Hybrid Human-Agent Social Dilemmas in Energy Markets

2026-03-12

AI Summary

This study addresses the challenge of coordination failures in energy markets populated by human users and autonomous agents, which can lead to congestion and social dilemmas. The authors propose a coordination mechanism leveraging globally observable signals to guide heterogeneous populations toward cooperative scheduling under dynamic pricing schemes. By integrating reinforcement learning with evolutionary dynamics, the mechanism designs agent strategies that effectively steer the system toward a coordinated equilibrium—even at low adoption rates—yielding substantial improvements in overall performance. The analysis further uncovers a strategic externality wherein non-adopters may disproportionately benefit from the presence of adopters, highlighting critical implications for mechanism design in partially deployed settings.

Technology Category

Multiagent Systems: Mechanism DesignGame Theory and Economic Paradigms: Coordination and CollaborationHumans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsResponsible Web: Machine-in-the-loop, human agency and autonomyUser Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

In hybrid populations where humans delegate strategic decision-making to autonomous agents, understanding when and how cooperative behaviors can emerge remains a key challenge. We study this problem in the context of energy load management: consumer agents schedule their appliance use under demand-dependent pricing. This structure can create a social dilemma where everybody would benefit from coordination, but in equilibrium agents often choose to incur the congestion costs that cooperative turn-taking would avoid. To address the problem of coordination, we introduce artificial agents that use globally observable signals to increase coordination. Using evolutionary dynamics, and reinforcement learning experiments, we show that artificial agents can shift the learning dynamics to favour coordination outcomes. An often neglected problem is partial adoption: what happens when the technology of artificial agents is in the early adoption stages? We analyze mixed populations of adopters and non-adopters, demonstrating that unilateral entry is feasible: adopters are not structurally penalized, and partial adoption can still improve aggregate outcomes. However, in some parameter regimes, non-adopters may benefit disproportionately from the cooperation induced by adopters. This asymmetry, while not precluding beneficial entry, warrants consideration in deployment, and highlights strategic issues around the adoption of AI technology in multiagent settings.

Problem

• social dilemma
• energy markets
• human-agent collaboration
• partial adoption
• coordination

Innovation

• hybrid human-agent systems
• social dilemma
• coordination via global signals
• partial AI adoption
• reinforcement learning in energy markets