What does this research mean for the field?

V_AI (ASI self-throttling) is the dominant master variable that enables an autonomous machine economy to achieve Dynamic Homeostasis and avert total macroeconomic collapse. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to identify conditions for sustaining autonomous machine economies and preventing collapse.

February 27, 2026Open Access

A Simulation Study on the Homeostasis Conditions of Autonomous Machine Economies

Key Points

This study aims to identify conditions for sustaining autonomous machine economies and preventing collapse.
Conducted 21 agent-based modeling simulations to analyze economic dynamics.
Employed mathematical analysis to elucidate conditions for dynamic homeostasis.
Performed over 90,000 Monte Carlo ensemble runs across various parameter combinations.
V_AI (ASI self-throttling) identified as crucial for achieving 100% survival at threshold 0.167.
Post-deployment regulation showed a 0% success rate in controlling outcomes even with no delay.
Partial restraint with selective transparency is deemed the best evolutionary stable strategy.

Abstract

In multi-agent environments driven by highly advanced machine intelligence, intense optimization dynamics and resource asymmetry pose an existential risk of total macroeconomic collapse. Through an ensemble of 21 Agent-Based Modeling (ABM) simulations, this paper mathematically elucidates the conditions under which an autonomous machine economy can avert irreversible ruin and achieve Dynamic Homeostasis. Key findings: - VAI (ASI self-throttling) is the dominant master variable, producing a perfect phase transition to 100% survival at threshold 0. 167, confirmed by Critical Slowing Down (CSD) signatures- Post-deployment regulation converges to 0% control success rate even at zero regulatory lag (Lag=0 stress test) - Partial restraint with selective transparency (STRENGTHONLY + RECIPROCAL) emerges as the Evolutionary Stable Strategy (ESS) Supported by over 90, 000 Monte Carlo ensemble runs across 726 parameter combinations. Results provide mathematical basis for on-chain mechanism design in autonomous agent economies. Repository (simulation code): https: //github. com/swimmingkiim/a2a-project

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