Incentive Mechanism Design Research Articles

Research on the green development path of prefabricated building industry based on intelligent technology

PurposeIn order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.Design/methodology/approachThis paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.FindingsAn assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.Research limitations/implicationsThis paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.Originality/valueDifferent from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

Game Analysis and Incentive Mechanism Design for Differentially Private Cross-Silo Federated Learning

Game Analysis and Incentive Mechanism Design for Differentially Private Cross-Silo Federated Learning

Game-Theoretic Design of Quality-Aware Incentive Mechanisms for Hierarchical Federated Learning

Game-Theoretic Design of Quality-Aware Incentive Mechanisms for Hierarchical Federated Learning

Cost-aware task offloading in vehicular edge computing: A Stackelberg game approach

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occurs when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the competitive and cooperation relations among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work.

Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design

Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design

Research on two‐level energy management based on tiered demand response and energy storage systems

AbstractIn response to the escalating demands of the electricity market for load dispatch optimization and the stable operation of power systems, the design of effective incentive mechanisms to guide user electricity consumption behaviour has become an urgent task. This study addresses the complexity of the power load dispatch system by analysing the characteristics and interrelations of large‐scale user load demand responses. A dual‐layer energy management model was constructed, and a demand response incentive mechanism was designed. Adaptive incentive strategies were formulated according to different electric power user demand response scenarios. Furthermore, an optimal incentive decision‐making technology oriented towards user comfort was proposed, achieving an integrated function of strategy formulation, implementation, analysis, and optimization for power demand response. Through typical applications in core business scenarios such as elasticity of power user demand response, tiered incentive mechanisms, and comprehensive user utility, the model and strategies have been confirmed to optimize the economic benefits of virtual power plants and demand‐side electricity users under the premise of ensuring user comfort. This provides a novel solution for the efficient operation of the power market.

AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation

Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimation by enabling distributedly owned data to be leveraged, while ensuring privacy and confidentiality. However, to effectively motivate stakeholders to contribute their high-quality local data and computational resources for this purpose, incentive mechanism design is key. In this paper, we report our experience designing and deploying the Hierarchical FL Incentive mechanism for Gas usage estimation (HiFi-Gas) system. It is designed to cater to the unique structure of gas companies and their affiliated heating stations. HiFi-Gas provides effective incentivization in a hierarchical federated learning framework that consists of a horizontal federated learning (HFL) component for effective collaboration among gas companies and multiple vertical federated learning (VFL) components for the gas company and its affiliated heating stations. To motivate active participation and ensure fairness among gas companies and heating stations, we incorporate a multi-dimensional contribution-aware reward distribution function that considers both data quality and model contributions. Since its deployment in the ENN Group in December 2022, HiFi-Gas has successfully provided incentives for gas companies and heating stations to actively participate in FL training, resulting in more than 12% higher average gas usage estimation accuracy and substantial gas procurement cost savings. This implementation marks the first successful deployment of a hierarchical FL incentive approach in the energy industry.

Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles

With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.

An incentive mechanism design for federated learning with multiple task publishers by contract theory approach

In the process of model training of the federated learning system, how to design an incentive mechanism to attract more high-quality worker nodes to join is a key issue. The existing researches on federated learning incentive mechanism only consider the scenario of single task publisher with multiple worker nodes. In the scenario of multi task publishers and multiple worker nodes, the competition between different task publishers makes the entire research process more complicated, and the contract design in the single task publisher scenario cannot be directly applied. To solve this problem, this paper proposes an incentive mechanism based on contract theory in the multi task publisher scenario and studies its application in federated learning. Simulation experiments show that this mechanism is effective for federated learning and can achieve the purpose of encouraging worker nodes to join and improve the efficiency of federated learning.

Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC

Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC

Strategic Analysis of the Parameter Servers and Participants in Federated Learning: An Evolutionary Game Perspective

Federated learning (FL) is a new decentralized deep learning paradigm developed for collaborative model training and solving the problem of data privacy and has received extensive attention from both the academic and business worlds. However, FL still faces challenges in encouraging participants to contribute private data and computational resources. Although many studies have applied game theory models to improve the incentive mechanism design of FL, they assume that the players are absolutely rational and that the game models are static. In this study, a mathematical model based on evolutionary game theory (EGT) is established to analyze the interaction between parameter servers and participants, considering that the participants are not completely rational in the long-term dynamic decision-making process. The evolutionarily stable status of the FL system and the strategies of the parameter servers and participants were analyzed under eight different scenarios. Based on the model analysis and results of the numerical experiments, managerial insights for maintaining a sustainable FL system are summarized.

Exploring pollution control in transboundary watersheds under the perspective of ecological compensation based on aggregate game.

Pollution control in inter-provincial river basins involves many complex subjects, so it is difficult to effectively implement ecological compensation policies. To clarify the interest relationship among pollution control subjects and stimulate their willingness to cooperate in collaborative governance, this paper builds a multi-agent coordinated pollution control model based on the theory of aggregate game; explores the change of equilibrium action of a single pollution control agent and multi-actors, symmetric, and asymmetric situations under the influence of synergistic benefits; and studies the incentive mechanism design to maintain the cooperation of various agents. The research results show that an increase in the number of upstream firms would lead to a reduction in the incentive effects of downstream government compensation and an increase in the likelihood of "free-riding" and "coordination failure." Synergy benefits vary positively with the degree of cooperation between government and enterprises, and higher synergy benefits can effectively compensate for the high transaction costs caused by multiple entities, alleviate the financial pressure on downstream governments, and increase the willingness of upstream and downstream entities to cooperate. In addition, focusing on wastewater reduction from core enterprises, such as heavy polluters can help improve the efficiency of regional emissions reduction, while having a catalytic effect on small enterprises.

QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning

In addition to federated optimization, more current studies focus on incentive mechanism design problems for federated learning (FL), stimulating data owners to share their resources securely. Most existing works only considered data quantity but neglected other key factors like data quality and training time prediction. In combination with all the above factors, we proposed an online quality-aware incentive mechanism based on multi-dimensional reverse auction, QuoTa, for achieving fast FL. In particular, it first designs model quality detection to eliminate some malicious or dispensable devices based on their historical behaviors and marginal contributions. Due to the possible fluctuations of CPU frequency in realistic model training, it next predicts model training time based on the upper confidence bound algorithm. By combining the two modules, QuoTa incentivizes data owners with high data quality, high computing capability, and low cost to participate in the FL process. By rigorous theoretical proof and extensive experiments, we prove that QuoTa satisfies all desired economic properties and achieves higher model accuracy and less convergence time than the state-of-the-art work.

Talent Management and Organizational Change in Corporate Mergers and Acquisitions

This paper thoroughly explores the pivotal issues of talent management and organizational change in corporate mergers and acquisitions, along with corresponding solutions. In the realm of talent management, our focus encompasses the evaluation and selection of talents in target companies, cultural assimilation and leadership adjustment, as well as the design of performance management and incentive mechanisms. Regarding organizational change, we analyze change management strategies, organizational structure adjustments, communication and employee engagement, and emphasize the significance of continuous improvement and learning. Through an in-depth examination of relevant literature and real-world cases, we present a series of practical solutions to address challenges that may arise during the merger and acquisition process, such as cultural differences, leadership adjustments, employee uncertainties, and business process integration. Finally, we provide recommendations for future research, including a deeper exploration of cultural differences in cross-border mergers and acquisitions, the impact of the digital era on corporate mergers and acquisitions, and the best practices in different industries and company scales.

Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists

Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists

A UAV-Assisted Truth Discovery Approach With Incentive Mechanism Design in Mobile Crowd Sensing

A UAV-Assisted Truth Discovery Approach With Incentive Mechanism Design in Mobile Crowd Sensing

RATE: Game-Theoretic Design of Sustainable Incentive Mechanism for Federated Learning

RATE: Game-Theoretic Design of Sustainable Incentive Mechanism for Federated Learning

Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency security. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due to the costs and losses. In this paper, to incentivize the LCC-HVDC systems and their connected adjacent AC systems to participate in the droop-based EFC, a novel control-parameter-based incentive mechanism is proposed, which can flexibly deal with various possible power imbalances. Then, a non-cooperative-based incentive game model is formulated to quantitatively analyze the incentive mechanism in the MIDC system. An algorithm for seeking the Nash equilibrium is designed, and the uniqueness of Nash equilibrium is proven. Moreover, the individual rationality, incentive compatibility and social optimality of the proposed mechanism are analyzed and proven. The effectiveness of the proposed incentive mechanism is verified through a case study.

Bi-Objective Incentive Mechanism for Mobile Crowdsensing With Budget/Cost Constraint

In recent years, mobile crowdsensing (MCS) has been widely adopted as an efficient method for large-scale data collection. In MCS systems, insufficient participation and unstable data quality have become two crucial issues that prevent crowdsensing from further development. Thus designing a valid incentive mechanism is essentially significant. Most of the existing works on incentive mechanism design focus on single-objective optimization with various constraints. However, in the real-world crowdsensing, it is common that several objectives to be optimized exist. Furthermore, constraints on budget or cost are often seen in MCS systems as the feasibility of implementing incentive mechanism is indispensable. This paper studies a bi-objective optimization scenario of MCS to simultaneously optimize total value function and coverage function with budget/cost constraint through a set of problem transformations. Then a budget- or cost-feasible bi-objective incentive mechanism is further proposed to solve the aforementioned bi-objective optimization problem through the combination of binary search and greedy heuristic solution under budget or cost constraint, respectively. Through both rigorous theoretical analysis and extensive simulations, the obtained results demonstrate that the mechanisms achieve computation efficiency, individual rationality, truthfulness, and budget or cost feasibility, while one mechanism obtains an approximation.