Generative AI as Digital Representatives in Collective Decision-Making: A Game-Theoretical Approach

2025-12-14

AI Summary

When generative AI (GenAI) agents participate in group decision-making as digital representatives, accurately modeling individual preferences remains challenging due to privacy constraints and practical limitations on acquiring complete personal information. Method: We develop a game-theoretic model that formally captures strategic interactions among agents using GenAI proxies. We identify and analyze a novel phenomenon—competitive disclosure of preferences—driven by preference conflicts, and derive a closed-form Nash equilibrium via equilibrium analysis and preference learning theory. Contribution/Results: Contrary to the intuition that “more information is always better,” we prove that strategic use of GenAI agents can *improve* preference alignment. Our framework quantifies the trade-off between information acquisition cost and alignment quality, and establishes precise boundary conditions under which individual utility increases—specifically, when human participation costs are high or GenAI capability is strong. This work presents the first theoretical framework unifying strategic information disclosure with AI-mediated collective decision-making, and identifies the critical conditions for utility enhancement.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Generative Artificial Intelligence (GenAI) enables digital representatives to make decisions on behalf of team members in collaborative tasks, but faces challenges in accurately representing preferences. While supplying GenAI with detailed personal information improves representation fidelity, feasibility constraints make complete information access impractical. We bridge this gap by developing a game-theoretic framework that models strategic information revelation to GenAI in collective decision-making. The technical challenges lie in characterizing members' equilibrium behaviors under interdependent strategies and quantifying the imperfect preference learning outcomes by digital representatives. Our contribution includes closed-form equilibrium characterizations that reveal how members strategically balance team decision preference against communication costs. Our analysis yields an interesting finding: Conflicting preferences between team members drive competitive information revelation, with members revealing more information than those with aligned preferences. While digital representatives produce aggregate preference losses no smaller than direct participation, individual members may paradoxically achieve decisions more closely aligned with their preferences when using digital representatives, particularly when manual participation costs are high or when GenAI systems are sufficiently advanced.

Problem

• Models strategic information revelation to GenAI in collective decision-making
• Characterizes equilibrium behaviors under interdependent strategies and imperfect learning
• Analyzes how conflicting preferences drive competitive information revelation

Innovation

• Game-theoretic framework for strategic information revelation
• Closed-form equilibrium characterizations balancing preferences and costs
• Digital representatives improving individual alignment despite aggregate losses