Hybrid Human-Agent Social Dilemmas in Energy Markets

Isuri Perera; Frits de Nijs; Julian Garcia

cs.MAMar 12, 2026

Hybrid Human-Agent Social Dilemmas in Energy Markets

Authors: Isuri Perera, Frits de Nijs, Julian Garcia

Organizations: Department of Data Science and AI, Faculty of Information Technology Monash University, Melbourne, Australia

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.

Figures & tables

Figure 1: Centralized vs Decentralized outcomes for 130 testing days across different population sizes. Selfish agents learn subobtimal policies.

	PST	Away
PST	$1$ , $1$	$0$ , $1+p$
Away	$1+p$ , $0$	$2+p$ , $2+p$

Table 1: Two-player, two-action normal-form game of load management.

Figure 2: All possible equilibrium payoffs of the repeated games as the inconvenience cost

p

varies. The shaded region illustrates mutually beneficial equilibrium payoffs; larger inconvenience costs shrink the space of cooperative equilibria.

Figure 3: Convergence of

1000

simulations of replicator dynamics with

p=0.5

for two populations P1 and P2, comparing low versus high patience regimes. (a) For

\delta=0.51

, the system predominantly converges to mixtures of PPP and PPA, resulting in non-cooperative outcomes. (b) For

\delta=0.95

, a greater proportion of simulations converge to the cooperative turn-taking equilibria (PPA, APA).

Figure 4: Average trajectory of system cost with intrinsic reward terms in the full DSLM/RL environment. (a) Basic scenario: agents adapt by alternating their actions, demonstrating turn-taking. (b) 4-appliance scenario: the intrinsic reward bonus enables agents to more frequently identify the socially optimal Nash equilibrium.

Figure 5: Convergence of

1000

simulations of replicator dynamics with

p=0.5

and intrinsic reward terms (

\Omega=100

) for two populations P1 and P2. (a) For

\delta=0.51

, the system converges to cooperative equilibria, in contrast to the non-cooperative outcome without intrinsic rewards (Figure 3 a). (b) For

\delta=0.95

, cooperative convergence is similarly achieved.

Panel of Figure 6 — Figure 6: Average population cost for varying $\delta$ ( $\Omega=100$ , $p=0.5$ ). (a) Both populations play the basic game (blue) or the game with intrinsic reward terms (red). (b) Population $P_{1}$ considers basic payoffs, $P_{2}$ considers payoffs with intrinsic reward terms.

Multi-Agent Coordination

Multi-agent coordination studies how autonomous agents align actions, share information, and divide work to achieve individual or collective goals.

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The Energy Society: A Simulation Environment for Studying Agent Cooperation under Survival Pressure

Lucas Bergholdt Hansen, Federico Torrielli, Filippo Tonini, Lukas Galke Poech

University of Southern Denmark Odense, Denmark · University of Turin Torino, Italy

Social Dilemmas Multi-Agent Coordination Multi-Agent Collaboration Agent-Based Modeling Multi-Agent Systems Emergent Behavior in Multi-Agent Systems

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LLM-based agents are increasingly deployed in multi-agent environments whose incentives can shape their behavior. We introduce The Energy Society, a minimal survival economy for studying how competitive and cooperative incentives affect emergent behavior when inference cost is directly tied to survival: Agents spend energy based on model size when generating tokens, regain energy by completing jobs or receiving donations, and deactivate if their energy reaches zero. We compare competitive and cooperative objectives against a baseline setting and several control variants. Across experiments, larger models consistently consume the most energy and spend more energy than they gain, even in those settings where token cost is not size-dependent. Cooperative incentives substantially alter behavior: agents donate to reactivate others, sometimes at the cost of their own survival, and job allocation changes. Ablations reveal that allowing agents to recommend actions to each other supports coordination and ambitious job selection, while memory helps agents calibrate risk from past outcomes. Agents rarely choose direct sabotage, but show more subtle signs of self-serving behavior in the competitive setting. The Energy Society is a compact testbed for studying the interaction between token costs and group incentives under a survival pressure. Source code is available at https://github.com/LucasBergholdt/EnergySociety

Abstract

Multi-Agent Coordination

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