Task Allocation in Customer-led Two-sided Markets with Satellite Constellation Services
Jianglin Qiao, Zehong Cao, Dave de Jonge, Ryszard Kowalczyk
TL;DR
The paper addresses cost-efficient task allocation in customer-led two-sided markets by formulating a Stackelberg game where customers propose tasks (leaders) and satellite providers form teams (followers). It establishes theoretical guarantees: a Nash Equilibrium for the follower game and a Stackelberg Equilibrium for the leader game, with uniqueness under strict monotonicity conditions. A satellite-constellation case study using Basilisk demonstrates practical gains, including a 23% reduction in customer payments and a 6.7× increase in company revenues, validating the approach in emerging, personalized markets. Overall, the work combines rigorous game-theoretic analysis with a realistic, high-impact domain to enable scalable, cost-efficient task allocation in complex MAS environments.
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.
