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# 物理代写|计算物理代写Computational physics代考|PHYS232A Desiderata of a Theory of Computation

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## 物理代写|计算物理代写Computational physics代考|Desiderata of a Theory of Computation

In this chapter, I outline the various demands that arise in a philosophical account of computation. This task is important for two reasons. One is that different lists of demands may lead to different accounts of computation. This can explain why certain accounts of computing that are successful in one domain fail to apply in other domains. The second reason is that some lists of demands set the threshold too high, and thus eventually lead to dead ends. We need a list of desiderata that sets the stage for a doable project, one that is not overly ambitious and therefore ends up with too little. In this chapter, I discuss the two lists of desiderata put forward by Brian Cantwell Smith $(1996,2002)$ for a general theory of computation and by Gualtiero Piccinini $(2007,2015)$ for an account of physical (concrete) computation. These authors differ in approach: while Smith focuses on the theory’s scope, Piccinini formulates a set of features that should be included in the theory. I discuss each of these approaches in turn.

## 物理代写|计算物理代写Computational physics代考|Scope

Smith (2002: 24ff.) states that a comprehensive theory of computing must meet three criteria. The empirical criterion is to do justice to real-world examples of computing (such as calculators and desktops). The conceptual criterion is to acknowledge related concepts such as interpretation, representation, and semantics. The cognitive criterion is to provide solid grounds for the computational theory of mind and for cognitive science. ${ }^{2}$

The account I propose in this book aims to meet these criteria, at least to some degree. The first aim is to account for physical computation (a goal parallel to Smith’s empirical criterion) – namely, to relate both to real-world examples of computing, such as laptops and smartphones, and to more recent technologies such as neural, quantum, DNA, membrane, and other styles of computing. The second aim is to pay special attention to the claim that cognitive and/or neural systems compute. This aim is similar to Smith’s cognitive criterion. As for Smith’s conceptual criterion, I agree that an account of computation should acknowledge closely related concepts such as interpretation, representation, and semantics. This, of course, does not mean providing accounts for these notions, which is even harder than accounting for computation-but rather explaining how these notions relate to computation.

That said, we must restrict the scope of the account. Setting an overly wide scope by trying to account for too much may lead to despairing conclusions (Fresco 2008). Smith himself famously summed up his project with the gloomy remarks that “computation is not a subject matter” (1996: 73); that “there will never be a satisfying and intellectually productive ‘theory of computation’ of the sort I initially set out to find” (1996: 74); and that “we will never have a theory of computing because there is nothing there to have a theory of” $(2010: 38)$.

One restriction is that we do not have to account for every use of the term computation, or every real-world example of it. Neither will I distinguish between the derivatives of computation-that is, computing, computational, etc.-nor, by the same token, will I make too much of the differences between computing entities, such as agents, systems, processes, states, events, and so forth. I will also refer to calculation and its derivatives as synonymous with computation. This is not to say that there are no differences between these terms, but I will draw attention to such differences only when they are important.

A second restriction pertains to various types of theses that state that the physical universe is best modeled as a giant computer or as a network of computational processes of one sort or another (such as a deterministic cellular automaton). The most renowned thesis in this genre was put forward by the computer pioneer Konrad Zuse (1967). Zuse’s thesis states that the physical universe is fundamentally a cellular automaton. Whether this thesis is true is an open question, though many believe it to be false. ${ }^{3}$ In any event, my account of physical computation does not address these theses. The account is not about the fundamentals of the physical universe, but about the fundamentals of physical computing systems.

## 物理代写|计算物理代写COMPUTATIONAL PHYSICS代 考|SCOPE

suchasadeterministiccellularautomaton. 这一类型中最著名的论文是由计算机先驱康拉德·祖泽（Konrad Zuse）提出的 $1967 .$ Zuse 的论文指出，物理宇宙本质 上是一个元胞自动机。这个论点是否正确是一个悬而末决的问题，尽管许多人认为它是错误的。 ${ }^{3}$ 无论如何，我对物理计算的描述并没有解决这些问题。该帐户不 是关于物理宇宙的基础知识，而是关于物理计算系统的基础知识。

## 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。