AI & GAME THEORY
Generative AI as Digital Representatives in Collective Decision-Making: A Game-Theoretical Approach
This paper introduces a novel game-theoretical framework to analyze the strategic information revelation of human members to Generative AI (GenAI) agents acting as digital representatives in collective decision-making. It addresses the critical challenge of accurately representing individual preferences while accounting for communication costs and strategic interactions.
Authors: Kexin Chen, Jianwei Huang, and Yuan Luo from The Chinese University of Hong Kong, Shenzhen and affiliated research centers.
Executive Impact: Key Findings at a Glance
This research uncovers critical dynamics for deploying GenAI as digital representatives, revealing both challenges and opportunities for enterprise decision-making.
Yes
Paradoxical Individual Alignment with Digital Reps
More Info
Conflicting Preferences Lead to Strategic Information Revelation
Possible
Overall Loss Reduction When Manual Participation Costs Are High
Critical
GenAI System Capability For Maximizing Benefits
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Game-Theoretical Model
Performance Comparison
Practical Implications
Enterprise Process Flow: Strategic Information Revelation
Member Preferences
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Strategic Revelation (am)
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GenAI Learns (θ'm)
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Team Decision (θT)
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Member Loss Calculation (Lm)
Strategic Balance
Between Preference Alignment & Communication Cost Drives Revelation
| Characteristic | Conflicting Preferences (κA,B < 0) | Aligned Preferences (κA,B > 0) |
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| Revelation Behavior |
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| Equilibrium Outcome |
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| Team Decision Gap |
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≥ Baseline
Equilibrium Preference Loss is Generally Higher or Equal to Direct Participation
| Metric | Digital Representatives (GenAI) | Direct Participation (Baseline) |
|---|---|---|
| Team Decision Alignment |
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| Preference Loss |
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| Total Loss (Incl. Communication Costs) |
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Possible
Individual Members Can Achieve Lower Preference Losses with GenAI
Optimizing GenAI Deployment in Enterprise Decision-Making: Key Recommendations
For high-cost, complex decision-making tasks (e.g., strategic planning, negotiations), GenAI digital representatives can significantly reduce overall team loss by automating information aggregation and communication. In scenarios with conflicting team member preferences, GenAI agents foster a competitive revelation dynamic that leads to outcomes closely approximating direct participation. Invest in advanced GenAI systems (lower trigger price 'p') to expand benefits to a wider range of preferences and reduce total team loss. Be aware that while aggregate preference loss may be higher, individual members might experience paradoxical gains in preference alignment, especially when manual participation costs are prohibitive or GenAI systems are sufficiently advanced.
Crucial
Advanced GenAI Systems (Lower Trigger Price 'p') Reduce Total Loss and Expand Benefits
Advanced ROI Calculator
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Your AI Implementation Roadmap
A structured approach to integrating Generative AI as digital representatives in your organization.
Phase 1: Discovery & Strategy
Assess current decision-making workflows, identify key stakeholders, define measurable objectives for GenAI integration, and conduct a detailed preference mapping exercise for critical roles.
Phase 2: Pilot & Customization
Develop a pilot program with a small team. Customize GenAI models to accurately reflect identified preferences and strategic objectives. Establish clear communication protocols between human members and their digital representatives.
Phase 3: Integration & Training
Roll out GenAI representatives to a broader audience. Provide comprehensive training for team members on strategic information revelation and interpreting GenAI outputs. Integrate GenAI with existing enterprise collaboration tools.
Phase 4: Optimization & Scaling
Continuously monitor GenAI performance, preference alignment, and team decision outcomes. Iterate on model training and information revelation strategies based on feedback. Scale successful implementations across the organization.
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