统计代写|机器学习作业代写machine learning代考|Cities as Convergent Autopoietic Systems

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  • Statistical Inference 统计推断
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  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
统计代写|机器学习作业代写machine learning代考|Cities as Convergent Autopoietic Systems

统计代写|机器学习作业代写machine learning代考|Introduction

The evolution of the human-constructed environment is a roadmap to understanding the impact of technology on nature and the resulting symbiotic relationship of humans, cities, and the natural world. Prior to the industrial revolution, human habitation relied on the delicate balance with nature, and as such, most concentrations of the human population lived in harmony with the environment from which it sustained essential resources. The nineteenth century was the century of industrialization that allowed the vast exploitation of physical resources and the construction of extensive infrastructure across the world. This new expansion disrupted the delicate balance between humans and nature and gave rise to unsustainable resource-intensive industrial development that propelled the twentieth century’s explosive growth of the modern urban metropolis. The twenty-first century will be defined by carbon-neutral innovation, and the design and development of sustainable smart cities. If managed correctly on both global and local levels, new intelligent urban systems will allow humans and their habitat to return to a more harmonious relationship.

Our renewed symbiosis with nature will drive the transformation of cities in the twenty-first century, reaffirming the critical role cities must play in the fight against climate change and planetary degradation. Cities offer many competitive advantages over nations when it comes to responding to current and emerging challengesflexibility, adaptability, access to capital, leading technology, educated workforce,

transportation, and communications infrastructure. Furthermore, cities are strategically positioned to benefit from the Fourth Industrial Revolution, where humans and machines will become increasingly connected, reaching an unprecedented level in the form of advanced co-design and co-development to accelerate innovation, commonwealth, quality of life, and individual well-being. To achieve the highest potential outcomes for humanity and planet earth, this next stage in humanity’s evolution will require a deeper understanding of the nature of living systems and the potential application of convergent properties that guide the behavior of selfsustaining systems.

Autopoiesis, or the process of self-production and self-organization, is a theory introduced in the second part of the twentieth century to describe the characteristics and behaviors of living entities and biological systems. Humberto Maturana and Francisco Varela introduced the term and defined an autopoietic system as ” $a$ network of inter-related component-producing processes such that the components in interaction generate the same network that produced them.” (Geyer 1995, pg. 12). The concept has seen wide application in the fields of mathematics (Robert Rosen), the study of cognition (Maturana and Varela; Luhman; Mingers), and in studies of the nervous system, information systems, sociology (Luhman; Mingers; Luisi and Damiano), legal studies (Hilpold), biomimetics, and others.

统计代写|机器学习作业代写machine learning代考|Theory: Evolution of Living Systems Thinking

In this section of the paper, we present a brief overview of the evolution of Systems Thinking presenting a selection of the most impactful works for the purpose of this paper, including General Systems Theory, Cybernetics, Autopoiesis, Second-order Cybernetics, and Anticipatory Systems drawing from the different classes of systems to synthesize the most important principles and characteristics that govern living systems. We start our research by introducing the theory of Cybernetics (Norbert Wiener), Second-order Cybernetics and General Systems Theory (Bertalanffy). Key concepts and characteristics serve as the foundation of Maturana and Varela’s Autopoiesis Theory-our research’s central theme. The self-preserving nature of autopoietic systems is the result of structural determinism and structural couplingunderlining behavioral traits of all living systems. Autopoiesis theory finds varied and increasingly useful application beyond the domain of Biology, including in Sociology (Luhmann 2012; Mingers 1991; Damiano and Luisi 2010), Governance (Andrew Dunsire 1996), Law and Human Rights (Peter Hilpold 2011), Smart Cities (Kirwan and Fu 2020), and Biomimetics (Robert Rosen 1978, 1985). In Sect. 3 we present and briefly discuss their implications. In Sect. 4, we discuss how these characteristics converge across the six core smart city functions to form the basis of Autopoietic Operating Systems (AOS).

统计代写|机器学习作业代写machine learning代考|Cybernetics

Norbert Wiener popularized the concept of $C$ ybernetics in the 1940 s while researching the application of control theory in relation to complex living and non-living systems. Wiener defined Cybernetics as “the scientific study of control and communication in the animal and the machine” (Wiener 1948). The term “Cybernetics” is derived from the Greek language and translated in English as “the art of steering.” Indeed, it offers a powerful framework for analyzing the properties and understanding the behavior of living systems. Wiener discovers that both living and non-living systems operate according to cybernetic principles-they require communication to achieve effective action through continuous internal and external feedback. In biological terms, the process of feedback takes place to maintain homeostasis, an equilibrium/optimal state of a system. This self-correcting mechanism is critical to survival because it drives adaptation to random environmental events and conditions. Their ability to change through corrective action and adaptation is achieved through a perpetual cycle of sensing, gathering information through a series of feedback loops, and comparing to the system’s original goals, prior to undertaking corrective action in a continuous pursuit of homeostasis.

Second-order cybernetics refers to systems classified as entities that encapsulate the capacity to project their operations on the environment and on themselves, regardless of whether the system is represented by a group or an individual. These operations give birth to variety within the environment or within systems themselves. This aspect can be regarded as a consequence of systematic variation, rendering systems as recursive. In recursive systems, communications can be conveyed, and observations can be noticed. The differences that exist between firstorder and second-order cybernetics have been examined by von Foerster and others including Pask, Varela, Umpleby, and Parsons. These dissimilarities highlight the relationship between the aim of a model and the goal of the modeler, the connection between systems that are autonomous and controlled systems, identifying links between variables within a system and the interaction between the observed system

and the observer, and can be applied to various theories that embody social systems and hypotheses concerning the interaction between society and ideas. The latter relationship illustrates a difference that appears to illuminate Parsons’ approach as a theorist concerned with first-order systems as well as the stability and the maintenance of systems. On the other hand, Luhman, as a cybernetician, was more interested in morphogenesis and change in second-order systems. (Geyer 1995).
The relationship between first- and second-order cybernetics defines a progression in systems organizational behavior from a linear command and control model to an organic and autonomous system that incorporates the observer. In this more holistic formation, the observer becomes part of the system itself, and hence part of its evolutionary trajectory. This recursive interaction between the observer and the system is an example of more complex and intelligent systems.

统计代写|机器学习作业代写machine learning代考|Cities as Convergent Autopoietic Systems


统计代写|机器学习作业代写machine learning代考|Introduction

人为环境的演变是理解技术对自然的影响以及由此产生的人类、城市和自然世界的共生关系的路线图。在工业革命之前,人类居住依赖于与自然的微妙平衡,因此,大多数人口集中与维持基本资源的环境和谐相处。19 世纪是工业化的世纪,它允许在世界范围内对物质资源进行大量开发并建设广泛的基础设施。这种新的扩张破坏了人与自然之间微妙的平衡,并引发了不可持续的资源密集型工业发展,推动了二十世纪现代城市大都市的爆炸式增长。二十一世纪将由碳中和创新以及可持续智慧城市的设计和发展来定义。如果在全球和地方层面都得到正确管理,新的智能城市系统将使人类及其栖息地恢复更和谐的关系。

我们与自然的重新共生将推动 21 世纪城市的转型,重申城市在应对气候变化和地球退化方面必须发挥的关键作用。在应对当前和新出现的挑战灵活性、适应性、获得资本、领先技术、受过教育的劳动力、


自创生,或自我生产和自我组织的过程,是二十世纪下半叶引入的一种理论,用于描述生物体和生物系统的特征和行为。Humberto Maturana 和 Francisco Varela 介绍了该术语并将自创生系统定义为“一种相互关联的组件生产过程的网络,使得交互中的组件生成生产它们的相同网络。(盖尔 1995 年,第 12 页)。该概念已广泛应用于数学领域(罗伯特·罗森)、认知研究(Maturana 和 Varela;Luhman;Mingers),以及神经系统、信息系统、社会学研究(Luhman;Mingers;Luisi 和 Damiano) )、法律研究 (Hilpold)、仿生学等。

统计代写|机器学习作业代写machine learning代考|Theory: Evolution of Living Systems Thinking

在本文的这一部分,我们简要概述了系统思维的演变,并为本文的目的介绍了一些最具影响力的作品,包括通用系统理论、控制论、自创生、二阶控制论和预期系统从不同类别的系统中提取,以综合管理生命系统的最重要的原则和特征。我们从介绍控制论(Norbert Wiener)、二阶控制论和一般系统理论(Bertalanffy)开始我们的研究。关键概念和特征是 Maturana 和 Varela 的自创生理论(我们研究的中心主题)的基础。自创生系统的自我保护性质是结构决定论和结构耦合的结果,强调了所有生命系统的行为特征。自创生理论在生物学领域之外发现了各种各样且越来越有用的应用,包括社会学(Luhmann 2012;Mingers 1991;Damiano 和 Luisi 2010)、治理(Andrew Dunsire 1996)、法律和人权(Peter Hilpold 2011)、智能城市( Kirwan 和 Fu 2020)和仿生学(Robert Rosen 1978、1985)。昆虫。3 我们介绍并简要讨论它们的含义。昆虫。在图 4 中,我们讨论了这些特征如何在六个核心智慧城市功能中融合,形成自创操作系统 (AOS) 的基础。包括社会学(Luhmann 2012;Mingers 1991;Damiano 和 Luisi 2010)、治理(Andrew Dunsire 1996)、法律和人权(Peter Hilpold 2011)、智能城市(Kirwan 和 Fu 2020)和仿生学(Robert Rosen 1978、1985) )。昆虫。3 我们介绍并简要讨论它们的含义。昆虫。在图 4 中,我们讨论了这些特征如何在六个核心智慧城市功能中融合,形成自创操作系统 (AOS) 的基础。包括社会学(Luhmann 2012;Mingers 1991;Damiano 和 Luisi 2010)、治理(Andrew Dunsire 1996)、法律和人权(Peter Hilpold 2011)、智能城市(Kirwan 和 Fu 2020)和仿生学(Robert Rosen 1978、1985) )。昆虫。3 我们介绍并简要讨论它们的含义。昆虫。在图 4 中,我们讨论了这些特征如何在六个核心智慧城市功能中融合,形成自创操作系统 (AOS) 的基础。

统计代写|机器学习作业代写machine learning代考|Cybernetics

诺伯特·维纳(Norbert Wiener)普及了C在 1940 年代,研究控制理论在复杂的生物和非生物系统中的应用。维纳将控制论定义为“对动物和机器控制和交流的科学研究”(维纳,1948)。“控制论”一词源自希腊语,在英语中翻译为“操纵的艺术”。事实上,它为分析生命系统的特性和理解行为提供了一个强大的框架。维纳发现生物和非生物系统都根据控制论原则运作——它们需要通过持续的内部和外部反馈来实现有效的行动。在生物学术语中,反馈过程的发生是为了维持体内平衡,即系统的平衡/最佳状态。这种自我纠正机制对生存至关重要,因为它推动了对随机环境事件和条件的适应。他们通过纠正行动和适应改变的能力是通过一个永久的感知循环来实现的,通过一系列反馈循环收集信息,并与系统的原始目标进行比较,然后再采取纠正行动以不断追求稳态。

二阶控制论是指被归类为实体的系统,这些系统封装了将其操作投射到环境和自身上的能力,而不管系统是由群体还是个人代表。这些操作在环境或系统本身内产生了多样性。这方面可以被视为系统变化的结果,使系统具有递归性。在递归系统中,可以传达通信,并且可以注意到观察。von Foerster 和包括 Pask、Varela、Umpleby 和 Parsons 在内的其他人已经研究过一阶和二阶控制论之间存在的差异。这些差异突出了模型的目标和建模者的目标之间的关系,

和观察者,并且可以应用于体现社会系统和关于社会与思想之间相互作用的假设的各种理论。后一种关系说明了一种差异,似乎阐明了帕森斯作为关注一阶系统以及系统稳定性和维护的理论家的方法。另一方面,卢曼作为控制论者,对二阶系统的形态发生和变化更感兴趣。(盖尔 1995 年)。

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。



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