Scholarlink

<p>Asymmetric information is considered a central challenge that significantly affects market efficiency. Its negative impacts, particularly in principal–agent relationships, result in adverse selection and moral hazard. Adverse selection occurs before contracting and is the result of hidden information. On the other hand, moral hazard arises afterward due to the agent’s hidden actions (behavior change of the agent) that shift the risk to the principal. In this paper, we focus on mitigating moral hazard by reducing information asymmetry through utilizing the recent advances of artificial intelligence (AI) methods. Specifically, we introduce an AI-assisted contract-theoretic framework, referred to as LLM-MH, in which the principal receives multiple noisy, unverifiable signals and employs a large language model (LLM) to infer the credibility of the agent’s narratives. We formulate the multi-period moral hazard problem, derive optimal contracts under different informational environments, and incorporate prospect theory to capture bounded rationality in effort choice. The proposed LLM-MH framework, as a soft verification method, enables the principal to use an LLM to evaluate the agent’s multi-signal narratives (such as reports or logs) to construct credibility ratings. By jointly considering the outcomes and the LLM-generated credibility ratings, the proposed framework allows the principal to design incentive-compatible contracts that can substantially mitigate the impact of moral hazard on the principal’s utility. We conduct a set of simulation experiments to investigate the effectiveness of the proposed LLM-based framework. Compared to reference models, the results reveal that the proposed LLM-MH framework effectively manages moral hazard by reducing information asymmetry, learning optimal agent effort, enhancing principal-agent utilities, and preventing strategic manipulation. The proposed LLM-MH framework provides one of the first practical and formal foundations for integrating LLM into contract theory, representing modern organizational environments characterized by complex textual communication.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mitigating information asymmetry: a large language model framework for managing multi-signal moral hazard

  • 期刊论文

  • 发表日期：

    2026年6月15日

查看全文
  • Rami Mohawesh,
  • Haythem Bany Salameh,
  • Ghaleb A. Elrefae

摘要

Asymmetric information is considered a central challenge that significantly affects market efficiency. Its negative impacts, particularly in principal–agent relationships, result in adverse selection and moral hazard. Adverse selection occurs before contracting and is the result of hidden information. On the other hand, moral hazard arises afterward due to the agent’s hidden actions (behavior change of the agent) that shift the risk to the principal. In this paper, we focus on mitigating moral hazard by reducing information asymmetry through utilizing the recent advances of artificial intelligence (AI) methods. Specifically, we introduce an AI-assisted contract-theoretic framework, referred to as LLM-MH, in which the principal receives multiple noisy, unverifiable signals and employs a large language model (LLM) to infer the credibility of the agent’s narratives. We formulate the multi-period moral hazard problem, derive optimal contracts under different informational environments, and incorporate prospect theory to capture bounded rationality in effort choice. The proposed LLM-MH framework, as a soft verification method, enables the principal to use an LLM to evaluate the agent’s multi-signal narratives (such as reports or logs) to construct credibility ratings. By jointly considering the outcomes and the LLM-generated credibility ratings, the proposed framework allows the principal to design incentive-compatible contracts that can substantially mitigate the impact of moral hazard on the principal’s utility. We conduct a set of simulation experiments to investigate the effectiveness of the proposed LLM-based framework. Compared to reference models, the results reveal that the proposed LLM-MH framework effectively manages moral hazard by reducing information asymmetry, learning optimal agent effort, enhancing principal-agent utilities, and preventing strategic manipulation. The proposed LLM-MH framework provides one of the first practical and formal foundations for integrating LLM into contract theory, representing modern organizational environments characterized by complex textual communication.