Governing the Ungovernable: A Framework for Ethical AI Governance in Autonomous Synthetic Economies

Abstract

The deployment of autonomous artificial intelligence agents in economic contexts is accelerating at a pace that has decisively outrun the development of governance frameworks capable of managing their collective behaviour. Existing regulatory instruments — including the EU Artificial Intelligence Act and the National Institute of Standards and Technology (NIST) AI Risk Management Framework — address the alignment of individual AI systems but offer little guidance on governing entire populations of autonomous agents operating within self-governing synthetic economies. The literature on AI governance has been predominantly normative, articulating principles without providing empirically validated mechanisms that link specific governance designs to measurable socio-economic outcomes. This paper addresses that gap by proposing Aegora Isle — a purpose-built closed-loop synthetic economy — as a simulation-based experimental framework for studying three governance regimes: ungoverned, rule-based, and adaptive reinforcement-learning-driven. Drawing on convergent evidence from agent-based computational economics, multi-agent reinforcement learning research, and institutional economics, the paper develops a theoretically grounded set of design hypotheses and prediction of governance performance across key indicators including economic inequality (Gini coefficient), resource sustainability (Mean Time to System Collapse), and aggregate productivity. The paper argues that adaptive, incentive-aligned governance mechanisms are theoretically superior to static rule-based approaches, and that this superiority is derivable from first principles established in prior empirical literature. The Aegora Isle framework is presented as both a theoretical contribution and a reusable experimental platform for the emerging empirical science of AI governance in autonomous economic systems. By embedding governance endogenously within Governor agents whose incentives are explicitly aligned with collective welfare, the framework provides a concrete, testable response to contemporary questions of institutional agency and ethical delegation in agentic AI systems.