Hybrid Human-Agent Social Dilemmas in Energy Markets

TL;DR

This paper explores how artificial agents can promote cooperation in energy markets with human participants, analyzing the effects of partial adoption and strategic interactions using evolutionary and reinforcement learning methods.

Contribution

It introduces artificial agents using observable signals to enhance coordination in hybrid human-agent energy load management systems and examines the impact of partial adoption.

Findings

Artificial agents can effectively promote coordination among agents.

Partial adoption of artificial agents can improve overall outcomes.

Non-adopters may benefit from the cooperation induced by adopters.

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.