Stackelberg competition

About: Stackelberg competition is a research topic. Over the lifetime, 6611 publications have been published within this topic receiving 109213 citations.

Incentive Mechanisms for Crowdblocking Rumors in Mobile Social Networks

Abstract: Mobile social networks (MSNs) have become an indispensable way for people to access information, express emotions, and communicate with each other. However, the advent and extensive use of MSNs has also created fertile soil for the breeding and rapid spread of rumors. Therefore, blocking the spread of rumors in MSNs has always been a hot topic in this field. With the idea of crowdsourcing, we propose a novel rumor control framework, called Crowdblocking , in which users can implement control schemes in a collaborative and distributed way, so that the rumors can be controlled more effectively. With the proposed framework, the main problem that arises is how to motivate more users to actively participate in rumor blocking activities. To this end, we design two effective incentive mechanisms in this paper. First, we propose an incentive mechanism based on the Stackelberg game for homogeneous control tasks. We theoretically analyze the Stackelberg equilibrium to maximize the utility of the network manager and users involved in blocking rumor tasks, and ensure that no user can improve its own utility by unilaterally changing the current strategy. Second, for heterogeneous control tasks, we design a real-time reverse auction incentive mechanism, which allows users to have more autonomy and freely customize their own plans to participate in control tasks. Also, we prove that the mechanism possesses the desired properties of task timeliness, computational efficiency, user rationality, manager profitability, and price truthfulness. Finally, we validate the efficiency of the proposed mechanisms through extensive simulation experiments on the real datasets.

Economic and Environmental Life Cycle Optimization of Noncooperative Supply Chains and Product Systems: Modeling Framework, Mixed-Integer Bilevel Fractional Programming Algorithm, and Shale Gas Application

Abstract: In this work, we propose a general modeling framework for the economic and environmental life cycle optimization of supply chains and product systems with noncooperative stakeholders. This framework is based on the functional-unit-based life cycle optimization approach and the leader–follower Stackelberg game structure to capture the decentralized feature and noncooperative relationships between multiple stakeholders across the product life cycle. The leader enjoys the priority of decision-making to optimize both its own economic performance and the life cycle environmental performance of the supply chain or product system. After the observation of leader’s decisions, the follower takes actions correspondingly to optimize its own economic performance. The resulting problem is formulated as a mixed-integer bilevel fractional program to account for conflicting objectives and interactions among different stakeholders. Design and operational decisions for both leader and follower are taken into consideration,...

An Optimal Pricing Scheme for the Energy-Efficient Mobile Edge Computation Offloading With OFDMA

Abstract: The mobile edge computation offloading (MECO) system has emerged as a promising technology to address several problematic issues in cloud computing such as data throughput, capacity, and security. In this letter, we consider a single-cloud-multi-user MECO system with orthogonal frequency-division multiple access and propose an optimal pricing scheme to consider each mobile user’s (MU) need for resources. The proposed model was formulated as a single-leader-multi-user Stackelberg game model, and the Stackelberg equilibrium was provided to optimize the utility of the MUs and the edge cloud. Here, the utility of the MU implies the energy efficiency and payment for using the computation capacity of the edge cloud, and the utility of the edge cloud implies the revenue for serving computation offloading. The optimal strategies of the MUs are uniquely given by closed-form expressions, and that of the edge cloud is given by gradient descent methods. Some numerical examples are provided to verify our approach.