Distributions of play approximate static equilibria in slowly changing systems.

Dynamic benchmark policies outperform static ones in non-stationary settings.

Improved welfare bounds are established for smooth games.

Abstract

We formulate and study a general time-varying multi-agent system where players repeatedly compete under incomplete information. Our work is motivated by scenarios commonly observed in online advertising and retail marketplaces, where agents and platform designers optimize algorithmic decision-making in dynamic competitive settings. In these systems, no-regret algorithms that provide guarantees relative to \emph{static} benchmarks can perform poorly and the distributions of play that emerge from their interaction do not correspond anymore to static solution concepts such as coarse correlated equilibria. Instead, we analyze the interaction of \textit{dynamic benchmark} consistent policies that have performance guarantees relative to \emph{dynamic} sequences of actions, and through a novel \textit{tracking error} notion we delineate when their empirical joint distribution of play can approximate an evolving sequence of static equilibria. In systems that change sufficiently slowly (sub-linearly in the horizon length), we show that the resulting distributions of play approximate the sequence of coarse correlated equilibria, and apply this result to establish improved welfare bounds for smooth games. On a similar vein, we formulate internal dynamic benchmark consistent policies and establish that they approximate sequences of correlated equilibria. Our findings therefore suggest that in a broad range of multi-agent systems where non-stationarity is prevalent, algorithms designed to compete with dynamic benchmarks can improve both individual and welfare guarantees, and their emerging dynamics approximate a sequence of static equilibrium outcomes.