An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

Economy supervision mode of electricity market and its incentive mechanism

The analysis of a supervision environment is the first step for a company to enter the new electricity market. Transmission and distribution assets are the main investment targets of a company. The overseas power market belongs to the regulated industry;whether it is a stock M&A project or a green land bidding project, the regulatory environment determines the assets. The level of return and investment risk that guides the operation strategy of existing overseas assets, has a significant impact on the investment and operations of international companies. A comprehensive and rapid assessment of the regulatory environment can help the project teams of international companies understand the macroenvironment of the target electricity market within a short period, quickly identify investment risks, qualitatively analyze the return level of the underlying assets, shorten the decision time, capture investment opportunities, and enhance the team. Efficiency and quality of work are factors of great importance.

An Incentive Mechanism for Mobile Crowd Sensing in Vehicular Ad Hoc Networks

In the mobile crowdsensing of vehicular ad hoc networks (VANETs), in order to improve the amount of data collection, an effective method to attract a large number of vehicles is needed. Therefore, the incentive mechanism plays a dominant role in the mobile crowdsensing of vehicular ad hoc networks. In addition, the behavior of providing malicious data by vehicles as data collectors will have a huge negative impact on the whole collection process. Therefore, participants need to be encouraged to provide data honestly to obtain more available data. In order to increase data collection and improve the availability of collected data, this paper proposes an incentive mechanism for mobile crowdsensing in vehicular ad hoc networks named V-IMCS. Specifically, the Stackelberg game model, Lloyd’s clustering algorithm and reputation management mechanism are used to balance the competitive relationship between participants and process the data according to the priority order, so as to improve the amount of data collection and encourage participants to honestly provide data to obtain more available data. In addition, the effectiveness of the proposed mechanism is verified by a series of simulations. The simulation results show that the amount of available data is significantly higher than the existing incentive mechanism while improving the amount of data collection.

Incentive Mechanism Design for Public Goods Provision:Price Cap Regulation and Optimal Regulation

This paper studies the mechanism design that induces firms to provide public goods under two regulatory means： price cap regulation and optimal regulation, respectively. We first outline two models of monopoly regulation with unobservable marginal costs and effort, which can be regard as an optimal problem with dual restrictions. By solving this problem, we get the two optimal regulatory mechanisms to induce the provision of public goods. Further, by comparative statics, the conclusion is drawn that the welfare loss as sociated with price cap regulation, with respective to optimal regulation, increases more with increase of the expense of public goods.

Optimization of Chinese power enterprises R&D staff incentive mechanism

With the deepening of electric power market reform in China,the monopoly edge of the state-owned electric power enterprises will lose.On the basis of the existing post performance salary mechanism,Chinese power enterprises need to optimize the incentive mechanism of R&D staff,to arouse the R&D staff's enthusiasm and creativity,to adapt to the new market competition and further improve market value.Whilst the incentive mechanism optimizing processing needs to consider not only the changing market environment but also the personal and working characteristics of R&D staff.This paper summarizes the characteristics of the current Chinese power enterprises' R&D staff:staff's theory quality is high,but insensitive to the market;they are confronted with heavy workload and diversified job choices;managers can observe their behavior choices or not;besides,the process of R&D is complex and the market reactions of R&D achievements are uncertain.Based on the premise of the above features,two incentive models are established in this paper from the point of view of enterprise managers.One is for the situation when staff's behavior choices can be observed;the other is for the situation when staff's behavior choices cannot be observed.Through solving the model,we analyze the optimization path of electric power enterprises R&D staff incentive mechanism under these conditions:(1) when staff's behavior choices can be observed,managers can pay more to the R&D staff who develop products with higher output value,in order to encourage them to work harder.(2) when staff's behavior choices cannot be observed,managers should take reasonable strategies according to the different situations:a.when R&D staff incentive totally depend on the market value of the R&D achievements,managers should allocate workload rationally according to their different technical levels;b.when the market reactions of R&D results become more precarious,managers need to reduce the incentive intensity which based on the market value and raise their fixed salary level;c.when R&D staff become more risk averse,managers should reduce the incentive intensity which based on the market value and raise their fixed salary level;on the contrary,managers should improve the incentive intensity and reduce the fixed salary level.

Analysis on the Practical Application of Multiple Incentive Mechanism in Educational Management

In the new era,China’s education industry was ushered in a high-quality development stage.On the one hand,it is necessary to set a goal of education management on the all-round development of students.On the other hand,we should innovate some practical application schemes of diversified incentive mechanism consistent with the needs of educational management.Taking this as the starting point,this paper summarizes the connotation of incentive mechanism and diversified incentive mechanism,and analyzes the practical problems of single incentive standard,issues on incentive timeliness,and reduced participation of incentive objects at the current stage.On this basis,this paper puts forward some corresponding measures from three angles:opening up thinking,updating in time and emphasizing the target.

Research on the Improvement of Incentive Mechanism for Pharmaceutical Sales Personnel - Taking Company A as an Example

Objective To analyze the improvement of the incentive mechanism of sales personnel in pharmaceutical company A,and to promote the smooth operation and further development of the company in a long term.Methods Compensation incentive,performance appraisal,welfare benefit,training incentive,promotion motivation and enterprise cultural inspiration were explored through questionnaires,telephone interviews and in-person interviews.Results and Conclusion This company’s incentive mechanism has problems in two aspects:Material incentives and spiritual incentives.As to the company’s characteristics and strategic development,the optimization countermeasures of incentive mechanism are proposed from the following three aspects:constructing a reasonable incentive system,establishing an efficient spiritual incentive mechanism,and implementing the dynamic incentive and differentiated incentive simultaneously.

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient manner.Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency.However,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning process.In this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning.The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning.Meanwhile,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and additivity.The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

An optimal coupling incentive mechanism concerning insider’s compliance behavior towards marine information security policy

It is widely agreed that the insider’s noncompliance to the marine information security policies has brought about a major security problem in the organizational context.Previous research has stressed the potential of remunerative control,i.e.,reward,to better understand this problem.Few studies have been devoted to the exploration of the coupling incentive mechanism of tangible and intangible rewards that would induce insider’s compliance behavior towards the marine information security policy.In the present study,we address this research gap by proposing a theoretical model that explains the optimal coupling incentive mechanism of these two different types of remunerative control.Our findings have delivered insightful implications for practice and research on how to improve the marine information security policy compliance in a more subtle way.

On Incentive and Coordination Mechanism of Service Outsourcing Based on Principal-Agent Theory and Blockchain Technology

To address the issue of information asymmetry between the two parties and moral hazard among service providers in the process of service outsourcing,this paper builds the Stackelberg game model based on the principal-agent framework,examines the dynamic game situation before the contract being signed,and develops four information models.The analysis reveals a Pareto improvement in the game’s Nash equilibrium when comparing the four models from the standpoint of the supply chain.In the complete information scenario,the service level of the service provider,the customer company’s incentive effectiveness,and the supply chain system’s ultimate profit are all maximized.Furthermore,a coordinating mechanism for disposable profit is built in this study.The paper then suggests a blockchain-based architecture for the service outsourcing process supervision and a distributed incentive mechanism under the coordination mechanism in response to the inadequacy of the principal-agent theory to address the information asymmetry problem and the moral hazard problem.The experiment’s end findings demonstrate that both parties can benefit from the coordination mechanism,and the application of blockchain technology can resolve these issues and effectively encourage service providers.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Impact of Crowdsourcee’s Vertical Fairness Concern on the Crowdsourcing Knowledge Sharing Behavior and Its Incentive Mechanism

This paper examines in detail the impact of the crowdsourcee’s vertical fairness concern on the knowledge sharing incentive mechanism in crowdsourcing communities.The conditions for the establishment of the incentive mechanism are analyzed and the impact of fairness concern sensitivity on expected economic revenues of both sides as well as the crowdsourcing project performance is studied by game theory and computer simulation.The results show that the knowledge sharing incentive mechanism can only be established if the ratio between the performance improvement rate and the private cost reduction rate caused by shared knowledge is within a certain range.The degree of the optimal linear incentives,the private solution efforts,and the improvement of knowledge sharing level are positively correlated with the sensitivity of vertical fairness concern.In the non-incentive mode,the ratio between the performance conversion rate of private solution effort and the performance conversion rate of knowledge sharing effort plays an important role in moderating a crowdsourcing project’s performance.The authors find that the number of participants is either conducive or nonconducive to the improvement of performance.The implementation of knowledge sharing incentive can achieve a win-win situation for both the crowdsourcer and the crowdsourcee.

FIMI： A Constant Frugal Incentive Mechanism for Time WindowCoverage in Mobile Crowdsensing

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. An incentive mechanism is important for a mobile crowdsensing system to stimulate participants and to achieve good service quality. In this paper, we explore truthful incentive mechanisms that focus on minimizing the total payment for a novel scenario, where the platform needs the complete sensing data in a requested time window (RTW). We model this scenario as a reverse auction and design FIMI, a constant frugal incentive mechanism for time window coverage. FIMI consists of two phases, the candidate selection phase and the winner selection phase. In the candidate selection phase, it selects two most competitive disjoint feasible user sets. Afterwards, in the winner selection phase, it finds all the interchangeable user sets through a graph-theoretic approach. For every pair of such user sets, FIMI chooses one of them by the weighted cost. Further, we extend FIMI to the scenario where the RTW needs to be covered more than once. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve the properties of RTW feasibility (or RTW multi-coverage), computation efficiency, individual rationality, truthfulness, and constant frugality.

Budget-Aware Dynamic Incentive Mechanism in SpatialCrowdsourcing

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA (Dynamic Fixed Budget Allocation) and DABA (Dynamic Adjusted Budget Allocation), which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.

Optimal incentive mechanism for dual referral based on the analytic hierarchy process

Objective:The optimal incentive mechanism for dual referral is discussed to provide a decision-making foundation for the implementation of a dual referral incentive mechanism.Methods:We established a multi-hierarchy evaluation model for a dual referral incentive mechanism by analyzing the main schemes and measures used to stimulate dual referral.The weight values of every scheme or measure were determined and consistency tests were carried out using expert judgement methods and an analytic hierarchy process.Results:The weight values of patients,community physicians,and hospital physicians as the incentive objects were 0.2583,0.1047,and 0.6370,respectively,and the weight values of medical insurance-based reward and punishment,community medical center construction,referral plat-form,and publicity and communication as incentive schemes were 0.4950,0.3103,0.1164,and 0.0783,respectively.Conclusion:The main incentive object in the implementation of a dual referral is the hospital physician,followed by the patient.The most workable incentive scheme is“medical insurance-based reward and punishment,”followed by“community medical center construction.”

A Study on Renewable Power Pricing Mechanism and Price Incentive Policies in China

The basic framework of price policies for promoting renewable power de- velopment in China is introduced. The background, concept and implementation of price policies, focused on wind power, biomass power and solar power, are summarized in the article. The experiences and lessons of implementation of these price policies are analyzed. It is concluded that reasonable price policy is quite effective for promoting re- newable power development. According to the requirement of China's renewable power development, the suggestions for improving renewable power pricing mechanism and price incentive policies are proposed.

RTRC:A Reputation-Based Incentive Game Model for Trustworthy Crowdsourcing Service

The ubiquity of mobile devices have promoted the prosperity of mobile crowd systems, which recruit crowds to contribute their resources for performing tasks. Yet, due to the various resource consumption, the crowds may be reluctant to join and contribute information. Thus, the low participation level of crowds will be a hurdle that prevents the adoption of crowdsourcing. A critical challenge for these systems is how to design a proper mechanism such that the crowds spontaneously act as suppliers to contribute accurate information. Most of existing mechanisms ignore either the honesty of crowds or requesters respectively. In this paper, considering the honesty of both, we propose a game-based incentive mechanism, namely RTRC, to stimulate the crowds to contribute accurate information and to motivate the requesters to return accurate feedbacks. In addition, an evolutionary game is designed to model the dynamic of user-strategy selection. Specially, the replicator dynamic is applied to model the adaptation of strategy interactions taking into account the dynamic nature in time dependence and we also derive the evolutionarily stable strategies(ESSs) for users. Finally, empirical results over the simulations show that all the requesters and suppliers will select honest strategy to maximize their profit.

A notary group-based cross-chain mechanism

As an emerging distributed technology, blockchain has begun to penetrate into many fields such as finance, healthcare, supply chain, intelligent transportation. However, the interoperability and value exchange between different independent blockchain systems is restricting the expansion of blockchain. In this paper, a notary group-based cross-chain interaction model is proposed to achieve the interoperability between different blockchains. Firstly, a notary election mechanism is proposed to choose one notary from the notary group to act as a bridge for cross-chain transactions. Secondly, a margin pool is introduced to limit the misconduct of the elected notary and ensure the value transfer between the involved blockchains. Moreover, a reputation based incentive mechanism is used to encourage members of the notary group to participate in cross-chain transactions. Ethereum-based experiments demonstrate that the proposed mechanism can provide an acceptable performance for cross-chain transactions and provide a higher security level than ordinary cross-chain mechanisms.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.