Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents

TL;DR

This paper studies multi-agent performative prediction, establishing conditions for unique stable solutions, and proposes a decentralized greedy deployment algorithm with proven convergence, validated by numerical experiments.

Contribution

It formulates Multi-PfD as a decentralized optimization problem, derives conditions for stability, and introduces a convergent decentralized greedy deployment scheme.

Findings

Unique multi-agent performative stable solutions exist under certain conditions.

Consensus enforcement relaxes distribution sensitivity requirements.

The DSGD-GD scheme converges to the Multi-PS solution with proven rates.

Abstract

We consider a scenario where multiple agents are learning a common decision vector from data which can be influenced by the agents' decisions. This leads to the problem of multi-agent performative prediction (Multi-PfD). In this paper, we formulate Multi-PfD as a decentralized optimization problem that minimizes a sum of loss functions, where each loss function is based on a distribution influenced by the local decision vector. We first prove the necessary and sufficient condition for the Multi-PfD problem to admit a unique multi-agent performative stable (Multi-PS) solution. We show that enforcing consensus leads to a laxer condition for the existence of Multi-PS solution with respect to the distributions' sensitivities, compared to the single agent case. Then, we study a decentralized extension to the greedy deployment scheme [Mendler-D\"unner et al., 2020], called the DSGD-GD scheme. We show that DSGD-GD converges to the Multi-PS solution and analyze its non-asymptotic convergence rate. Numerical results validate our analysis.