A Simulation Study on the Homeostasis Conditions of Autonomous Machine Economies

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:- V_AI (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 (STRENGTH_ONLY + 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