Stackelberg Game-Based Optimal Dispatch for PEDF Park and Power Grid Interaction under Multiple Incentive Mechanisms

The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.

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.

基于Stackelberg Game诱导策略的网络调度算法

首先建立了网络Stackelberg Game模型,分析了该模型下Nash均衡的存在性,给出了网络的队最优解。在网络资源管理中,引入基于Stackelberg Game的网络诱导策略,利用动态博弈和多次逐步诱导的方法,提出了一种网络由一般状态到最优运行状态的动态调度算法。队最优解保证了网络在最优运行状态下的稳定性。数值仿真验证了该算法的有效性。

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.

Incentive Feedback Stackelberg Strategy in Mean-Field Type Stochastic Difference Games

This paper designs an incentive Stackelberg strategy for the discrete-time stochastic systems with mean-field terms.Sufficient conditions for the existence of such a design are suggested.Moreover,the incentive strategy is obtained as a feedback form including the deviation of the state and its mathematical expectation.Also,the stability analysis is involved.It is found that the stability can be guaranteed by the follower.In addition,the specific algorithm is proposed and its effectiveness is checked by two examples.

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.

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.

Enhancing customers prevention efforts:An incentive feedback mechanism design

To improve the inefficient prevention caused by customers unwillingness to adopt prevention strategies in health management,an incentive feedback mechanism that is based on game theory and contract design theory is introduced.The conditions for making customers and health maintenance organizations(HMOs)willing to participate in the proposed mechanism are given.A dual nonlinear programming model is used to identify the optimal prevention effort of customers and the pricing strategy of HMOs.Results show that to generate increased benefits,HMOs need to consider cost sharing when customers are not familiar with the proposed health services.When health services are gradually accepted,the cost sharing factor can be gradually reduced.Simulation shows that under random circumstances in which the market reaches a certain size,the proposed method exhibits a positive network externality.Motivated by network externality,HMOs only need to make their customers understand that the larger the number of participants,the greater the utility of each person.Such customers may then spontaneously invite others to purchase insurance.

Improvement of Incentive Mechanism on BitTorrent-like Peer-to-Peer File Sharing Systems

BitTorrent is a very popular Peer-to-Peer file sharing system, which adopts a set of incentive mechanisms to encourage contribution and prevent free-riding. However, we find that BitTorrent’s incentive mechanism can prevent free-riding effectively in a system with a relatively low number of seeds, but may fail in producing a disincentive for free-riding in a system with a high number of seeds. The reason is that BitTorrent does not provide effective mechanisms for seeds to guard against free-riding. Therefore, we propose a seed bandwidth allocation strategy for the BitTorrent system to reduce the effect of seeds on free-riding. Our target is that a downloader which provides more service to the system will be granted a higher benefit than downloaders which provide lower service when some downloaders ask for downloading file from a seed. Finally, simulation results are given, which validate the effectiveness of the proposed strategy.

On Incentive Strategy with a kind of Incomplete Information

The designing of incentive strategy when the follower’s objective functions have Parameters unknown to the leader is investigated in this paper. A desinging approach named IncentiveStrategy with Unknown but Bounded error (ISUBB) is proposed. A simple example is given to explain the use of ISUBS.

基于多领导者Stackelberg博弈的分层联邦学习激励机制设计

分层联邦学习中隐私安全与资源消耗等问题的存在降低了参与者的积极性。为鼓励足够多的参与者积极参与学习任务,并针对多移动设备与多边缘服务器之间的决策问题,提出基于多领导者Stackelberg博弈的激励机制。首先,通过量化移动设备的成本效用与边缘服务器的支付报酬,构建效用函数并定义最优化问题;其次,将移动设备之间的交互建模为演化博弈,将边缘服务器之间的交互建模为非合作博弈。为求解最优边缘服务器选择和定价策略,提出多轮迭代边缘服务器选择算法(MIES)和梯度迭代定价算法(GIPA),前者用于求解移动设备之间的演化博弈均衡解,后者用于求解边缘服务器之间的定价竞争问题。实验结果表明,所提算法GIPA与最优定价预测策略(OPPS)、历史最优定价策略(HOPS)和随机定价策略(RPS)相比,可使边缘服务器的平均效用分别提高4.06%、10.08%和31.39%。

多市场接触、相互克制战略与企业绩效——基于企业内部机制视角的研究

以往文献对于企业多市场接触进行了深入而广泛的研究,大多数局限于企业外部视角,很少关注企业内部实施机制对其多市场战略的影响。本文主要考察企业内多市场协同与激励机制对企业相互克制战略实施的影响。我们构建了关于多市场接触、相互克制战略、内部协同机制、多市场激励机制等构念的量表,进行了结构方程分析。结果表明:企业间多市场接触促进了相互克制战略的实施,进而会对企业绩效产生积极影响;企业的内部多市场激励机制对多市场战略与企业绩效间关系具有显著的正向调节作用,但是多市场协同机制的调节效果并不显著。本文认为,合理地安排企业内部协同与激励机制,将对企业实施有效的相互克制战略提供重要支持,进而会对企业绩效有较大的提升作用。

建筑市场绿色采购激励策略演化博弈研究

为实现我国“双碳”目标和人与自然和谐共生的现代化,绿色发展成为我国当前必然趋势,绿色采购是实现绿色发展的关键环节。为分析建筑市场绿色采购激励策略,基于演化博弈理论构建建筑项目业主、建筑企业和政府的三方演化博弈模型并分析演化稳定策略,结合Matlab数值仿真探究不同补贴和处罚参数对演化稳定策略的影响,揭示政府激励策略的作用机制。研究表明:政府应早期重补贴后期重处罚;政府对建筑企业补贴的效果优于对项目业主补贴的效果;政府对项目业主处罚的效果优于对建筑企业处罚的效果。并为促进建筑业绿色发展对政府建筑市场绿色采购激励策略提出建议。

基于区块链的知识密集型服务业数据共享动态激励机制的构建

过高的初始投资和维护成本以及知识密集型服务组织间天然竞争关系限制区块链广泛应用及数据共享平台发展。通过对知识密集型服务业数据共享策略进行演化博弈分析,构建一种基于区块链智能合约的知识密集型服务组织的数据共享动态激励机制。针对产业数据共享现状,于博弈模型中引入区块链投资维护成本、数据竞争成本及激励成本/收益3个影响因素,通过复制动态方程分析组织间数据共享演化稳定策略。设计数据共享激励规则,并利用智能合约技术实现动态调整激励成本/收益,鼓励服务组织积极参与数据共享。结果表明:当服务组织接受符合数据共享策略条件的激励收益后,数据共享参与者比例最终达到饱和,且激励收益越大,趋近饱和状态的速度越快;针对不同数据共享策略的激励成本不仅有效增加知识密集型服务组织参与数据共享的意愿,且为知识密集型服务业数据共享平台带来额外收入。

双向机制下企业自愿碳减排的三方博弈模拟

工业企业碳减排对平衡环境保护和经济发展尤为重要。基于政府和消费者双向机制探究企业自愿碳减排的影响因素,根据政府正向激励机制和消费者反向倒逼机制构建政府、企业和消费者三方演化博弈模型,分析了各参与方策略选择的演化稳定性,从系统动力学角度探讨三方主体策略选择不同时各个参数对企业自愿碳减排的影响机制,进一步分析了三方博弈系统中均衡点的稳定性。结果表明:企业选择自愿减排策略的概率与消费者低碳消费概率和政府非物质激励带给企业的收益额、减排成本补贴率、政府奖金、企业碳披露损失额等参数呈正相关;与企业的低碳营销费用、绿色积分价值、消费污染等参数呈负相关。最后,利用MATLAB 2016a进行数值仿真,验证了以上结论,并结合实际提出相关建议。

政府引导下古迹类文化传播的激励机制研究

为有效提升我国文物古迹类文化产业的文化影响力,促进文化产业弘扬自身的历史文化,提升全民文化素养。基于有限理性和利益最大化原则,依据政府、文化产业机构和消费者之间的文化传播交互行为,提出了相关的假设,构建了演化博弈模型,分析了政府、古迹类文化产业机构和消费者三类主体在文化传播过程中的策略选择;借助Matlab仿真模拟了策略演化,并进行了敏感性分析。研究结果表明:政府可以通过对经营优秀的文化产业机构实行免税政策,引导文化产业机构文化传播,实现三方帕累托最优策略均衡;政府和文化产业机构文化传播的素材量、消费者受文化传播影响花费的费用,以及消费者吸收的文化知识量对文化传播具有促进作用;消费者实地参观收益系数对三方博弈演变结果的影响作用最大,体现了消费者实地参观后总体评价的重要性。

基于区块链的负荷聚合商及居民用户多方共治交易模式

居民用户以负荷聚合商为中介参与需求响应,在交易中往往不具有话语权和选择权,由此产生信息不透明、第三方权力过大等信任问题;同时,居民用户因信息获取能力差以及负荷实时控制能力弱,难以在聚合响应中享受平等决策权、知情权和话语权。因此,基于区块链技术设计负荷聚合商及居民用户多方共治交易模式,针对包含3类柔性负荷的居民用户及聚合商的区块链节点,以自治共享为基本原则建立区块链节点模型和配置方法;基于负荷聚合商及居民用户节点间的聚合响应新模式,构建了基于聚合商非合作博弈、居民用户间演化博弈及聚合商与居民用户间主从博弈的激励机制,设计了激励相容的智能合约算法和区块数据结构,并提出响应标识因数和激励理性系数来评估所提交易模式的有效性;最后,经算例仿真验证表明,所提交易模式在保证各交易节点效用的基础上,可确保聚合服务高效可信,有利于唤醒海量需求侧沉睡资源。

基于动态博弈的科研诚信激励机制分析

为进一步激励和促进科研人员保持学术初心,减少各类学术不端现象的发生,通过建立科研活动主体、评议主体及科研诚信监督主体的动态博弈模型,从学术共同体参与科研诚信监督体系及对科研主体是否进行诚信激励两个维度出发,以最大化诚信率、最小化学术不端、最大化学术不端惩罚等3个目标为导向,系统性分析当前科研诚信体系激励机制,由此提出进一步完善相关奖惩机制设计以及推动科研诚信立法等对策建议。

基于动态诚信因子的主从博弈抑制微电网虚假信息交易研究

为了解决微电网群内交互信息中存在的虚假信息问题,在考虑微电网(Microgrid,MG)诚信行为的基础上,文章提出了一种基于动态诚信因子的主从博弈电能交易机制。首先,以MG上报电量与实际交易电量为基准,建立奖惩机制,通过动态调整诚信因子来调节交易电价。其次,构建了一个以微电网群运营商(Microgrid Cluster Operator,MGCO)为领导、MG为跟随者的主从博弈模型。上层模型以最小化微电网群整体功率波动为目标,通过建立交易定价的合约机制,有针对性地调节群内交易电价;下层模型以MG运行费用最小为目标,根据群内交易电价动态调整自身购售电需求量,实现电能优化。最后,通过算例分析验证了该交易机制能够有效抑制微电网群内人为上报虚假信息的行为。