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

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习machine learning代写方面经验极为丰富，各种代写机器学习machine learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|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.

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## MATLAB代写

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。