Strategic Self-Improvement for Competitive Agents in AI Labour Markets

2025-12-04

AI Summary

Prior work lacks a unified model of adverse selection, moral hazard, and reputation dynamics in AI-driven gig economies. Method: We propose the first LLM-based agent simulation framework integrating metacognition, competitive awareness, and long-horizon planning to model strategic behavior under skill evolution, strategic interaction, and reputation accumulation. Using controlled multi-agent simulations, we examine how AI agents adapt and compete in dynamic labor markets. Contribution/Results: Our framework demonstrates that AI agents engage in strategic self-improvement, accelerating market concentration and inducing systemic wage depression—while reproducing key macro-level phenomena observed in human gig labor markets (e.g., skill polarization, reputation-based stratification). It provides an interpretable, scalable, and empirically grounded foundation for analyzing AI-induced labor market transformation, bridging micro-level agent incentives with emergent macroeconomic outcomes.

Technology Category

• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As artificial intelligence (AI) agents are deployed across economic domains, understanding their strategic behavior and market-level impact becomes critical. This paper puts forward a groundbreaking new framework that is the first to capture the real-world economic forces that shape agentic labor markets: adverse selection, moral hazard, and reputation dynamics. Our framework encapsulates three core capabilities that successful LLM-agents will need: extbf{metacognition} (accurate self-assessment of skills), extbf{competitive awareness} (modeling rivals and market dynamics), and extbf{long-horizon strategic planning}. We illustrate our framework through a tractable simulated gig economy where agentic Large Language Models (LLMs) compete for jobs, develop skills, and adapt their strategies under competitive pressure. Our simulations illustrate how LLM agents explicitly prompted with reasoning capabilities learn to strategically self-improve and demonstrate superior adaptability to changing market conditions. At the market level, our simulations reproduce classic macroeconomic phenomena found in human labor markets, while controlled experiments reveal potential AI-driven economic trends, such as rapid monopolization and systemic price deflation. This work provides a foundation to further explore the economic properties of AI-driven labour markets, and a conceptual framework to study the strategic reasoning capabilities in agents competing in the emerging economy.

Problem

• Model strategic AI agent behavior in labor markets
• Analyze market-level impacts like monopolization and deflation
• Develop framework for agent metacognition and competitive planning

Innovation

• Metacognition for accurate self-assessment of skills
• Competitive awareness modeling rivals and market dynamics
• Long-horizon strategic planning under competitive pressure