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Generative AI and Information Asymmetry: Impacts on Adverse Selection and Moral Hazard

Yukun Zhang, Tianyang Zhang

TL;DR

This paper addresses information asymmetry in markets by introducing Generative AI signals that produce high-precision information about agents' private type and effort. It develops a principal–agent framework where AI-derived signals inform payment rules, enabling contracts that mitigate adverse selection and moral hazard. Theoretical results show that decreasing signal noise, with $\sigma_\theta^2 \to 0$ and $\sigma_e^2 \to 0$, moves outcomes toward the first-best and reduces information rents, while multi-period and multi-agent extensions reveal reputational and externality effects. Experimental simulations across market structures demonstrate robust welfare gains, particularly under competition, and offer policy guidance on responsible AI deployment, data governance, and fairness. Overall, the work provides a rigorous, AI-augmented mechanism-design approach with practical implications for improving market efficiency and social welfare.

Abstract

Information asymmetry often leads to adverse selection and moral hazard in economic markets, causing inefficiencies and welfare losses. Traditional methods to address these issues, such as signaling and screening, are frequently insufficient. This research investigates how Generative Artificial Intelligence (AI) can create detailed informational signals that help principals better understand agents' types and monitor their actions. By incorporating these AI-generated signals into a principal-agent model, the study aims to reduce inefficiencies and improve contract designs. Through theoretical analysis and simulations, we demonstrate that Generative AI can effectively mitigate adverse selection and moral hazard, resulting in more efficient market outcomes and increased social welfare. Additionally, the findings offer practical insights for policymakers and industry stakeholders on the responsible implementation of Generative AI solutions to enhance market performance.

Generative AI and Information Asymmetry: Impacts on Adverse Selection and Moral Hazard

TL;DR

This paper addresses information asymmetry in markets by introducing Generative AI signals that produce high-precision information about agents' private type and effort. It develops a principal–agent framework where AI-derived signals inform payment rules, enabling contracts that mitigate adverse selection and moral hazard. Theoretical results show that decreasing signal noise, with and , moves outcomes toward the first-best and reduces information rents, while multi-period and multi-agent extensions reveal reputational and externality effects. Experimental simulations across market structures demonstrate robust welfare gains, particularly under competition, and offer policy guidance on responsible AI deployment, data governance, and fairness. Overall, the work provides a rigorous, AI-augmented mechanism-design approach with practical implications for improving market efficiency and social welfare.

Abstract

Information asymmetry often leads to adverse selection and moral hazard in economic markets, causing inefficiencies and welfare losses. Traditional methods to address these issues, such as signaling and screening, are frequently insufficient. This research investigates how Generative Artificial Intelligence (AI) can create detailed informational signals that help principals better understand agents' types and monitor their actions. By incorporating these AI-generated signals into a principal-agent model, the study aims to reduce inefficiencies and improve contract designs. Through theoretical analysis and simulations, we demonstrate that Generative AI can effectively mitigate adverse selection and moral hazard, resulting in more efficient market outcomes and increased social welfare. Additionally, the findings offer practical insights for policymakers and industry stakeholders on the responsible implementation of Generative AI solutions to enhance market performance.

Paper Structure

This paper contains 52 sections, 9 theorems, 83 equations, 4 figures, 5 tables.

Table of Contents

  1. Background and Motivation
  2. Contributions
  3. Organization
  4. Literature Review
  5. Frameworks Review for Information Asymmetry: Adverse Selection and Moral Hazard
  6. Emerging Applications of Generative AI in Economic Research
  7. Single-period Model Analysis
  8. Model Setup and Assumptions
  9. Theoretical Analysis
  10. Multi-period Analysis
  11. Multi-Period Model and Dynamic Contract Design
  12. Dynamic Contracts and Long-Term Incentives.
  13. Multi-Agent Environments and Information Externalities
  14. Signal Manipulation and Robustness Checks
  15. Summary
  16. ...and 37 more sections

Key Result

proposition 1

As $\sigma_\theta^2$ decreases, the principal can design contracts that fully separate agents by type. In the limit as $\sigma_\theta^2 \to 0$, pooling is eliminated and information rents for low-quality agents are minimized.

Figures (4)

  • Figure 1: The figure above summarizes the results of the Single-period, Single-agent Experiment.
  • Figure 2: Results for the perfectly competitive market: The figure above summarizes the results of the perfectly competitive market in the Multi-Period, Multi-Agent Experiment. The results show that in a perfectly competitive market, generative AI can not only improve immediate efficiency, but also continuously enhance social welfare through learning optimization. However, the inclusiveness of generative AI in complex markets still needs to be considered.
  • Figure 3: Results for the oligopoly market: The figure above summarizes the results of the oligopoly market in the Multi-Period, Multi-Agent Experiment. The results show that in the oligopoly market, the improvement of social welfare and other indicators by generative AI is positive but limited. This improvement is heterogeneous, and the stratification between different types of agents is more obvious. In addition, the inclusiveness and fairness of generative AI in the oligopoly market require more attention.
  • Figure 4: Results for the monopoly market: The figure above summarizes the results of the monopoly market in the Multi-Period, Multi-Agent Experiment. The results show that in the monopoly market, the improvement of various indicators such as social welfare by generative AI is limited, especially for low- and medium-skilled people. The stratification of this improvement between different types of agents is more blurred. In addition, generative AI suggests introducing a regulatory framework to balance efficiency and competition in the monopoly market.

Theorems & Definitions (9)

  • proposition 1: Reduction in Adverse Selection
  • proposition 2: Reduction in Moral Hazard
  • proposition 3: Rent Extraction and Welfare Gains
  • proposition 4: Long-Run Welfare Improvements
  • proposition 5: Efficiency Gains in Multi-Agent Settings
  • proposition 6: Robustness Against Manipulation
  • proposition 7: Monopoly Welfare Effects
  • proposition 8: Oligopoly Efficiency and Information Distribution
  • proposition 9: Perfect Competition and Innovation Incentives