Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test
arXiv:2607.06001v1 Announce Type: new Abstract: We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.8), testing two quantitative predictions about coupled multi-agent systems: an information-theoretic capacity region for wealth growth under market coupling, and a mean-field residual-scaling law for population misalignment under incentive and control levers. All predictions, acceptance bands, and decision rules were frozen in a public git chain before any run; every reported number re-derives mechanically from cached model outputs;
The rapid advancement of frontier LLMs and multi-agent systems enables controlled experiments on emergent economic behaviors, providing early insights into their potential impact.
This research provides quantitative data on the emergent economic properties and control mechanisms of LLM agents, which is crucial for understanding and mitigating future risks and opportunities.
The ability to pre-register and mechanically re-derive results from LLM agent experiments improves the scientific rigor and reproducibility of research into AI agent economies.
- · AI model developers
- · Quantitative economists
- · AI safety researchers
- · Regulators developing AI policy
- · Traditional economic modeling
- · Unprepared labor markets
- · Companies slow to adopt agentic systems
Experimental validation of economic theories in synthetic multi-agent environments accelerates understanding of AI's systemic effects.
Insights from these experiments could inform strategies for designing or controlling large-scale AI agent economies in the real world.
The development of robust and stable AI agent ecosystems could profoundly reshape global economic structures and human-AI interaction.
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Read at arXiv cs.AI