基于主体的模型

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An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

个体为本模型模型是一类计算模型，用于模拟自治主体(个人或集体实体，如组织或团体)的行为和相互作用，以评估它们对整个系统的影响。它结合了博弈论、复杂系统、涌现、计算社会学、多智能体系统和进化规划等要素。蒙特卡罗方法用于引入随机性。特别是在生态学中，基于个体的模型也被称为基于个体的模型(ibm) ，而基于 ibm 的个体可能比基于 ABMs 的完全自主的个体更简单。综述了基于个体的模型、基于主体的模型和多主体系统的最新文献，表明基于主体的模型被用于非计算相关的科学领域，包括生物学、生态学和社会科学。基于 agent 的建模与多 agent 系统或多 agent 模拟的概念有关，但又有所不同，因为 ABM 的目标是寻求对遵守简单规则的 agent 的集体行为的解释性洞察，尤其是在自然系统中，而不是设计 agent 或解决具体的实际或工程问题。

Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level).

Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level).

基于 agent 的模型是一种微尺度模型，它模拟多个 agent 同时进行的操作和相互作用，试图重建和预测复杂现象的出现。这个过程是一个涌现的过程，有些人将其表述为“整体大于各部分之和”。换句话说，较高层次的系统属性来自于较低层次的子系统之间的交互。或者，宏观尺度的状态变化来自微观尺度的主体行为。或者，简单的行为(意味着规则由代理遵循)生成复杂的行为(意味着整个系统级别的状态变化)。

Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

个体行为主体通常被描述为有限理性的，被认为是按照他们认为是自己的利益行事，如再生产、经济利益或社会地位，使用启发式或简单的决策规则。Abm 代理人可能会经历“学习”、适应和繁殖。

Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.

Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.

大多数基于代理的模型由以下几个部分组成: (1)在不同尺度上指定的众多代理(通常称为代理粒度) ; (2)决策启发法; (3)学习规则或自适应过程; (4)交互拓扑; (5)环境。Abm 通常是以计算机模拟的形式实现的，可以是定制的软件，也可以通过 ABM 工具包，然后这个软件可以用来测试个人行为的改变将如何影响系统的整体行为。

History

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.

基于 agent 的建模思想是在20世纪40年代后期作为一个相对简单的概念发展起来的。由于它需要计算密集型的过程，直到20世纪90年代才广泛使用。

Early developments

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend Stanislaw Ulam, also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed cellular automata.

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend Stanislaw Ulam, also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed cellular automata.

个体为本模型的历史可以追溯到冯 · 诺依曼机器，一种理论上能够复制的机器。冯 · 诺依曼提出的装置将按照精确详细的指令制作自己的复制品。冯 · 诺依曼的朋友、数学家斯坦尼斯拉夫 · 乌拉姆建立了这个概念; 乌拉姆建议这台机器应该建立在纸上，作为一个网格上的细胞集合。这个想法激起了冯 · 诺依曼的兴趣，他提出了这个想法ーー创造了第一个后来被称为细胞自动机的装置。

Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.

Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.

另一个进步是由数学家约翰 · 康威提出的。他构建了著名的生命游戏。与冯 · 诺依曼的机器不同，康威的《生命的游戏》以二维跳棋盘的形式在虚拟世界中按照简单的规则操作。

The Simula programming language, developed in the mid 1960's and widely implemented by the early 1970's, was the first framework for automating step-by-step agent simulations.

The Simula programming language, developed in the mid 1960's and widely implemented by the early 1970's, was the first framework for automating step-by-step agent simulations.

Simula 编程语言开发于20世纪60年代中期，在20世纪70年代早期得到广泛实现，是自动化分步代理仿真的第一个框架。

1970s and 1980s: the first models

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.

最早的基于主体的概念模型之一是托马斯·克罗姆比·谢林的分离模型，这在他1971年的论文《分离的动态模型》中得到了讨论。虽然谢林最初使用的是硬币和图纸，而不是计算机，但他的模型体现了基于主体的模型的基本概念，即自主主体在共享环境中相互作用，具有可观察的聚合、突现的结果。

In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture.

In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture.

在20世纪80年代早期，罗伯特 · 阿克塞尔罗德主持了一场囚徒困境策略锦标赛，并让他们以基于主体的方式进行互动，以决定谁是赢家。阿克塞尔罗德继续在政治科学领域发展了许多其他基于主体的模型，这些模型研究从种族中心主义到文化传播等现象。

By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as artificial life, a term coined by Christopher Langton.

By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as artificial life, a term coined by Christopher Langton.

到20世纪80年代末，克雷格 · 雷诺兹关于群集模型的工作促成了一些包含社会特征的第一批基于生物剂的模型的发展。他试图为活跃的生物制剂---- 即人工生命---- 的现实建立模型，人工生命是克里斯托弗·兰顿创造的一个术语。

The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs.

The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs.

“代理”这个词的第一次使用以及目前使用的定义很难追踪。约翰•霍兰德(John Holland)和约翰• h •米勒(John h. Miller)1991年发表的论文《经济理论中的人工适应性代理人》(Artificial Adaptive Agents in Economic Theory)似乎就是一个候选人，这篇论文是基于他们早些时候的一次会议报告。

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).

与此同时，在20世纪80年代，社会科学家、数学家、运算研究人员以及来自其他学科的分散人员发展了计算和数学组织理论(CMOT)。这个领域成长为管理科学研究所(TIMS)及其姊妹学会美国运筹学会(ORSA)的一个特殊兴趣小组。

1990s: expansion

With the appearance of StarLogo in 1990, Swarm and NetLogo in the mid-1990s and RePast and AnyLogic in 2000, or GAMA in 2007 as well as some custom-designed code, modelling software became widely available and the range of domains that ABM was applied to, grew. Bonabeau (2002) is a good survey of the potential of agent-based modeling as of the time.

With the appearance of StarLogo in 1990, Swarm and NetLogo in the mid-1990s and RePast and AnyLogic in 2000, or GAMA in 2007 as well as some custom-designed code, modelling software became widely available and the range of domains that ABM was applied to, grew. Bonabeau (2002) is a good survey of the potential of agent-based modeling as of the time.

随着1990年 StarLogo、20世纪90年代中期的 Swarm 和 NetLogo、2000年的 RePast 和 AnyLogic、2007年的 GAMA 以及一些定制代码的出现，建模软件得到了广泛应用，ABM 应用的领域范围不断扩大。Bonabeau (2002年)是一个很好的调查潜力的基于代理建模的时间。

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by

20世纪90年代尤其值得注意的是反弹道导弹在社会科学的扩展，其中一个值得注意的努力是大规模的反弹道导弹，糖景，开发的

Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture. Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM, to explore the co-evolution of social networks and culture.

Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture. Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM, to explore the co-evolution of social networks and culture.

和 Robert Axtell 来模拟和探索诸如季节性迁徙、污染、有性生殖、疾病甚至文化的战斗和传播等社会现象的作用。20世纪90年代其他值得注意的发展包括卡内基梅隆大学的 Kathleen Carley ABM，探索社会网络和文化的共同进化。

During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM).

During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM).

在20世纪90年代，奈杰尔 · 吉尔伯特出版了第一本《社会模拟: 社会科学家的模拟》教科书(1999年) ，并从社会科学的角度创办了一本杂志: 《人工社会与社会模拟杂志》(JASSS)。除了 JASSS 之外，任何学科的基于代理的模型都在 SpringerOpen 杂志的复杂适应性系统建模(CASM)的范围之内。

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history, and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history, and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.

整个20世纪90年代中期，ABM 的社会科学主线开始关注诸如设计有效的团队、理解组织有效性所需的沟通以及社会网络的行为等问题。Cmot 后来更名为社会与组织系统计算分析(CASOS) ，它吸收了越来越多的基于主体的建模方法。萨缪尔森(2000)是一个很好的早期历史概述，萨缪尔森(2005)和萨缪尔森和马卡尔(2006)跟踪更近的发展。

In the late 1990s, the merger of TIMS and ORSA to form INFORMS, and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining National Science Foundation funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the University of Chicago and Argonne National Laboratory, and then by Michael Prietula of Emory University. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006. The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.

In the late 1990s, the merger of TIMS and ORSA to form INFORMS, and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining National Science Foundation funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the University of Chicago and Argonne National Laboratory, and then by Michael Prietula of Emory University. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006. The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.

20世纪90年代后期，TIMS 和 ORSA 合并组成了 infirms，infirms 将每年两次会议改为一次会议，这有助于促使 CMOT 集团成立一个单独的社团，即北美计算社会和组织科学协会。凯瑟琳 · 卡利是一个主要贡献者，特别是社交网络模型，为年度会议获得了国家科学基金会的资助，并担任 NAACSOS 的第一任主席。她的继任者是芝加哥大学和阿贡国家实验室的 David Sallach，然后是艾默理大学的 Michael Prietula。大约在 NAACSOS 成立的同时，欧洲社会模拟协会(ESSA)和太平洋亚洲社会系统科学基于 agent 的方法协会(PAAA)也成立了，这两个协会是 NAACSOS 的同行。截至2013年，这三个组织开展了国际合作。第一届世界社会模拟大会于2006年8月在他们的共同赞助下在日本京都举行。第二届世界大会于2008年7月在华盛顿特区弗吉尼亚北部郊区举行，乔治梅森大学在地方安排中发挥了主导作用。

2000s and later

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation. Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making. Since 2001, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field.

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation. Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making. Since 2001, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field.

最近，Ron Sun 开发了基于人类认知模型的基于 agent 的模拟方法，称为认知社会模拟。加州大学洛杉矶分校的 Bill McKelvey，Suzanne Lohmann，Dario Nardi，Dwight Read 和其他人也在组织行为学和决策方面做出了重大贡献。自2001年以来，加州大学洛杉矶分校在加利福尼亚州箭头湖安排了一次会议，该会议已成为该领域从业者的另一个主要聚集点。

Theory

Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity.

Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity.

大多数计算模型研究描述的是处于平衡或在平衡之间移动的系统。然而，基于 agent 的建模，使用简单的规则，可以导致不同类型的复杂和有趣的行为。基于主体的模型的三个核心思想是作为对象的主体、涌现和复杂性。

Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are

Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are

基于 agent 的模型由动态交互的基于规则的 agent 组成。它们相互作用的系统可以创造出现实世界一样的复杂性。通常情况下，经纪人都是这样的

situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water).

situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water).

位于空间和时间中，居住在网络中或格子状的社区中。代理人的位置和他们的响应行为以算法形式编码在计算机程序中。在某些情况下，尽管不总是这样，代理人可能被认为是聪明和有目的的。在生态反弹道导弹(在生态学中通常称为“基于个人的模型”)中，行动者可能，例如，是森林中的树木，不会被认为是智能的，尽管他们可能在优化获得资源(如水)的意义上是“有目的的”。

The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.

The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.

建模过程最好用归纳法来描述。建模者做出那些假设，认为最相关的情况在手，然后观察现象出现的代理人的相互作用。有时候这个结果就是一个平衡。有时这是一种自然发生的模式。然而，有时这是一种莫名其妙的混乱。

In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, power-law distributions in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.

In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, power-law distributions in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.

在某些方面，基于 agent 的模型补充了传统的分析方法。当分析方法使人们能够描述系统的平衡时，基于主体的模型允许生成这些平衡的可能性。这种生成性贡献可能是基于主体的建模的最主流的潜在好处。基于代理的模型可以解释高层次模式的出现ーー恐怖组织和互联网的网络结构、交通堵塞、战争和股市崩盘规模中的权力法则分布，以及尽管有宽容的人群但仍然存在的社会隔离。基于 agent 的模型还可以用来识别杠杆点，即干预措施产生极端后果的时刻，并区分不同类型的路径依赖。

Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.

Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.

许多模型没有关注稳定状态，而是考虑系统的鲁棒性ーー即复杂系统适应内部和外部压力以维持其功能的方式。控制这种复杂性的任务需要考虑代理人自身ーー他们的多样性、连通性和相互作用的程度。

Framework

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

最近关于复杂适应系统建模与模拟模型的工作已经证明了需要结合基于代理和基于复杂网络的模型。描述一个由四个层次的复杂适应系统开发模型组成的框架，使用几个多学科案例研究实例进行描述:

1. Complex Network Modeling Level for developing models using interaction data of various system components.

Complex Network Modeling Level for developing models using interaction data of various system components.

复杂网络建模级，用于使用各种系统组件的交互数据开发模型。

1. Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.

Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.

基于探索性 agent 的建模级开发基于 agent 的模型，评估进一步研究的可行性。例如:。对于开发概念验证模型非常有用，例如用于资助应用，而不需要研究人员进行广泛的学习曲线。

1. Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.

Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.

基于描述代理的建模(DREAM) ，通过使用模板和复杂的网络模型来开发基于代理的模型描述。建立梦想模型允许跨科学分支的模型比较。

1. Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.

Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.

基于验证代理的建模使用虚拟覆盖多代理系统(VOMAS) ，以形式化的方式开发验证和验证的模型。

Other methods of describing agent-based models include code templates and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.

Other methods of describing agent-based models include code templates and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.

描述基于代理的模型的其他方法包括代码模板和基于文本的方法，如 ODD (概述、设计概念和设计详细信息)协议。

The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour.

The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour.

在基于主体的建模和仿真工作中，主体所处的宏观和微观环境的作用也正在成为一个重要因素。简单的环境提供简单的代理，但复杂的环境产生多样化的行为。

Applications

In biology

Main article: Agent-based model in biology

Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics, and the threat of biowarfare, biological applications including population dynamics, stochastic gene expression,, plant-animal interactions, vegetation ecology, landscape diversity, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation,

Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics, and the threat of biowarfare, biological applications including population dynamics, stochastic gene expression,, plant-animal interactions, vegetation ecology, landscape diversity, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation,

基于 agent 的建模在生物学中得到了广泛的应用，包括对流行病传播和生物战威胁的分析，生物学应用，包括族群动态，随机基因表达，，植物-动物相互作用，植被生态学，景观多样性，古代文明的生长和衰退，人种中心行为的进化，强迫迁移 / 迁移，语言选择动力学，认知建模，以及生物医学应用，包括3 d 乳腺组织形成 / 形态发生，电离辐射对乳腺干细胞亚群动力学的影响，炎症,

and the human immune system. Agent-based models have also been used for developing decision support systems such as for breast cancer. Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori. Military applications have also been evaluated. Moreover, agent-based models have been recently employed to study molecular-level biological systems.

</ref> Agent-based models have also been used for developing decision support systems such as for breast cancer. Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori. Military applications have also been evaluated. Moreover, agent-based models have been recently employed to study molecular-level biological systems.

基于 agent 的模型也被用于开发决策支持系统，例如乳腺癌。基于代理人的模型越来越多地被用于早期和临床前研究中的药理系统模型，以帮助药物开发，并获得对生物系统的了解，而这在先验上是不可能的。军事应用也得到了评估。此外，基于主体的模型最近已被用于研究分子水平的生物系统。

In business, technology and network theory

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include marketing, organizational behaviour and cognition, team working, supply chain optimization and logistics, modeling of consumer behavior, including word of mouth, social network effects, distributed computing, workforce management, and portfolio management. They have also been used to analyze traffic congestion.

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include marketing, organizational behaviour and cognition, team working, supply chain optimization and logistics, modeling of consumer behavior, including word of mouth, social network effects, distributed computing, workforce management, and portfolio management. They have also been used to analyze traffic congestion.

自20世纪90年代中期以来，基于 agent 的模型被用于解决各种商业和技术问题。应用的例子包括市场营销，组织行为和认知，团队合作，供应链优化和物流，消费者行为建模，包括口碑，社会网络效应，分布式计算，劳动力管理和投资组合管理。他们也被用来分析交通堵塞。

Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences). In addition, ABMs have been used to simulate information delivery in ambient assisted environments. A November 2016 article in arXiv analyzed an agent based simulation of posts spread in the Facebook online social network. In the domain of peer-to-peer, ad-hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown. The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated.

Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences). In addition, ABMs have been used to simulate information delivery in ambient assisted environments. A November 2016 article in arXiv analyzed an agent based simulation of posts spread in the Facebook online social network. In the domain of peer-to-peer, ad-hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown. The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated.

近年来，基于 agent 的建模和仿真已经被应用到各个领域，例如计算机科学领域研究人员对出版场所的影响的研究(期刊与会议)。此外，ABMs 已经被用来模拟环境辅助环境中的信息传递。2016年11月发表在 arXiv 上的一篇文章分析了一个基于代理的模拟 Facebook 在线社交网络上的帖子。在对等网络、 ad-hoc 网络和其他自组织复杂网络领域，基于 agent 的建模与模拟服务已经被证明是有用的。基于计算机科学的无线传感器网络和基于代理的仿真形式规范框架的使用最近已经得到了证实。

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.

基于 Agent 的进化搜索算法是解决复杂优化问题的一个新的研究课题。

In economics and social sciences

Main articles: Agent-based computational economics and Agent-based social simulation

Prior to, and in the wake of the financial crisis, interest has grown in ABMs as possible tools for economic analysis. ABMs do not assume the economy can achieve equilibrium and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "bottom-up" approach and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-linear (disproportionate) responses to proportionally small changes. A July 2010 article in The Economist looked at ABMs as alternatives to DSGE models. The journal Nature also encouraged agent-based modeling with an editorial that suggested ABMs can do a better job of representing financial markets and other economic complexities than standard models along with an essay by J. Doyne Farmer and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations.模板:Sfn Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models.模板:Sfn By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index.

Prior to, and in the wake of the financial crisis, interest has grown in ABMs as possible tools for economic analysis. ABMs do not assume the economy can achieve equilibrium and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "bottom-up" approach and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-linear (disproportionate) responses to proportionally small changes. A July 2010 article in The Economist looked at ABMs as alternatives to DSGE models. along with an essay by J. Doyne Farmer and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations. Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models. By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index.

在金融危机之前和之后，人们对作为可能的经济分析工具的 abm 的兴趣日益增长。反弹道导弹并不假设经济能够达到平衡，“代表性代理人”被具有多样化、动态和相互依赖行为的代理人所取代，其中包括羊群行为。反弹道导弹采取”自下而上”的方法，可以产生极其复杂和动荡的模拟经济。反弹道导弹可以代表不稳定系统的崩溃和繁荣，它们对相对小的变化作出非线性(不成比例)的反应。2010年7月《经济学人》的一篇文章将 abm 看作 DSGE 模型的替代品。与此同时，j · 多恩 · 法默和邓肯 · 福利的一篇文章认为，ABMs 可以同时满足凯恩斯代表复杂经济的愿望和罗伯特 · 卢卡斯建立基于微观基础的模型的愿望。法默和福利指出，已经取得了进展，使用反弹道导弹模型的部分经济模型，但主张创建一个非常大的模式，包括低水平的模型。通过建模一个复杂的系统的分析师基于三个不同的行为剖面-模仿，反模仿，无关金融市场被模拟到高准确度。结果表明，网络形态与股票市场指数之间存在相关性。

Since the beginning of the 21st century ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment. There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network.

Since the beginning of the 21st century ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment. There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network.

自21世纪初以来，基于动态模型的城市行人交通流模拟技术在建筑和城市规划中得到了广泛的应用。利用反弹道导弹识别对社会经济网络的系统性影响的能力，对基础设施投资影响进行社会经济分析的领域也在不断扩大。

Organizational ABM: agent-directed simulation

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems." Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems." Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).

主体导向模拟(ADS)隐喻区分了两类，即“主体导向的系统”和“主体导向的系统”代理系统(有时称为代理系统)是用于工程、人类和社会动态、军事应用等领域的系统执行代理。系统的代理分为两个子类别。代理支持的系统处理代理作为支持设施的使用，使计算机协助解决问题或增强认知能力。基于 agent 的系统关注于在系统评估(系统研究和分析)中使用 agent 来生成模型行为。

Implementation

Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size, scalability restrictions may hinder model validation. Such limitations have mainly been addressed using distributed computing, with frameworks such as Repast HPC specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization, as well as deployment complexity, remain potential obstacles for their widespread adoption.

Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size, scalability restrictions may hinder model validation. Such limitations have mainly been addressed using distributed computing, specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization, as well as deployment complexity, remain potential obstacles for their widespread adoption.

许多 ABM 框架是为串行 von-Neumann 计算机体系结构设计的，这限制了实现模型的速度和可扩展性。由于大规模 ABMs 中的突发行为与种群大小有关，可扩展性限制可能会妨碍模型验证。这些限制主要是通过分布式计算来解决的，特别是针对这些类型的实现。虽然这些方法可以很好地映射到集群和超级计算机架构，但是与通信和同步相关的问题，以及部署的复杂性，仍然是广泛采用这些方法的潜在障碍。

A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation. The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.

A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation. The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.

最近的一个发展是在图形处理单元 gpu 上使用数据并行算法进行 ABM 仿真。极高的内存带宽，加上多处理器 gpu 超强的数字运算能力，已经能够模拟数千万个帧率的代理程序。

Verification and validation

Verification and validation (V&V) of simulation models is extremely important. Verification involves the model being debugged to ensure it works correctly, whereas validation ensures that the right model has been built. Face validation, sensitivity analysis, calibration and statistical validation have also been demonstrated. A discrete-event simulation framework approach for the validation of agent-based systems has been proposed. A comprehensive resource on empirical validation of agent-based models can be found here.

Verification and validation (V&V) of simulation models is extremely important. Verification involves the model being debugged to ensure it works correctly, whereas validation ensures that the right model has been built. Face validation, sensitivity analysis, calibration and statistical validation have also been demonstrated. A discrete-event simulation framework approach for the validation of agent-based systems has been proposed. A comprehensive resource on empirical validation of agent-based models can be found here.

验证及确认的模拟模型是非常重要的。验证包括调试模型以确保其正确工作，而验证则确保建立了正确的模型。人脸验证、敏感度分析、校准和统计验证也得到了验证。提出了一种基于离散事件仿真的系统验证框架方法。这里可以找到一个关于基于主体的模型的经验验证的综合资源。

As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system), a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. The agents in the multi-agent system are able to gather data by generation of logs as well as provide run-time validation and verification support by watch agents and also agents to check any violation of invariants at run-time. These are set by the Simulation Specialist with help from the SME (subject-matter expert). Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model.

As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system), a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. The agents in the multi-agent system are able to gather data by generation of logs as well as provide run-time validation and verification support by watch agents and also agents to check any violation of invariants at run-time. These are set by the Simulation Specialist with help from the SME (subject-matter expert). Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model.

作为 v & v 技术的一个例子，考虑一下基于软件工程的方法，VOMAS (虚拟覆盖多智能体系统) ，在个体为本模型旁边开发了一个虚拟覆盖程序。多智能体系统中的代理可以通过生成日志来收集数据，同时还可以通过观察代理和代理提供运行时验证和验证支持，以检查运行时是否存在任何违反不变量的情况。这些是由模拟专家在中小企业(主题专家)的帮助下设置的。等人。还提供了一个使用 VOMAS 模拟森林火灾模拟模型的验证及确认例子。

VOMAS provides a formal way of validation and verification. To develop a VOMAS, one must design VOMAS agents along with the agents in the actual simulation, preferably from the start. In essence, by the time the simulation model is complete, one can essentially consider it to be one model containing two models:

VOMAS provides a formal way of validation and verification. To develop a VOMAS, one must design VOMAS agents along with the agents in the actual simulation, preferably from the start. In essence, by the time the simulation model is complete, one can essentially consider it to be one model containing two models:

为确认和验证提供了一种正式的方法。要开发一种呕吐物，必须在实际的模拟中设计呕吐物药剂，最好从一开始就设计。实质上，等到仿真模型完成时，人们基本上可以认为它是一个包含两个模型的模型:

1. An agent-based model of the intended system

An agent-based model of the intended system

预期系统的个体为本模型

1. An agent-based model of the VOMAS

An agent-based model of the VOMAS

呕吐物的个体为本模型

Unlike all previous work on verification and validation, VOMAS agents ensure that the simulations are validated in-simulation i.e. even during execution. In case of any exceptional situations, which are programmed on the directive of the Simulation Specialist (SS), the VOMAS agents can report them. In addition, the VOMAS agents can be used to log key events for the sake of debugging and subsequent analysis of simulations. In other words, VOMAS allows for a flexible use of any given technique for the sake of verification and validation of an agent-based model in any domain.

Unlike all previous work on verification and validation, VOMAS agents ensure that the simulations are validated in-simulation i.e. even during execution. In case of any exceptional situations, which are programmed on the directive of the Simulation Specialist (SS), the VOMAS agents can report them. In addition, the VOMAS agents can be used to log key events for the sake of debugging and subsequent analysis of simulations. In other words, VOMAS allows for a flexible use of any given technique for the sake of verification and validation of an agent-based model in any domain.

与之前所有关于验证及确认的工作不同，VOMAS 代理确保模拟是经过仿真验证的。甚至在执行死刑的时候。在任何异常情况下，在仿真专家(SS)的指令下编程，VOMAS 代理可以报告他们。此外，还可以使用 VOMAS 代理记录关键事件，以便调试和随后的仿真分析。换句话说，为了在任何领域的验证及确认 / 个体为本模型，VOMAS 允许灵活地使用任何给定的技术。

Details of validated agent-based modeling using VOMAS along with several case studies are given in. This thesis also gives details of "exploratory agent-based modeling", "descriptive agent-based modeling" and "validated agent-based modeling", using several worked case study examples.

Details of validated agent-based modeling using VOMAS along with several case studies are given in. This thesis also gives details of "exploratory agent-based modeling", "descriptive agent-based modeling" and "validated agent-based modeling", using several worked case study examples.

中给出了使用 VOMAS 进行基于验证代理的建模的详细信息以及一些案例研究。本文还详细介绍了“基于探索性 agent 的建模”、“基于描述 agent 的建模”和“基于验证 agent 的建模” ，并给出了几个工作实例。

Complex systems modelling

Mathematical models of complex systems are of three types: black-box (phenomenological), white-box (mechanistic, based on the first principles) and grey-box (mixtures of phenomenological and mechanistic models).

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In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden.

Mathematical models for complex systems

Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has 'transparent walls' and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches.

Logical deterministic individual-based cellular automata model of single species population growth

Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study. The white-box modeling represents an automatic hyper-logical inference from the first principles because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic axiomatic system of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.

}}</ref> In black-box models, the individual-based (mechanistic) mechanisms of a complex dynamic system remain hidden. Mathematical models for complex systems Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model. A white-box model of complex dynamic system has 'transparent walls' and directly shows underlying mechanisms. All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution. In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches. Logical deterministic individual-based cellular automata model of single species population growth Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model. The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study. The white-box modeling represents an automatic hyper-logical inference from the first principles because it is completely based on the deterministic logic and axiomatic theory of the subject. The purpose of the white-box modeling is to derive from the basic axioms a more detailed, more concrete mechanistic knowledge about the dynamics of the object under study. The necessity to formulate an intrinsic axiomatic system of the subject before creating its white-box model distinguishes the cellular automata models of white-box type from cellular automata models based on arbitrary logical rules. If cellular automata rules have not been formulated from the first principles of the subject, then such a model may have a weak relevance to the real problem.

} / ref 在黑盒模型中，复杂动态系统中基于个体的(机制)机制仍然是隐藏的。复杂系统的数学模型黑箱模型是完全非机械的。它们是现象学的，忽略了复杂系统的组成和内部结构。我们不能研究这样一个非透明模型的子系统之间的相互作用。复杂动态系统的白盒子模型具有“透明墙” ，直接揭示了内在机制。一个动态系统的微观、中观和宏观层面的所有事件在其白盒模型演化的所有阶段都是直接可见的。在大多数情况下，数学模型使用沉重的黑箱数学方法，不能产生复杂动态系统的机械模型。灰盒模型是中间的，结合了黑盒和白盒方法。建立复杂系统的白盒子模型，关系到建模主体先验知识的必要性问题。确定性逻辑元胞自动机是白盒模型存在的充分必要条件。白盒模型的第二个必要前提是被研究对象的物理本体的存在。白盒建模代表了从第一原则自动超逻辑推理，因为它完全基于主体的确定性逻辑和公理系统。白盒建模的目的是从基本公理推导出更详细、更具体的关于被研究对象动力学的机械知识。在创建白盒子模型之前，必须确定主体的内在公理系统，这使得白盒子类型的细胞自动机模型区别于基于任意逻辑规则的细胞自动机模型。如果细胞自动机规则没有从主题的第一原则制定，那么这样一个模型可能有一个弱相关性的实际问题。

Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource

Logical deterministic individual-based cellular automata model of interspecific competition for a single limited resource

对于单个有限资源，基于逻辑确定性个体的种间竞争元胞自动机模型

See also

• Agent-based computational economics

• Agent-based model in biology

• Agent-based social simulation (ABSS)

• Artificial society

• Boids

• Comparison of agent-based modeling software

• Complex system

• Complex adaptive system

• Computational sociology

• Conway's Game of Life

• Dynamic network analysis

• Emergence

• Evolutionary algorithm

• Flocking

• Internet bot

• Kinetic exchange models of markets

• Multi-agent system

• Simulated reality

• Social complexity

• Social simulation

• Sociophysics

• Software agent

• Swarming behaviour

• Web-based simulation

• TOTREP

References

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External links

Articles/general Information

• Agent-based models of social networks, java applets.

• On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences

• Introduction to Agent-based Modeling and Simulation. Argonne National Laboratory, November 29, 2006.

• Agent-based models in Ecology – Using computer models as theoretical tools to analyze complex ecological systems模板:Dead link

• Network for Computational Modeling in the Social and Ecological Sciences' Agent Based Modeling FAQ

• Multiagent Information Systems – Article on the convergence of SOA, BPM and Multi-Agent Technology in the domain of the Enterprise Information Systems. Jose Manuel Gomez Alvarez, Artificial Intelligence, Technical University of Madrid – 2006

• Artificial Life Framework

• Article providing methodology for moving real world human behaviors into a simulation model where agent behaviors are represented

• Agent-based Modeling Resources, an information hub for modelers, methods, and philosophy for agent-based modeling

• An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications, Tommi A. Vuorenmaa (Valo Research and Trading), Liang Wang (University of Helsinki - Department of Computer Science), October, 2013

Simulation models

• Multi-agent Meeting Scheduling System Model by Qasim Siddique

• Multi-firm market simulation by Valentino Piana

模板:Industrial ecology

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Category:Models of computation

类别: 计算模型

Category:Complex systems theory

范畴: 复杂系统理论

Category:Scientific modeling

类别: 科学建模

Category:Multi-agent systems

类别: 多代理系统

Category:Methods in sociology

范畴: 社会学方法

Category:Artificial life

类别: 人工生命

Category:Simulation

类别: 模拟

Category:Systems theory

范畴: 系统论

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