Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

A CASE STUDY ON CONCENTRATION AND DECENTRALIZATION:BEHAVIOR AND DYN AMIC MECHANISM OF SPATIAL EVOLUTION IN METROPOLITAN AREA,NANJING,CHINA

In rapid socio-economic developme nt,the process of concentration and dispersal of various elements tends to be more dramatic,tremendously in fluencing the shaping and transform ation of the space in metropolitan area.Survey of spatial concentration and decent ralization has thus become a basic me thod in examining metropolitan spatial evolution.In this research,three elements were selected as the essential indicato rs of the process:demographic density distribu-tion,employment density distribut ion and business office location.Performance of these elements in Nanji ng City was exam-ined historically.As Nanjing City c ould be regarded as a representative of metropolitan areas in China,its s ituation large-ly suggestes the general characteristics in similar areas of China.Hence based on the investigation of Nanji ng City,four general implications were highligh ted.First,metropolitan areas inChina are in a violentprocess and shift of spatialconcentra-tion and decentralization.Second,from now to at least the near future,c oncentration will continue to be the central fea-ture.Third,the landscape of metrop olitan areas basically exhibits a dual structure character.The gap in en vironmental and ecological qualities among different districts will continue for a l ong time.Fourth,Central Business District(CBD)is playing an important role in helpi ng to convert the traditionally single-centered city structure into a polycentric one.

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data.However,the training mechanism for passing model parameters is still threatened by gradient inversion,inference attacks,etc.With a lightweight encryption overhead,function encryption is a viable secure aggregation technique in federation learning,which is often used in combination with differential privacy.The function encryption in federal learning still has the following problems:a)Traditional function encryption usually requires a trust third party(TTP)to assign the keys.If a TTP colludes with a server,the security aggregation mechanism can be compromised.b)When using differential privacy in combination with function encryption,the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible.In this paper,we propose a hybrid privacy-preserving scheme for federated learning,called Fed-DFE.Specifically,we present a decentralized multi-client function encryption algorithm.It replaces the TTP in traditional function encryption with an interactive key generation algorithm,avoiding the problem of collusion.Then,an embedded incentive mechanism is designed for function encryption.It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy.Subsequently,we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm.The experimental results demonstrate the effectiveness and efficiency of our scheme.

Use of Research Evidence in Policy and Decision-Making: Views, Attitudes and Practices of Health Policy Makers in Lusaka Province of Zambia

Background: The integration of relevant high-quality research evidence into the health decision and policy formulation process is a key strategy for improving health systems especially in developing countries such as Zambia. However, the lack of capacity to understand and value research evidence by policy and decision makers makes it difficult for them to find and use research evidence in a timely manner even when motivated to do so. This study aimed to establish the views, attitudes and practices of policy makers on the use of research evidence in policy and decision-making process in Zambia. Methodology: This descriptive cross-sectional study was conducted in Lusaka, Zambia among selected public health decision and policy making institutions. A purposive sample of 21 consenting policy makers who were working in different positions in the Ministry of Health Headquarters, Provincial and District Health Offices, Health Professions Regulatory Bodies, United Nations Agencies, International Non-Governmental Organizations and University Deans from the University of Zambia participated in the study. A self-administered questionnaire was used to collect data. The IBM? SPSS? Statistics for Windows Version 20.0 was used for data analysis. Results: The concept of Evidence Informed Health Policy was not well understood such that only less than half (47.5%) of the participants reported having heard specifically about Evidence Informed Health Policy meanwhile almost two thirds (61.9%) reported that they used research evidence in decision making and policy formulation. Similar discrepancy was expressed in the understanding of and use of rapid response mechanisms such that although (47.6%) of the participants reported having heard about it, (57%) had never used rapid response mechanisms for deci-sion-making. With regard to the sources of information, about half (52.3) of the participants reported scholarly articles as their main source of evidence. Con-clusion and Recommendations: There is need for more sensitization and ca-pacity building among the decision and policy makers on the importance of using research evidence in decision and policy making process as incorporation of relevant high-quality research evidence into the health policy making pro-cess is a key strategy for improving health systems.

Key Technologies for Decentralized Control Platform with Dynamic Evolution for Railway Transportation System

Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of the control center migration, the principle and technology of system self-monitoring, the principle and technology of the switch-mode of remote control station, the information transmission technology, and the data consistency technology. These key technologies have shown a series of advanced characteristics for decentralized control platform.

去中心化分布式控制系统设计研究

针对上万个被控对象,传统的分布式控制系统无法实现控制器与服务器去中心化,导致效率低且故障率高,设计了一种去中心化分布式控制系统。该系统中的控制器、被控对象、传感器及执行器、服务器通过控制网络进行通信传输,利用冗余备份机制进行控制器动态切换,从而实现控制器去中心化,利用映射表动态更新机制使服务器对相应的被控对象进行数据运算,从而实现服务器去中心化;利用工具箱Truetime2.0及MATLAB/Simulink搭建去中心化分布式电机控制系统仿真平台,以电机为被控对象,研究冗余备份机制对去中心化分布式控制系统的性能影响以及去中心化分布式控制系统的网状网架构能否支持大规模电机正常运行。仿真结果表明,冗余备份机制有效地提高了系统运行的平稳性,系统的网状网架构足以支持上万个电机运行。

基于投票机制的暖通空调空气处理单元传感器故障诊断

现有暖通空调(HVAC)空气处理单元(AHU)的故障诊断研究往往是集中式的。少量的分布式方法大多需要求解大量耗时的优化问题,使得无法及时完成故障诊断。针对以上挑战,该文提出一种基于投票机制的分布式故障诊断方法。在该方法中,建立一个玻尔兹曼机来描述传感器网络,通过传感器之间的相互投票来确定玻尔兹曼机的边权值,基于边权值对玻尔兹曼机的状态也就是传感器的状态进行迭代,从而定位传感器的故障。设计了一种基于欧氏距离的投票策略确定投票值。开发了一种方法,通过在玻尔兹曼机中增加一个额外的节点来重置其权值矩阵,在将玻尔兹曼机对称化的同时,保持原来各传感器之间的投票关系,以保证玻尔兹曼机状态的迭代收敛。该方法不需要求解大量的优化问题,相较于当前的分布式方法计算量小。使用ASHRAE Project RP-1312提供的实际数据对所提方法进行验证。实验结果表明所提方法可以精确且高效地诊断出空气处理单元传感器的偏差故障和漂移故障。

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

基于区块链技术的高校毕业生实习管理应用研究

我国高校毕业生就业、实习管理过程中存在诸多问题,如管理过于中心化、学生实习择业困难、校企沟通不畅、信息不对称、可信度低等问题。文章设计一种基于区块链技术的高校毕业生实习管理系统模型,该系统能够促进学校、用人单位以及学生三方打破信息壁垒,实现信息透明共享、建立可信任机制、激励合法行为,为各利益相关方提供一个可靠的交互平台,具有广阔的应用前景。

劳动教育风险分散机制研究

为提高劳动教育参与人员的安全性、活动有序性及风险抵御性,运用文献综述法及文本分析法,梳理劳动教育风险分散机制变迁历程,分析校责险及学平险发展现状;构建“政府—学校—家庭—社会”四位一体的劳动教育风险分散机制;并结合不同学段劳动教育风险特征,提出针对性的劳动教育风险分散手段。以期为强化劳动教育安全保障提供一定参考。

China's Development Modeli:the Costs and Benefits of the Decentralization Approach to Transition

This article provides a coherent framework within which to understand China’s development model,as well as the successes and the failures of China’s decentralization approach to reform.The combination of political centralization and economic decentralization provide local government with enough incentives to develop local economies,in particular incentives to promote market privatisation locally.However,the relative evaluation-based incentive schemes lead to inter- regional market segmentation,increasing inter-regional development gaps and the unequal provision of certain public goods.The success of early-stage reform can be attributed to the benefits of the decentralization approach.The next stage reform should however focus on minimizing the associated costs.China’s gradualist reform can be seen as a mechanism design issue under the control of central government.Therefore,it is essential to take both the costs and benefits of the decentralization approach into account in the design of the next-stage reform package.

一种基于合同理论的可激励联邦学习模型

针对目前较少研究去中心化联邦学习中的激励机制设计,且已有联邦学习激励机制较少以全局模型效果为出发点的现状,该文为去中心化联邦学习加入了基于合同理论的联邦学习激励机制,提出一种新的可激励的联邦学习模型。使用区块链与星际文件系统(IPFS)取代传统联邦学习的中央服务器,用于模型参数存储与分发,在此基础上使用一个合同发布者来负责合同的制定和发布,各个联邦学习参与方结合本地数据质量选择签订合同。每轮本地训练结束后合同发布者将对各个本地训练模型进行评估,若满足签订合同时约定的奖励发放条件则发放相应的奖励,同时全局模型的聚合也基于奖励结果进行模型参数的聚合。通过在MNIST数据集以及行业用电量数据集上进行实验验证,相比于传统联邦学习,加入激励机制后的联邦学习训练得到的全局模型效果更优,同时去中心化的结构也提高了联邦学习的鲁棒性。

长三角环境治理动力机制研究——环境分权抑或环保立法

生态环境治理是高质量发展的核心目标之一,为治理污染,我国政府制定诸多环境政策,并将生态文明建设纳入国家发展总体布局,这表明我国政府高度重视环境治理,也显示出我国环境治理的严峻性。环境是典型的公共物品,具有较强的负外部性,其赋予环境的消费非竞争性和收益非排他性,政府作为理性的“经济人”,具有追求自身利益最大化的偏好,以上特征使得政府在治理环境过程时权责不清、环境治理缺乏动力的现象频繁发生,增加环境治理的困难。环境分权、环保立法是环境治理的重要手段,能否在激励机制与问责机制方面激发地方政府环境治理动力,降低环境污染水平?文章探究环境分权和环保立法对环境治理的影响,并分析环境分权与环保立法对环境治理的动力机制,在此基础上,以长三角27个中心城市为样本,选取2003—2019年市级面板数据,采用双重差分法实证检验环境分权与环保立法对环境治理的影响,同时通过倾向得分匹配法、动态效应检验、安慰剂检验等验证文章研究结果,并提出相关政策建议。研究结果表明:环境分权激励政府进行环境治理,有效降低长三角环境污染水平,并对环境污染滞后三期发现环境分权对环境污染具有明显的滞后作用,其影响具有长效性;相较于环境分权而言,环保立法对长三角城市群环境污染的作用较为微弱,并通过动态效应检验、增加控制变量和安慰剂检验等角度进行检验,使结果更为可靠;值得注意的是,环境分权与环保立法政策组合的环境污染治理动力更为显著,两种政策组合对环境污染的抑制作用高于两者单独的政策影响。因此,文章从建立区域环境协调治理联动、强化法治建设、激励各主体环境治理等方面提出相应的环境治理政策建议,持续发挥环境分权在长期治理污染的作用,完善区域环保立法标准体系,强化环境分权与环保立法的合作治理互动效应,增强环境治理动力。文章从市级层面视角分析环境分权与环保立法对环境治理的动力机制,并通过实证进行一系列检验,丰富市级层面环境治理研究,并通过多角度稳健性检验验证环境分权与环保立法对环境治理的影响,为地方政府治理环境提供有益借鉴。

浅析区块链底层平台技术与发展

针对不同区块链平台在去中心化程度、性能、扩展性、可部署性等方面特性的不同,本文对部分主流平台从区块链类型、共识算法、智能合约部署、数据存储、对外接口、性能等方面进行了对比总结,结果表明:深度定制化的联盟链在性能上较优秀;公有链去中心化程度高,但性能受限;区块链平台技术总体向高性能、高扩展性方向发展。

考虑不完全信息披露的共享储能联合调频分散交易机制

随着大量分布式储能的接入,分散交易机制能够有效满足火电机组-共享储能联合调频新商业模式下交易隐私保护和自主选择交易对象的需求,然而分散交易时,不完全信息下个体的有限理性决策往往影响了资源的有效配置。在此情景下,及时地披露适当的市场整体表现信息,能够有效减少共享储能搜索信息成本,促进市场快速达到均衡状态。基于此,提出了一种考虑不完全信息披露的共享储能联合调频分散交易机制,以克服分散交易给市场效率和社会总福利带来的负面影响。首先,针对分散交易中的信息不完全问题,设计了基于效率违约理论的共享储能违约机制和考虑不完全信息披露的市场交易信息公布机制,并利用区块链取代第三方交易平台实施分散交易流程。然后,构建了共享储能和火电机组谈判交易策略模型,该模型包括基于主动试错学习的共享储能适应性报价修正策略和火电机组优化决策模型。最后,通过模拟10个火电机组和50个共享储能构成的市场验证了所设计共享储能联合调频分散交易机制的有效性。算例结果表明,适度的信息披露及合理的违约赔偿能使得分散交易快速达到市场均衡状态,且能有效提升社会效益。

引入通信与探索的多智能体强化学习QMIX算法

非平稳性问题是多智能体环境中深度学习面临的主要挑战之一,它打破了大多数单智能体强化学习算法都遵循的马尔可夫假设,使每个智能体在学习过程中都有可能会陷入由其他智能体所创建的环境而导致无终止的循环。为解决上述问题,研究了中心式训练分布式执行(CTDE)架构在强化学习中的实现方法,并分别从智能体间通信和智能体探索这两个角度入手,采用通过方差控制的强化学习算法(VBC)并引入好奇心机制来改进QMIX算法。通过星际争霸Ⅱ学习环境(SC2LE)中的微操场景对所提算法加以验证。实验结果表明,与QMIX算法相比,所提算法的性能有所提升,并且能够得到收敛速度更快的训练模型。

县级财力保障机制与城乡居民收入差距——基于局部准自然实验的经验证据

构建公平合理的财税体制是降低收入分配不平等、实现共同富裕的必要制度保障。基于中国式财政分权框架,分析中央财政县级基本财力保障机制对缩小县域内城乡收入差距的制度逻辑。在此基础之上,利用2013—2019年667个县级市辖区的年度面板数据,构建局部准自然实验,运用断点回归设计(RDD)方法,评估中央财政县级基本财力保障机制对县域内城乡收入差距的影响、异质性和作用路径。结果表明:保障机制可以引导县级政府财政支出向教育、医疗和农业农村等领域倾斜;可以提高县级政府的教育财政支出效率,进而推动城乡收入差距的缩小。基于上述结论,缩小城乡收入差距需要进一步完善均衡性转移支付制度,建立健全财政支出的监督评价体系,切实推动地方政府财政支出结构向民生领域倾斜,并防止财政资金的无效率使用。

基于注意力学习的多智能体路径规划协作策略

多智能体路径发现(MAPF)是一个具有挑战性和有意义的问题,其中要求所有智能体在不相互碰撞和避免障碍的情况下有效地达到其目标地点。在多智能体路径发现中,有效地提取和表达智能体的观察结果、有效地利用历史信息以及与相邻智能体之间的有效通信是完成多智能体协作任务所面临的挑战。为了解决这些问题,本文提出了一个精心设计的模型,利用附近智能体的局部状态,并为每个智能体输出一个最优的执行动作。我们通过使用残差注意力卷积神经网络(residual attention CNN)来提取局部观测构建局部观测编码器,并使用Transformer架构来构建交互层来组合智能体的局部观测。为了提高该模型的成功率。我们还引入了一种新的指标,即额外时间比率(ETR)。实验结果表明,我们提出的模型在成功率和ETR方面优于以前的大多数模型。此外,我们还完成了对模型的消融研究,并证明了模型各组成部分的有效性。

论中国式分权下的环境治理逻辑:理论分析及实证检验

健全现代环境治理体系是推动绿色发展的必然要求,从学理上阐明环境治理的逻辑是其题中应有之义。本文从中国的历史和现实出发,基于理论和实证两个角度梳理了中国式分权下的环境治理逻辑。首先,本文创新性地将中国式分权的要素凝练为组织结构(权力分配)、激励机制和运行机制三点,通过阐述三者之间的关系梳理出中国式分权的逻辑框架。其次,鉴于激励机制对运行机制的引导作用,在中国式分权的逻辑框架内,本文以权力分配为基础,着重考察激励机制和运行机制对环境治理的影响,从而总结出中国式分权下的环境治理逻辑。再次,采用县级面板数据,对省级以下分权与环境治理之间的因果关系进行回归,结果发现分权程度的提高不利于环境治理。最后,结合理论机理,对基准回归结果的内涵加以解释,认为经济激励、政治激励利于财政收入的增加和经济发展,可能不利于环境治理,而社会激励利于公共品供给,从而于环境治理有益。此外,激励目标的多元化弱化了政治激励对经济增长的作用,增强了社会激励对公共品提供的积极作用,两者均益于环境治理。本文的研究结果对如何发挥激励机制的作用来督促地方政府提供高质量的公共服务有一定启示。

一种REB&PMDS的无线MAC协议

针对分散无线网络的媒质访问控制,提出了一种新的MAC算法;算法根据几何分布采用随机长度的突发来重复消除信道访问节点,其中每个发送节点能够在每个时隙中执行载波侦听操作或执行一个短突发或干扰发送;计算了在单次消除和重复消除中n个节点中有m个节点在消除阶段存活下来的概率以及重复消除中一个节点成为唯一获胜者的概率;并基于基本优先级向量提出了一种实现差异化服务的相对优先级机制,从而提高整个网络的信道总利用率;仿真实验结果表明,提出的协议算法相比于目前广泛采用的几种MAC协议在信道接入成功率、信道利用率和信道分配公平性方面都有明显的优势。