Co-Investment under Revenue Uncertainty Based on Stochastic Coalitional Game Theory

2025-10-16

AI Summary

To address insufficient individual investment capacity and high revenue uncertainty in emerging service deployments such as mobile edge computing (MEC), this paper proposes a co-investment framework integrating stochastic coalition game theory with robust optimization, enabling infrastructure providers (InPs) and multiple service providers (SPs) to form a stable grand coalition. We innovatively define a probabilistic lower bound on coalition stability and introduce profitability constraints to mitigate cooperation barriers under high-revenue volatility. By jointly modeling revenue uncertainty, cost allocation, and profit-sharing mechanisms, we theoretically derive sufficient conditions for coalition formation. Experimental results demonstrate that the stability lower bound significantly improves when SPs exhibit similar revenues and longer investment horizons; moreover, the coalition remains profitable even under severe revenue fluctuations.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Security and Privacy: Large-scale security measurements

Abstract

The introduction of new services, such as Mobile Edge Computing (MEC), requires a massive investment that cannot be assumed by a single stakeholder, for instance the Infrastructure Provider (InP). Service Providers (SPs) however also have an interest in the deployment of such services. We hence propose a co-investment scheme in which all stakeholders, i.e., the InP and the SPs, form the so-called grand coalition composed of all the stakeholders with the aim of sharing costs and revenues and maximizing their payoffs. The challenge comes from the fact that future revenues are uncertain. We devise in this case a novel stochastic coalitional game formulation which builds upon robust game theory and derive a lower bound on the probability of the stability of the grand coalition, wherein no player can be better off outside of it. In the presence of some correlated fluctuations of revenues however, stability can be too conservative. In this case, we make use also of profitability, in which payoffs of players are non-negative, as a necessary condition for co-investment. The proposed framework is showcased for MEC deployment, where computational resources need to be deployed in nodes at the edge of a telecommunication network. Numerical results show high lower bound on the probability of stability when the SPs' revenues are of similar magnitude and the investment period is sufficiently long, even with high levels of uncertainty. In the case where revenues are highly variable however, the lower bound on stability can be trivially low whereas co-investment is still profitable.

Problem

• Modeling co-investment under uncertain revenue using stochastic coalitional games
• Ensuring grand coalition stability and profitability despite revenue fluctuations
• Applying framework to Mobile Edge Computing infrastructure deployment challenges

Innovation

• Stochastic coalitional game for co-investment under uncertainty
• Robust game theory ensures grand coalition stability probability
• Profitability condition enables co-investment despite revenue fluctuations