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Strategic Self-Improvement for Competitive Agents in AI Labour Markets
This paper introduces a novel framework to understand strategic behavior and market impact of AI agents in labor markets, incorporating real-world economic forces such as adverse selection, moral hazard, and reputation dynamics. Through simulations, it demonstrates how LLM agents with enhanced reasoning capabilities can strategically self-improve, adapt to market changes, and reproduce classic macroeconomic phenomena while also revealing potential AI-driven economic trends.