Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.
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Principal-agent problems with adverse selection: A stochastic target problem formulation
Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Meta-prompt optimization enables LLM agents to discover stable, generalizable tacit collusion strategies in market simulations that outperform hand-crafted prompt baselines.