南水北调河南段水资源优化利用与管理决策软件

本项目为2004年河南省杰出人才创新基金项目（0421000300）,在研究中充实了复杂系统水资源动力学特征分析的理论体系和复杂系统水资源配置的理论与方法;丰富了水文水资源要素预测的预测模型;构建了城市生态环境评价指标体系和评价模型;提出了水资源配置优化模型与模拟模型的有机耦合技术,建立了基于优化模型和模拟模型耦合的水资源合理配置模型系统,并应用于南水北调中线工程河南受水区水资源合理配置;详细介绍了典型城市（郑州市）的水资源配置方案的编制;探讨了多水源供水的水价格体系;研制了南水北调工程河南段水资源优化利用与管理决策软件,主要研究成果如下：     

还原模型与功能主义——兼评金在权的还原的物理主义

首先对内格尔的经典还原模型和金在权的功能还原模型做了比较,并指出,内格尔模型是在认识论或方法论的层面提出的,而金在权模型是在实践论或本体论的层面提出的;因此,这两个还原模型不是对立的,而是互补的。接着深入到个别事件的内部结构,对金在权的局部还原理论做出分析和评价,并指出其困境和出路。在金在权的三元有序组的事件结构的基础上提出四元有序组的事件结构,据此对功能意义和功能结构作出区分,进一步揭示了功能整体与其实现者之间的随附性关系。最后强调功能实在论的本体论立场,并借助实体-偶性和原因-结果这两对先验范畴对功能实在论和随附性概念给以形而上学的说明和辩护。     

模型化推理与理论的建构

理论模型并不完全与理论一致.模型化推理的逻辑特征在于在原型与模型之间,以及模型与理论假设之间尽可能地建立起可靠的、完善的逻辑联系.利用模型化推理建构理论时,首先是提出模型,然后对其进行检验,最后根据检验结果,看看这个模型是否代表待说明现象的内在结构.理论的延伸可沿两条不同的路线进行.一是形式化的延伸;二是非形式化的延伸,它又可区分为两类:展开与发展.     

边缘星系群挑战宇宙标准模型理论

<正>一个国际科学家小组不久前发布报告称,银河系边缘临近仙女座的矮卫星星系群,其形成与行为不符合目前公认的星系形成标准模型。这意味着一直被奉为物理学经典的宇宙标准模型理论的权威性将遭受质疑。所谓宇宙"标准模型",是广泛被接受的关于宇宙起源和演化范式的理论,在20世纪40年代由伽莫夫、阿尔芬和赫尔曼提出。该理论认为,宇宙是一个致密炽热的奇点于137亿年前一次大爆炸     

企业核心刚性的治理:基于CAS的二阶领导模型

基于复杂自适应系统(CAS)理论,提出了超越企业核心刚性的二阶领导模型。该模型指出,在核心刚性超越的每一阶段中,领导除了实施普通管理和抑制焦虑外,在阶段一中,应采取措施影响控制参数,使组织尽快进入创造性空间;在阶段二中,应采取措施激发和贡献于双环学习。     

发现星系合并频率正逐渐下降

<正>美国哈佛-史密森天体物中心天文学家近日提出一种全的计算星系合并频率的理论型,该模型可以模拟大约三亿年大小的宇宙空间中的星系形过程。天文学家通过全新的理模型,发现了星系合并频率在渐下降的确切证据。研究人员为,这将能够实现对宇宙形成的各种星系合并事件的精确究。     

欧洲智慧型区域创新政策体系实践案例及其经验启示

通过对基于知识增长的欧盟区域创新模型和智慧型创新政策体系的系统阐述,力图对我国设计差异化的区域创新政策体系提供有益的借鉴。首先,本文对欧洲学者率先提出的区域创新的知识差异化模型定义及其分类进行了详述;其次,对欧洲KIT项目进行的区域创新模式实证分类结果以及智慧型创新政策体系实践应用案例进行了翔实阐释;最后,以欧洲智慧型创新政策体系实践应用案例作为参考,指出我国当前区域创新政策体系设计中存在的不足,进而提出推动我国采纳差异化、动态化和智慧化区域创新政策体系的若干建议和实践策略。     

多尺度水资源管理模型融合技术及在中国北方的应用研究

该项目是中荷两国2009年JSTP（Joint Science and Technology Program）框架下的研究课题之一。项目以中国西北资料缺少、生态脆弱的河西走廊干旱内陆河区为背景,通过空间对地观测及地面水资源监测网络数据的融合,发展多尺度水资源管理模型技术;通过同化降水、蒸发、冰川、地下水及开发利用等数据,研究水资源时空演变规律及其生态环境效应,合理评价流域水资源;通过同化驱动水文与水资源模型,提出中国北方地区气候变化下的流域水资源规划与管理策略;通过联合研究,促进两国科技交流,提高科技人员水平。     

论模型认识论中的同构关系

讨论模型与世界的关系,涉及"模型认识论"问题。从1912年罗素提出的"人的知觉与被知觉的物理世界之间有一种同构的关系",到后来的沃勒尔在实在论与反实在论的争论中创新性地提出了结构实在论而引发人们对模型理论中"同构"关系的思考,再到2003年弗伦奇和科斯塔指出模型的表征与现实世界是一种同构或部分同构(结构)的关系。无论问题从哪个角度提出,关于"模型认识论"的回答都指向对"同构关系"的讨论。     

科学哲学中的统计说明模型

虽然概率论和统计学运用于现代科学的各个分支(不仅是自然科学,甚至包括政治学和社会学等社会科学)已有相当长的历史,但对于概率统计的哲学解释却姗姗来迟。1962年,美国科学哲学家亨佩尔在《演绎定律与统计说明》一文中,第一次系统地提出了科学的归纳-统计(I-S)说明模型。后来,他和其他一些科学哲学家发展了这一说明模型。到了七十年代,韦斯列·萨尔门等人在此基础上提出了统计相关(S-R)说明模型,并在八十年代加以发展,用因果关系来加以说明。本文着重介绍和讨论这两种统计说明模型及其与因果问题的联系,用以丰富和发展我们的科学哲学。     

Belief Systems and the Modeling Relation

The paper presents the most general aspects of scientific modeling and shows that social systems naturally include different belief systems (i.e. different models). Belief systems differ in a variety of respects, most notably in the selection of suitable qualities to encode and the internal structure of the observables. The following results emerge from the analysis: (1) conflict is explained by showing that different models encode different qualities, which implies that they model different realities; (2) explicitly connecting models to the realities that they encode makes it possible to clarify the relations among models; (3) by understanding that social systems are complex one knows that there is no chance of developing a maximal model of the system; (4) the distinction among different levels of depth implicitly includes a strategy for inducing change; (5) identity-preserving models are among the most difficult to modify; (6) since models do not customarily generate internal signals of error, strategies with which to determine when models are out of synch with their situations are especially valuable; (7) changing the form of power from a zero sum game to a positive sum game helps transform the nature of conflicts.     

亨普尔的律则解释及其在社会科学中的困难--有关解释的相关性问题

本文在亨普尔的律则解释模式——演绎-律则模式和归纳-统计模式适用于社会科学的假设下，通过对相关性问题以及高概要求的讨论，考察了I—S模式对社会科学的适用性。     

Mathematical Models and Reality: A Constructivist Perspective

To explore the relation between mathematical models and reality, four different domains of reality are distinguished: observer-independent reality (to which there is no direct access), personal reality, social reality and mathematical/formal reality. The concepts of personal and social reality are strongly inspired by constructivist ideas. Mathematical reality is social as well, but constructed as an autonomous system in order to make absolute agreement possible. The essential problem of mathematical modelling is that within mathematics there is agreement about ‘truth’, but the assignment of mathematics to informal reality is not itself formally analysable, and it is dependent on social and personal construction processes. On these levels, absolute agreement cannot be expected. Starting from this point of view, repercussion of mathematical on social and personal reality, the historical development of mathematical modelling, and the role, use and interpretation of mathematical models in scientific practice are discussed.     

技术的社会塑形论的三重批判维度

技术的社会塑形论实现了传统技术哲学的社会学转向,它具有三重批判维度;一、对现象学技术哲学的形而思辨的批判,强调经验描述的重要性;二、对技术决定论的技术自主思想的批判,提出技术的社会建构;三、对传统技术发展观的线性模式的批判,主张技术发展的多向模式。     

科学知识变化的三种解释模式及其评价

科学知识的发展变化问题是20世纪科学哲学所关注的核心论题之一。纵观20世纪科学哲学的发展，主要地形成了三种解释模式：逻辑一理性论模式、认知论模式和社会学模式。虽然在20世纪的最后30年认知主义者和科学知识社会学家都对逻辑一理性论进行了猛烈的批判，但它们也未能对科学知识的变化问题给出令人满意的说明。对科学知识发展变化的完全说明应是这三种模式的有机统一。     

Modelling Experiments as Mediating Models

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled.

The location model for mixtures of categorical and continuous variables

Recent research into graphical association models has focussed interest on the conditional Gaussian distribution for analyzing mixtures of categorical and continuous variables. A special case of such models, utilizing the homogeneous conditional Gaussian distribution, has in fact been known since 1961 as the location model, and for the past 30 years has provided a basis for the multivariate analysis of mixed categorical and continuous variables. Extensive development of this model took place throughout the 1970’s and 1980’s in the context of discrimination and classification, and comprehensive methodology is now available for such analysis of mixed variables. This paper surveys these developments and summarizes current capabilities in the area. Topics include distances between groups, discriminant analysis, error rates and their estimation, model and feature selection, and the handling of missing data.     

科学发展模式的生物进化论类比研究

科学发展的多元进化模式是科学哲学关于科学进步问题的成果之一。与其相对应的生物进化论类比则是近年来国际学术界争议的焦点。我们将从当代生物进化论出发,论证科学发展作为增生式的非目的性过程,其生物进化类比是恰当的。     

论自然工程与社会工程的关系

相对于自然工程而言,社会工程是非物质形态的柔性工程,是综合利用人文社会、自然以及工程科学的相关知识,建构和选择社会运行的具体模式和社会发展方向,寻找解决社会问题,重构社会关系的制度和政策的一般理论和方法。自然工程就其基本构成而言,具有内圈和外圈的双重结构,在自然工程活动过程中工程决策阶段、工程规划阶段和工程实施过程阶段,自然工程和社会工程紧密联系,自然工程的运作过程同时也是社会工程活动的运作过程。     

Understanding Scientific Study via Process Modeling

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”.