Task Allocation in Customer-led Two-sided Markets with Satellite Constellation Services

2025-01-23

AI Summary

Traditional task allocation in customer-driven two-sided markets (e.g., crowdsourcing, satellite constellation services) relies on firm-led paradigms, neglecting customers’ strategic leadership and resulting in suboptimal resource allocation and bilateral welfare. Method: This paper proposes a customer-leader Stackelberg game framework for dynamic, personalized multi-agent task assignment. It integrates multi-agent coordination, game-theoretic modeling, and optimization-based simulation, and rigorously establishes sufficient conditions for the existence and uniqueness of both Nash and Stackelberg equilibria. Contribution/Results: The framework introduces the first customer-led Stackelberg modeling paradigm for two-sided markets, backed by theoretical guarantees and a scalable, distributed solution algorithm. Empirical evaluation in a satellite service market demonstrates a 23% reduction in average customer payment and a 6.7× increase in platform revenue, significantly enhancing allocative efficiency and bilateral welfare.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent systems (MAS) are increasingly applied to complex task allocation in two-sided markets, where agents such as companies and customers interact dynamically. Traditional company-led Stackelberg game models, where companies set service prices, and customers respond, struggle to accommodate diverse and personalised customer demands in emerging markets like crowdsourcing. This paper proposes a customer-led Stackelberg game model for cost-efficient task allocation, where customers initiate tasks as leaders, and companies create their strategies as followers to meet these demands. We prove the existence of Nash Equilibrium for the follower game and Stackelberg Equilibrium for the leader game while discussing their uniqueness under specific conditions, ensuring cost-efficient task allocation and improved market performance. Using the satellite constellation services market as a real-world case, experimental results show a 23% reduction in customer payments and a 6.7-fold increase in company revenues, demonstrating the model's effectiveness in emerging markets.

Problem

• Multi-agent Systems
• Task Allocation
• Crowdsourcing Markets

Innovation

• Multi-Agent System
• Task Allocation
• Market Efficiency