Distributed Learning of Pricing and Discount Policies from Interacting Revenue Recovery Agents

Abstract

Revenue recovery processes for subscription, credit, and utility services increasingly rely on large collections of autonomous or semi-autonomous agents interacting with customers under diverse contractual, regulatory, and behavioral conditions. These agents offer payment plans, temporary discounts, and fee waivers in an attempt to restore revenue while satisfying operational and fairness constraints. The design of effective pricing and discount policies in this setting is complicated by limited observability, heterogeneous customer responses, and strong coupling among agents through shared budgets, risk limits, and regulatory caps on concessions. Centralized optimization approaches may be difficult to deploy when data are fragmented across business units or jurisdictions and when system operators require local autonomy of existing revenue recovery teams and tools. This paper investigates distributed learning mechanisms that infer pricing and discount policies from the behavior of interacting revenue recovery agents and from their long-run performance. The discussion formulates the problem as a multi-agent sequential decision process with linearly parameterized value functions and linear constraints capturing budget, exposure, and regulatory requirements. A distributed learning architecture is introduced in which agents update local policy parameters from their own interaction histories while exchanging low-dimensional aggregate statistics that gradually coordinate their policies. Analytical results describe the structure of the induced linear systems, properties of the distributed fixed points, and conditions under which the learning dynamics remain stable. Numerical experiments on stylized revenue recovery scenarios illustrate how local exploration, policy heterogeneity, and different coordination rates influence the evolution of pricing and discount policies.