### 数学代考|计算复杂性理论代写computational complexity theory代考|ALife Summary

statistics-lab™ 为您的留学生涯保驾护航 在代写计算复杂性理论computational complexity theory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。我们的专家在代写计算复杂性理论computational complexity theory代写方面经验极为丰富，各种代写计算复杂性理论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

## 数学代考|计算复杂性理论代写computational complexity theory代考|ALife Summary

Based on the previous discussion, the essential features of an ALife program can be summarized as follows:

• Population: A population of organisms or individuals is considered. The population may be diversified, and individuals may vary their characteristics, behaviors, and accumulated resources, in both time and space.
• Interaction: Interaction requires sensing of the immediate locale, or neighborhood, on the part of an individual. An organism can simply become “aware” of other organisms in its vicinity or it may have a richer set of interactions with them. The individual also interacts with its (non-agent) environment in its immediate locale. This requirement introduces spatial aspects into the problem, as organisms must negotiate the search for resources through time and space.
• Sustainment and renewal: Sustainment and renewal requires the acquisition of resources. An organism needs to sense, find, ingest, and metabolize resources, or nourishment as an energy source for processing into other forms of nutrients. Resources may be provided by the environment, i. e., outside of the agents themselves, or by other agents. The need for sustainment leads to competition for resources among organisms. Competition could also be a precursor to cooperation and more complex emergent social structures if this proves to be a more effective strategy for survival.
• Self-reproduction and replacement: Organisms reproduce by following instructions at least partially embedded within themselves and interacting with the environment and other agents. Passing on traits to the next generation implies a requirement for trait transmission. Trait transmission requires encoding an organism’s traits in a reduced form, that is, a form that contains less than the total information representing the entire organism. It also requires a process for transforming the organism’s traits into a viable set of possible new traits for a new organism. Mutation and crossover operators enter into such a process. Organ-

isms also leave the population and are replaced by other organisms, possibly with different traits. The organisms can be transformed through changes in their attributes and behaviors, as in, for example, learning or aging. The populations of organisms can be transformed through the introduction of new organisms and replacement, as in evolutionary adaptation.

As we will see in the section that follows, many of the essential aspects of ALife have been incorporated into the development of agent-based models.

## 数学代考|计算复杂性理论代写computational complexity theory代考|Agent-Based Modeling Topologies

Agent-based modeling owes much to artificial life in both form and substance. Modeling a population of heterogeneous agents with a diverse set of characteristics is a hallmark of agent-based modeling. The agent perspective is unique among simulation approaches, unlike the process perspective or the state-variable approach taken by other simulation approaches.

As we have seen, agents interact with a small set of neighbor agents in a local area. Agent neighborhoods are defined by how agents are connected, the agent interaction topology. Cellular automata represent agent neighborhoods by using a grid in which the agents exist in the cells, one agent per cell, or as the nodes of the lattice of the grid. The cells immediately surrounding an agent comprise the agent’s neighborhood and the agents that reside in the neighborhood cells comprise the neighbors. Many agent-based models have been based on this cellular automata spatial representation. The transition from a cellular automata, such as the game of Life, to an agent-based model is accomplished by allowing agents to be distinct from the cells on which they reside and allowing the agents to move from cell to cell across the grid. Agents move according to the dictates of their behaviors, interacting with other agents that happen to be in their local neighborhoods along the way.

Agent interaction topologies have been extended beyond cellular automata to include networks, either pre-

defined and static as in the case of autocatalytic chemical networks, or endogenous and dynamic, according to the results of agent interactions that occur in the model. Networks allow an agent’s neighborhood to be defined more generally and flexibly, and in the case of social agents, more accurately describe social agents’ interaction patterns. In addition to cellular automata grids and networks, agent interaction topologies have also been extended across a variety of domains. In summary, agent interaction topologies include:

• Cellular automata grids (agents are cells or are within in cells) or lattices (agents are grid points),
• Networks, in which agents of vertices and agent relationships are edges,
• Continuous space, in one, two or three dimensions;
• Aspatial random interactions, in which pairs of agents are randomly selected; and
• Geographical Information Systems (GIS), in which agents move over geographically-defined patches, relaxing the one-agent per cell restriction.

## 数学代考|计算复杂性理论代写computational complexity theory代考|Social Agent-Based Modeling

Early social agent-based models were based on ALife’s cellular automata approach. In applications of agent-based modeling to social processes, agents represent people or groups of people, and agent relationships represent processes of social interaction [33].

Social Agents Sakoda [61] formulated one of the first social agent-based models, the Checkerboard Model, which had some of the key features of a cellular automaton. Following a similar approach, Schelling developed a model of housing segregation in which agents represent homeowners and neighbors, and agent interactions represent agents’ perceptions of their neighbors [62]. Schelling studied housing segregation patterns and posed the question of whether it is it possible to get highly segregated settlement patterns even if most individuals are, in fact, “colorblind? The Schelling model demonstrated that segregated housing areas can develop spontaneously in the sense that system-level patterns can emerge that are not necessarily implied by or consistent with the objectives of the individual agents (Fig. 6). In the model, agents operated according to a fixed set of rules and were not adaptive.

Identifying the social interaction mechanisms for how cooperative behavior emerges among individuals and groups has been addressed using agent-based modeling and evolutionary game theory. Evolutionary game theory accounts for how the repeated interactions of players in a game-theoretic framework affect the development and evolution of the players’ strategies. Axelrod showed, using a cellular automata approach, in which agents on the grid employed a variety of different strategies, that a simple TitFor-Tat strategy of reciprocal behavior toward individuals is enough to establish sustainable cooperative behavior $[4,5]$. In addition, Axelrod investigated strategies that

were self-sustaining and robust in that they reduced the possibility of invasion by agents having other strategies.
Epstein and Axtell introduced the notion of an external environment that agents interact with in addition to other agents. In their groundbreaking Sugarscape model of artificial societies, agents interacted with their environment depending on their location in the grid [26]. This allowed agents to access environmental variables, extract resources, etc., based on location. In numerous computational experiments, Sugarscape agents emerged with a variety of characteristics and behaviors, highly suggestive of a realistic, although rudimentary and abstract, society (Fig. 7). They observed emergent processes that they interpreted as death, disease, trade, wealth, sex and reproduction, culture, conflict and war, as well as externalities such as pollution. As agents interacted with their neighbors as they moved around the grid, the interactions resulted in a contact network, that is, a network consisting of nodes and links. The nodes are agents and the links indicate the agents that have been neighbors at some point in the course of their movements over the grid. Contact networks were the basis for studying contagion and epidemics in the Sugarscape model. Understanding the agent rules that govern how networks are structured and grow, how quickly information is communicated through networks, and the kinds of relationships that networks embody are important aspects of modeling agents.

## 数学代考|计算复杂性理论代写computational complexity theory代考|ALife Summary

• 种群：考虑生物体或个体的种群。人口可能是多样化的，个人可能会在时间和空间上改变他们的特征、行为和积累的资源。
• 交互：交互需要感知个人的直接区域或邻域。一个有机体可以简单地“意识到”它附近的其他有机体，或者它可能与它们有更丰富的相互作用。个人还在其直接区域中与其（非代理）环境进行交互。这一要求将空间方面引入了问题，因为生物必须通过时间和空间来协商寻找资源。
• 维持和更新：维持和更新需要获取资源。有机体需要感知、发现、摄取和代谢资源，或将营养物作为能量来源，以加工成其他形式的营养物。资源可以由环境提供，即在代理本身之外，或由其他代理提供。维持的需要导致生物之间对资源的竞争。如果证明是一种更有效的生存策略，竞争也可能是合作和更复杂的新兴社会结构的前兆。
• 自我繁殖和替换：生物通过遵循至少部分嵌入自身的指令并与环境和其他代理相互作用进行繁殖。将性状传递给下一代意味着对性状传递的要求。特征传递需要以简化形式编码有机体的特征，即包含少于代表整个有机体的全部信息的形式。它还需要一个将有机体的特征转化为新有机体的一组可行的新特征的过程。突变和交叉算子进入这样的过程。器官-

## 数学代考|计算复杂性理论代写computational complexity theory代考|Agent-Based Modeling Topologies

• 元胞自动机网格（代理是细胞或在细胞内）或格子（代理是网格点），
• 网络，其中顶点的代理和代理关系是边，
• 连续空间，一维、二维或三维；
• 空间随机交互，其中随机选择代理对；和
• 地理信息系统 (GIS)，其中代理在地理定义的块上移动，放宽了每个单元一个代理的限制。

## 数学代考|计算复杂性理论代写computational complexity theory代考|Social Agent-Based Modeling

Epstein 和 Axtell 引入了代理与其他代理交互的外部环境的概念。在他们开创性的人工社会 Sugarscape 模型中，代理根据其在网格中的位置与环境进行交互 [26]。这允许代理根据位置访问环境变量、提取资源等。在众多的计算实验中，Sugarscape 代理出现了具有各种特征和行为的各种特征和行为，高度暗示了一个现实的社会，尽管是基本的和抽象的社会（图 7）。他们观察到了被他们解释为死亡、疾病、贸易、财富、性和繁殖、文化、冲突和战争的紧急过程，以及污染等外部因素。当代理在网格周围移动时与他们的邻居互动时，这些交互产生了一个联系网络，即一个由节点和链接组成的网络。节点是代理，链接指示代理在其在网格上的移动过程中的某个时间点是邻居。联系网络是在 Sugarscape 模型中研究传染和流行病的基础。了解管理网络如何构建和增长的代理规则、信息通过网络传递的速度以及网络体现的关系类型是建模代理的重要方面。联系网络是在 Sugarscape 模型中研究传染和流行病的基础。了解控制网络如何构建和增长的代理规则、信息通过网络传递的速度以及网络体现的关系类型是建模代理的重要方面。联系网络是在 Sugarscape 模型中研究传染和流行病的基础。了解控制网络如何构建和增长的代理规则、信息通过网络传递的速度以及网络体现的关系类型是建模代理的重要方面。

## 有限元方法代写

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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。