社会仿真

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Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology,[1] organizational behavior,[2] sociology, political science, economics, anthropology, geography, engineering,[2] archaeology and linguistics 模板:Harv.

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, others have modeled the emergence of norms using memes, or how social norms and emotions can regulate each other.

社会模拟是应用计算方法研究社会科学问题的一个研究领域。所探讨的问题包括计算法学、心理学、组织行为学、社会学、政治学、经济学、人类学、地理学、工程学等领域的问题。


Social simulation aims to cross the gap between the descriptive approach used in the social sciences and the formal approach used in the natural sciences, by moving the focus on the processes/mechanisms/behaviors that build the social reality.


In social simulation, computers support human reasoning activities by executing these mechanisms. This field explores the simulation of societies as complex non-linear systems, which are difficult to study with classical mathematical equation-based models. Robert Axelrod regards social simulation as a third way of doing science, differing from both the deductive and inductive approach; generating data that can be analysed inductively, but coming from a rigorously specified set of rules rather than from direct measurement of the real world. Thus, simulating a phenomenon is akin to generating it—constructing artificial societies. These ambitious aims have encountered several criticisms.


The social simulation approach to the social sciences is promoted and coordinated by three regional associations, ESSA for Europe, North America (reorganizing under the new CSSS name), and PAAA Pacific Asia.


History and development

Social simulation can refer to a general class of strategies for understanding social dynamics using computers to simulate social systems. Social simulation allows for a more systematic way of viewing the possibilities of outcomes.

社会模拟可以指的是一类通用的策略,用计算机模拟社会系统来理解社会动态。社会模拟允许一种更系统的方式来看待结果的可能性。


The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproducing itself. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then improved 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 devices later termed cellular automata.

There are four major types of social simulation:

社会模拟主要有四种类型:


System level simulation.

系统级模拟。

Another improvement was brought by mathematician, John Conway. He constructed the well-known Game of Life. Unlike the 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.

System level modeling.

系统级建模。


Agent-based simulation.

基于 agent 的模拟。

The birth of the agent-based model as a model for social systems was primarily brought about by a computer scientist, Craig Reynolds. He tried to model the reality of lively biological agents, known as the artificial life, a term coined by Christopher Langton.

Agent-based modeling.

基于 agent 的建模。


Joshua M. Epstein and Robert Axtell developed the first large scale agent model, the Sugarscape, to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, transmission of disease, and even culture.

A social simulation may fall within the rubric of computational sociology which is a recently developed branch of sociology that uses computation to analyze social phenomena. The basic premise of computational sociology is to take advantage of computer simulations in the construction of social theories. It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence.

社会模拟可能属于计算社会学社会学的范畴,这是一个最近发展起来的社会学分支,它利用计算机来分析社会现象。计算社会学的基本前提是在社会理论的构建中利用计算机模拟的优势。它涉及对社会行为主体的理解,这些行为主体之间的相互作用,以及这些相互作用对社会总量的影响。虽然社会科学的主题和方法不同于自然科学或计算机科学,但是当代社会模拟中使用的一些方法起源于物理学和人工智能等领域。


Kathleen M. Carley published "Computational Organizational Science and Organizational Engineering" defining the movement of simulation into

organizations, established a journal for social simulation applied to organizations and complex socio-technical systems: Computational and Mathematical Organization Theory, and was the founding president of the North American Association of Computational Social and Organizational Systems that morphed into the current CSSSA.

System Level Simulation (SLS) is the oldest level of social simulation. System level simulation looks at the situation as a whole. This theoretical outlook on social situations uses a wide range of information to determine what should happen to society and its members if certain variables are present. Therefore, with specific variables presented, society and its members should have a certain response to the new situation. Navigating through this theoretical simulation will allow researchers to develop educated ideas of what will happen under some specific variables.

系统级仿真(SLS)是最古老的社会仿真级别。系统级仿真将情况作为一个整体来看待。这种关于社会状况的理论观点使用广泛的信息来确定,如果存在某些变量,社会及其成员将会发生什么。因此,在提出具体变量的情况下,社会及其成员应该对新形势做出一定的反应。通过这个理论模拟,研究人员可以对某些特定变量下会发生什么有根据的想法。


Nigel Gilbert published with Klaus G. Troitzsch the first textbook on Social Simulation: Simulation for the Social Scientist (1999) and established its most relevant journal: the Journal of Artificial Societies and Social Simulation.

For example, if NASA were to conduct a system level simulation it would benefit the organization by providing a cost-effective research method to navigate through the simulation. This allows the researcher to steer through the virtual possibilities of the given simulation and develop safety procedures, and to produce proven facts about how a certain situation will play out.

例如,如果美国航天局进行一次系统级别的模拟,它将通过提供一种成本效益高的研究方法来导航通过模拟,从而使该组织受益。这使得研究人员能够通过给定的模拟和发展安全程序的虚拟可能性,并产生关于某种情况将如何发挥的证明事实。


More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation (see 模板:Harv)


Topics

System level modeling (SLM) aims to specifically predict (unlike system level simulation's generalization in prediction) and convey any number of actions, behaviors, or other theoretical possibilities of nearly any person, object, construct et cetera within a system using a large set of mathematical equations and computer programming in the form of models.

系统级建模(System level modeling,SLM)的目的是通过大量的数学方程和模型形式的计算机编程,具体地预测(不同于系统级模拟的预测泛化) ,并传达几乎任何人、对象、构造等系统内的任何数量的动作、行为或其他理论可能性。


Here are some sample topics that have been explored with social simulation:

A model is a representation of a specific thing ranging from objects and people to structures and products created through mathematical equations and are designed, using computers, in such a way that they are able to stand-in as the aforementioned things in a study. Models can be either simplistic or complex, depending on the need for either; however, models are intended to be simpler than what they are representing while remaining realistically similar in order to be used accurately. They are built using a collection of data that is translated into computing languages that allow them to represent the system in question. These models, much like simulations, are used to help us better understand specific roles and actions of different things so as to predict behavior and the like.

模型是一个具体事物的代表,范围从物体和人到通过数学方程创建的结构和产品,并使用计算机设计,使他们能够在研究中代替上述事物。模型可以是简单的,也可以是复杂的,这取决于两者的需要; 然而,模型的目的是比它们所表示的更简单,同时保持实际上的相似,以便准确地使用。它们是通过将数据集合转换成计算语言来构建的,计算语言允许它们表示所涉及的系统。这些模型,就像模拟一样,被用来帮助我们更好地理解不同事物的特定角色和行为,以便预测行为等等。


  • Social norms: Robert Axelrod has used simulations to investigate the foundation of morality;[3] others have modeled the emergence of norms using memes,[4] or how social norms and emotions can regulate each other.[5][6]
  • Reputation, for example by making agents with a model of reputation from Pierre Bourdieu (image, social esteem, and prestige) and observing their behavior in a virtual marketplace.[9]

Agent-based social simulation (ABSS) consists of modeling different societies after artificial agents, (varying on scale) and placing them in a computer simulated society to observe the behaviors of the agents. From this data it is possible to learn about the reactions of the artificial agents and translate them into the results of non-artificial agents and simulations. Three main fields in ABSS are agent-based computing, social science, and computer simulation.

基于智能体的社会模拟(Agent-based social simulation,ABSS)是将不同社会群体按照人工智能(artificial agent)模型(various on scale)进行建模,并将其置于计算机模拟社会中,以观察智能体的行为。从这些数据可以了解人工代理的反应,并将其转化为非人工代理和模拟的结果。ABSS 的3个主要领域是基于代理的计算、社会科学和计算机模拟。

  • Elections: Kim (2011) has modeled a psychological model of judgement from previous research (notably featuring motivated reasoning), and compared the statistical regularities of the simulation with empirical observations of voter behavior;[11] others have compared delegation methods.[12][13]

Agent-based computing is the design of the model and agents, while the computer simulation is the part of the simulation of the agents in the model and the outcomes. The social science is a mixture of sciences and social part of the model. It is where the social phenomena is developed and theorized. The main purpose of ABSS is to provide models and tools for agent-based simulation of social phenomena. With ABSS we can explore different outcomes for phenomena where we might not be able to view the outcome in real life. It can provide us valuable information on society and the outcomes of social events or phenomena.

基于 agent 的计算是模型和 agent 的设计,而计算机模拟是模型和结果中 agent 的模拟部分。社会科学是模型中科学和社会的混合体。它是社会现象发展和理论化的地方。ABSS 的主要目的是为基于智能体的社会现象模拟提供模型和工具。使用 ABSS,我们可以探索现象的不同结果,在现实生活中我们可能无法看到结果。它可以为我们提供有关社会和社会事件或现象的结果的宝贵信息。


Types of simulation and modeling

Social simulation can refer to a general class of strategies for understanding social dynamics using computers to simulate social systems. Social simulation allows for a more systematic way of viewing the possibilities of outcomes.

Agent-based modeling (ABM) is a system in which a collection of agents independently interact on networks. Each individual agent is responsible for different behaviors that result in collective behaviors. These behaviors as a whole help to define the workings of the network. ABM focuses on human social interactions and how people work together and communicate with one another without having one, single "group mind". This essentially means that it tends to focus on the consequences of interactions between people (the agents) in a population. Researchers are better able to understand this type of modeling by modeling these dynamics on a smaller, more localized level. Essentially, ABM helps to better understand interactions between people (agents) who, in turn, influence one another (in response to these influences). Simple individual rules or actions can result in coherent group behavior. Changes in these individual acts can affect the collective group in any given population.

基于 agent 的建模(Agent-based modeling,ABM)是一个由多个 agent 在网络上相互独立交互的系统。每个个体代理人都要对导致集体行为的不同行为负责。这些行为作为一个整体有助于定义网络的工作。ABM 的重点是人类的社会互动,以及人们如何一起工作和相互沟通,而没有一个,单一的“群体思维”。这本质上意味着它倾向于关注人与人(代理人)之间相互作用的结果。研究人员能够更好地理解这种类型的建模通过建模这些动态在一个更小的,更局部的水平。本质上,ABM 有助于更好地理解人(代理)之间的相互作用,这些人(代理)反过来影响另一个(响应这些影响)。简单的个人规则或动作可以导致连贯的群体行为。这些个人行为的变化可以影响任何特定人口中的集体群体。


There are four major types of social simulation:

Agent-based modeling is an experimental tool for theoretical research. It enables one to deal with more complex individual behaviors, such as adaptation. Overall, through this type of modeling, the creator, or researcher, aims to model behavior of agents and the communication between them in order to better understand how these individual interactions impact an entire population. In essence, ABM is a way of modeling and understanding different global patterns.

基于 agent 的建模是一种理论研究的实验工具。它使人们能够处理更复杂的个人行为,例如适应。总的来说,通过这种类型的建模,创造者,或者说研究者,旨在为代理人的行为和他们之间的交流建模,以便更好地理解这些个体的相互作用是如何影响整个群体的。本质上,ABM 是一种建模和理解不同全局模式的方法。

  1. System level simulation.
  1. System level modeling.
  1. Agent-based simulation.
  1. Agent-based modeling.

There are several current research projects that relate directly to modeling and agent-based simulation the following are listed below with a brief overview.

有几个当前的研究项目,直接相关的建模和基于代理的模拟下面列出了一个简短的概述。


A social simulation may fall within the rubric of computational sociology which is a recently developed branch of sociology that uses computation to analyze social phenomena. The basic premise of computational sociology is to take advantage of computer simulations 模板:Harv in the construction of social theories. It involves the understanding of social agents, the interaction among these agents, and the effect of these interactions on the social aggregate. Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence.


System level simulation

System Level Simulation (SLS) is the oldest level of social simulation. System level simulation looks at the situation as a whole. This theoretical outlook on social situations uses a wide range of information to determine what should happen to society and its members if certain variables are present. Therefore, with specific variables presented, society and its members should have a certain response to the new situation. Navigating through this theoretical simulation will allow researchers to develop educated ideas of what will happen under some specific variables.


For example, if NASA were to conduct a system level simulation it would benefit the organization by providing a cost-effective research method to navigate through the simulation. This allows the researcher to steer through the virtual possibilities of the given simulation and develop safety procedures, and to produce proven facts about how a certain situation will play out. 模板:Harv


System level modeling

System level modeling (SLM) aims to specifically predict (unlike system level simulation's generalization in prediction) and convey any number of actions, behaviors, or other theoretical possibilities of nearly any person, object, construct et cetera within a system using a large set of mathematical equations and computer programming in the form of models.

Agent-based modeling is most useful in providing a bridge between micro and macro levels, which is a large part of what sociology studies. Agent-based models are most appropriate for studying processes that lack central coordination, including the emergence of institutions that, once established, impose order from the top down. The models focus on how simple and predictable local interactions generate familiar but highly detailed global patterns, such as emergence of norms and participation of collective action. Michael W. Macy and Robert Willer researched a recent survey of applications and found that there were two main problems with agent-based modeling the self-organization of social structure and the emergence of social order . Below is a brief description of each problem Macy and Willer believe there to be;

基于 agent 的建模在微观层面和宏观层面之间架起了一座桥梁,这是社会学研究的重要组成部分。基于代理的模型最适合于研究缺乏中央协调的过程,包括出现的机构,一旦建立,从上到下强加秩序。这些模式侧重于简单和可预测的地方互动如何产生熟悉但非常详细的全球模式,如规范的出现和集体行动的参与。和 Robert Willer 研究了最近的一项应用调查,发现基于 agent 的社会结构自我组织建模和社会秩序的出现存在两个主要问题。下面是梅西和威勒认为存在的每个问题的简要描述;


"Emergent structure. In these models, agents change location or behavior in response to social influences or selection pressures. Agents may start out undifferentiated and then change location or behavior so as to avoid becoming different or isolated (or in some cases, overcrowded). Rather than producing homogeneity, however, these conformist decisions aggregate to produce global patterns of cultural differentiation, stratification, and homophilic clustering in local networks. Other studies reverse the process, starting with a heterogeneous population and ending in convergence: the coordination, diffusion, and sudden collapse of norms, conventions, innovations, and technological standards."

“新兴结构。在这些模型中,行为主体根据社会影响或选择压力而改变地点或行为。代理人可能开始时没有区别,然后改变位置或行为,以避免变得不同或孤立(或在某些情况下,过度拥挤)。然而,这些循规蹈矩的决定并没有产生同质性,而是聚合在一起,在地方网络中产生了文化分化、分层和同质聚集的全球模式。其他的研究则反其道而行之,从异质人口开始,到趋同结束: 规范、惯例、创新和技术标准的协调、扩散和突然崩溃。”

A model is a representation of a specific thing ranging from objects and people to structures and products created through mathematical equations and are designed, using computers, in such a way that they are able to stand-in as the aforementioned things in a study. Models can be either simplistic or complex, depending on the need for either; however, models are intended to be simpler than what they are representing while remaining realistically similar in order to be used accurately. They are built using a collection of data that is translated into computing languages that allow them to represent the system in question. These models, much like simulations, are used to help us better understand specific roles and actions of different things so as to predict behavior and the like.

"Emergent social order. These studies show how egoistic adaptation can lead to successful collective action without either altruism or global (top down) imposition of control. A key finding across numerous studies is that the viability of trust, cooperation, and collective action depends decisively on the embeddedness of interaction."

“涌现的社会秩序。这些研究表明,在没有利他主义或全球(自上而下)控制的情况下,利己主义适应可以导致成功的集体行动。众多研究的一个重要发现是,信任、合作和集体行动的可行性决定性地取决于互动的嵌入性。”


Agent-based simulation

These examples simply show the complexity of our environment and that agent-based models are designed to explore the minimal conditions, the simplest set of assumptions about human behavior, required for a given social phenomenon to emerge at a higher level of organization.

这些例子只是简单地展示了我们环境的复杂性,基于代理的模型被设计用来探索最小条件,最简单的一套关于人类行为的假设,需要一个给定的社会现象出现在更高的组织水平。


Agent-based social simulation (ABSS) consists of modeling different societies after artificial agents, (varying on scale) and placing them in a computer simulated society to observe the behaviors of the agents. From this data it is possible to learn about the reactions of the artificial agents and translate them into the results of non-artificial agents and simulations. Three main fields in ABSS are agent-based computing, social science, and computer simulation.


Since its creation, computerized social simulation has been the target of some criticism in regard to its practicality and accuracy. Social simulation's simplification of the complex to form models from which we can better understand the latter is sometimes seen as a draw back, as using fairly simple models to simulate real life with computers is not always the best way to predict behavior.

计算机社会模拟自诞生以来,由于其实用性和准确性一直受到一些批评。社会模拟将复杂的事物简化为模型,从而我们可以更好地理解后者,这有时被看作是一种退步,因为使用相当简单的模型来用计算机模拟现实生活并不总是预测行为的最佳方式。

Agent-based computing is the design of the model and agents, while the computer simulation is the part of the simulation of the agents in the model and the outcomes. The social science is a mixture of sciences and social part of the model. It is where the social phenomena is developed and theorized. The main purpose of ABSS is to provide models and tools for agent-based simulation of social phenomena. With ABSS we can explore different outcomes for phenomena where we might not be able to view the outcome in real life. It can provide us valuable information on society and the outcomes of social events or phenomena.


Most of the criticism seems to be aimed at agent-based models and simulation and how they work:

大多数批评似乎都针对基于主体的模型和模拟,以及它们是如何工作的:

Agent-based modeling

Simulations, being man-made from mathematical interfaces, predict human behavior in a far too simple manner in regard to the complexities of humanity and our actions.

模拟是由数学界面人工制造的,对于人类和我们行为的复杂性,模拟预测人类行为的方式过于简单。

Agent-based modeling (ABM) is a system in which a collection of agents independently interact on networks. Each individual agent is responsible for different behaviors that result in collective behaviors. These behaviors as a whole help to define the workings of the network. ABM focuses on human social interactions and how people work together and communicate with one another without having one, single "group mind". This essentially means that it tends to focus on the consequences of interactions between people (the agents) in a population. Researchers are better able to understand this type of modeling by modeling these dynamics on a smaller, more localized level. Essentially, ABM helps to better understand interactions between people (agents) who, in turn, influence one another (in response to these influences). Simple individual rules or actions can result in coherent group behavior. Changes in these individual acts can affect the collective group in any given population.

Simulations cannot enlighten researchers as to how people interact or behave in ways not programmed into their models. For this reason, the scope of simulations are limited in that the researchers must already know what they are going to find (to a degree, for they cannot find anything they themselves did not place in the model) at least vaguely, possibly skewing the results.

模拟无法启发研究人员了解人们如何以未编入模型的方式进行互动或行为。由于这个原因,模拟的范围是有限的,因为研究人员必须已经知道他们将要发现什么(在一定程度上,因为他们无法找到任何他们自己没有放在模型中的东西) ,至少含糊不清,可能扭曲结果。


Due to the complexities of what is being measured, simulations must be analyzed in unbiased ways; however, with the model running on a pre-made set of instructions coded into it by a modeler, biases exist almost universally.

由于测量内容的复杂性,模拟必须以不偏不倚的方式进行分析; 然而,由于模型运行在预先制定的指令集上,模型建立者将这些指令编码到模型中,偏差几乎普遍存在。

Agent-based modeling is an experimental tool for theoretical research. It enables one to deal with more complex individual behaviors, such as adaptation. Overall, through this type of modeling, the creator, or researcher, aims to model behavior of agents and the communication between them in order to better understand how these individual interactions impact an entire population. In essence, ABM is a way of modeling and understanding different global patterns.

It is highly difficult and often impractical to attempt to link the findings from the abstract world the simulation creates and our complex society and all of its variation.

试图将模拟创造的抽象世界和我们复杂的社会及其所有的变化联系起来是非常困难的,而且通常是不切实际的。


Current research

Researchers working in social simulation might respond that the competing theories from the social sciences are far simpler than those achieved through simulation and therefore suffer the aforementioned drawbacks much more strongly. Theories in some social science tend to be linear models that are not dynamic, and are generally inferred from small laboratory experiments (laboratory tests are most common in psychology but rare in sociology, political science, economics and geography). The behavior of populations of agents under these models is rarely tested or verified against empirical observation.

从事社会模拟研究的研究人员可能会回应说,来自社会科学的竞争理论远比那些通过模拟得到的理论简单,因此遭受上述弊端更为强烈。一些社会科学中的理论往往是线性模型,不是动态的,通常是从小型实验室实验中推断出来的(实验室测试在心理学中最常见,但在社会学、政治学、经济学和地理学中很少见)。在这些模型下代理人群体的行为很少被检验或验证与经验观察。


There are several current research projects that relate directly to modeling and agent-based simulation the following are listed below with a brief overview.



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  • "Generative e-Social Science for Socio-Spatial Simulation" or (GENESIS) is a research node of the UK National Centre for e-Social Science funded by the UK research council ESRC. For further details please see: GENESIS Web Page and Blog.
  • "National e-Infrastructure for Social Simulation" or (NeISS) is a UK-based project funded by JISC. For further details please see: The NeISS Web Pages.
  • "Network Models Governance and R&D collaboration networks" or (N.E.M.O) is a research centre whose main focus is to identify ways to create and to assess desirable network structures for typical functions; (e.g. knowledge, creation, transfer, and distribution.) This research will ultimately aid policy-makers at all political levels in improving the effectiveness and efficiency of network-based policy instruments at promoting the knowledge economy in Europe.
  • "Agent-based Simulations of Market and Consumer Behavior" is another research group that is funded by the Unilever Corporate Research. The current research that is being conducted is investigating the usefulness of agent-based simulations for modeling consumer behavior and to show the potential value and insights it can add to long-established marketing methods.
  • "New and Emergent World Models Through Individual, Evolutionary and Social Learning" or (New Ties) is a three-year project that will ultimately create a virtual society developed by agent-based simulation. The project will develop a simulated society capable of exploring the environment and developing its own image of this environment and the society through interaction. The goal of the research project is for the simulated society to exhibit individual learning, evolutionary learning and social learning.
  • Bruch and Mare's project on neighborhood segregation: The purpose of the study is to figure out the reasoning for neighborhood segregation based on race, and to figure out the tipping point or when people become uncomfortable with the integration levels into their neighborhood, and decide to flee from the neighborhood. They set up a model using flash cards, and put the agent's house in the middle and put houses of different races surrounding the agent's house. They asked people how comfortable they would feel with different situations; if they were okay with one situation, they asked another until the neighborhood was fully integrated. Bruch and Mare's results showed that the tipping point was at 50%. When a neighborhood became 50% minority and 50% white, people of both races began to become uncomfortable and white flight began to rise. The use of agent-based modeling showed how useful it can be in the world of sociology, people did not have to answer why they would become uncomfortable, just which situation they were uncomfortable with.
  • The MAELIA Program (Multi-Agent Emergent Norms Assessment) is a project dealing with the relationships between the users and managers of a natural resource, in that case water, and the related norms and laws that are to be built within them (conventions) or are imposed to them by other actors (institutions). The purpose of the project is to build a generic multiscale platform which is planned to deal with water conflict-related issues.
  • The Mosi-Agil project is a four-year program funded by the Autonomous Region of Madrid through the program MOSI-AGIL-CM (grant S2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER). It aims at creating a body of knowledge and practical tools which are necessary to handle more effectively the behavior of occupants of large facilities. Therefore, the project studies the development of ambient intelligence and intelligent environments supported by the use of Agent-Based Social Simulation.


Agent-based modeling is most useful in providing a bridge between micro and macro levels, which is a large part of what sociology studies. Agent-based models are most appropriate for studying processes that lack central coordination, including the emergence of institutions that, once established, impose order from the top down. The models focus on how simple and predictable local interactions generate familiar but highly detailed global patterns, such as emergence of norms and participation of collective action. Michael W. Macy and Robert Willer researched a recent survey of applications and found that there were two main problems with agent-based modeling the self-organization of social structure and the emergence of social order 模板:Harv. Below is a brief description of each problem Macy and Willer believe there to be;

  1. "Emergent structure. In these models, agents change location or behavior in response to social influences or selection pressures. Agents may start out undifferentiated and then change location or behavior so as to avoid becoming different or isolated (or in some cases, overcrowded). Rather than producing homogeneity, however, these conformist decisions aggregate to produce global patterns of cultural differentiation, stratification, and homophilic clustering in local networks. Other studies reverse the process, starting with a heterogeneous population and ending in convergence: the coordination, diffusion, and sudden collapse of norms, conventions, innovations, and technological standards."
  1. "Emergent social order. These studies show how egoistic adaptation can lead to successful collective action without either altruism or global (top down) imposition of control. A key finding across numerous studies is that the viability of trust, cooperation, and collective action depends decisively on the embeddedness of interaction."


These examples simply show the complexity of our environment and that agent-based models are designed to explore the minimal conditions, the simplest set of assumptions about human behavior, required for a given social phenomenon to emerge at a higher level of organization.


Criticisms

Since its creation, computerized social simulation has been the target of some criticism in regard to its practicality and accuracy. Social simulation's simplification of the complex to form models from which we can better understand the latter is sometimes seen as a draw back, as using fairly simple models to simulate real life with computers is not always the best way to predict behavior.

| doi=10.1016/S1569-190X(02)00119-3

| doi = 10.1016/S1569-190X (02)00119-3


| last=Carley | first=Kathleen M. | year=2002

2002年,凯瑟琳 · m

Most of the criticism seems to be aimed at agent-based models and simulation and how they work:

| title=Computational organizational science and organizational engineering

| title = 计算机组织科学与组织工程


| journal=Simulation Modelling Practice and Theory

模拟建模实践和理论

  1. Simulations, being man-made from mathematical interfaces, predict human behavior in a far too simple manner in regard to the complexities of humanity and our actions.
| volume=10 | issue=5–7 | pages=253–269

10 | issue = 5-7 | pages = 253-269

  1. Simulations cannot enlighten researchers as to how people interact or behave in ways not programmed into their models. For this reason, the scope of simulations are limited in that the researchers must already know what they are going to find (to a degree, for they cannot find anything they themselves did not place in the model) at least vaguely, possibly skewing the results.
| citeseerx=10.1.1.299.9346 }}

10.1.1.299.9346}

  1. Due to the complexities of what is being measured, simulations must be analyzed in unbiased ways; however, with the model running on a pre-made set of instructions coded into it by a modeler, biases exist almost universally.
  1. It is highly difficult and often impractical to attempt to link the findings from the abstract world the simulation creates and our complex society and all of its variation.
| last=Dal Forno | first=Arianna | last2=Merlone | first2=Ugo

2 = Merlone | first2 = Ugo


| year=2002

2002年

Researchers working in social simulation might respond that the competing theories from the social sciences are far simpler than those achieved through simulation and therefore suffer the aforementioned drawbacks much more strongly. Theories in some social science tend to be linear models that are not dynamic, and are generally inferred from small laboratory experiments (laboratory tests are most common in psychology but rare in sociology, political science, economics and geography). The behavior of populations of agents under these models is rarely tested or verified against empirical observation.

| title=A multi-agent simulation platform for modeling perfectly rational and bounded-rational agents in organizations

| title = 一个多智能体仿真平台,用于建模组织中完全理性和有限理性的智能体


| journal= Journal of Artificial Societies and Social Simulations

| Journal = Journal of Artificial Societies and Social Simulations

See also

| volume=5 | issue=2

5 | issue = 2


| url=http://www.ugomerlone.net/documenti/MerloneDalFornoJasss2002.pdf

Http://www.ugomerlone.net/documenti/merlonedalfornojasss2002.pdf

}}

}}

| last=Davidsson | first=Paul | year=2000

| last = Davidsson | first = Paul | year = 2000

| title=Multi Agent Based Simulation: Beyond social simulation

基于多 Agent 的模拟: 超越社会模拟

| journal=In Multi Agent Based Simulation (LNCS Vol. 1979), Springer Verlag

| journal = In Multi Agent Based Simulation (LNCS Vol.1979) ,Springer Verlag

| volume=1979 | pages=97–107 | url=http://www.ide.hk-r.se/~pdv/Papers/MABS2000.pdf

1979 | pages = 97-107 | url = http://www.ide.hk-r.se/~pdv/papers/mabs2000.pdf

| doi=10.1007/3-540-44561-7_7 | citeseerx=10.1.1.15.1056 | series=Lecture Notes in Computer Science | isbn=978-3-540-41522-0 }}

| doi = 10.1007/3-540-44561-7 | citeseerx = 10.1.1.15.1056 | series = Lecture Notes in Computer Science | isbn = 978-3-540-41522-0}

| doi=10.1016/j.physa.2008.02.003

| doi = 10.1016/j.physa. 2008.02.003

| last=Hadzibeganovic | first=Tarik | last2=Stauffer | first2=Dietrich

2 = Stauffer | first2 = Dietrich

| last3=Schulze | first3=Christian

3 = Schulze | first3 = Christian

| year=2008

2008年

| title=Boundary effects in a three-state modified voter model for languages

| title = 语言的三态修改选民模型中的边界效应


| journal=Physica A: Statistical Mechanics and Its Applications

| journal = 物理 a: 统计力学及其应用

References

| volume=387 |issue=13 | pages=3242–3252

387 | issue = 13 | pages = 3242-3252

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| doi=10.1016/S1569-190X(02)00119-3
| last=Carley | first=Kathleen M. | year=2002
| last=Polhill | first=G. J. | last2=Edmonds | first2=B.

| last = Polhill | first = g.J.2 = Edmonds | first2 = b.

| title=Computational organizational science and organizational engineering
| year=2007

2007年

| journal=Simulation Modelling Practice and Theory
| title=Open Access for Social Simulation

| title = 社交模拟的开放存取

| volume=10 | issue=5–7 | pages=253–269
| journal=Journal of Artificial Societies and Social Simulation

| Journal = Journal of Artificial Societies and Social Simulation

| citeseerx=10.1.1.299.9346 }}
| volume=10 |issue=3

10 | issue = 3

Http://jasss.soc.surrey.ac.uk/10/3/10/10.pdf http://jasss.soc.surrey.ac.uk/10/3/10/10.pdf Http://jasss.soc.surrey.ac.uk/10/3/10/10.pdf] Check |url= value (help) line feed character in |url= at position 45 (help); Missing or empty |title= (help)

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| year=2002
| title=A multi-agent simulation platform for modeling perfectly rational and bounded-rational agents in organizations
| last=Takahashi | first=Shingo | last2=Sallach | first2=David

2 = Sallach | first2 = David

| journal= Journal of Artificial Societies and Social Simulations
| last3=Rouchier | first3=Juliette | year=2007

3 = Rouchier | first3 = Juliette | year = 2007

| volume=5 | issue=2
| title=Advancing Social Simulation: The First World Congress

推进社会模拟: 第一届世界大会

| url=http://www.ugomerlone.net/documenti/MerloneDalFornoJasss2002.pdf
| publisher=Springer | page=354

354

}}

| isbn=978-4-431-73150-4

978-4-431-73150-4

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| title=Multi Agent Based Simulation: Beyond social simulation
| journal=In Multi Agent Based Simulation (LNCS Vol. 1979), Springer Verlag
| last=Macy | first=M. W. | last2=Willer | first2= R.

| last = Macy | first = m.2 = Willer | first2 = r.

| volume=1979 | pages=97–107 | url=http://www.ide.hk-r.se/~pdv/Papers/MABS2000.pdf
| year=2002

2002年

| doi=10.1007/3-540-44561-7_7 | citeseerx=10.1.1.15.1056 | series=Lecture Notes in Computer Science | isbn=978-3-540-41522-0 }}

| title=From Factors to Actors

从因素到演员

  • Hadzibeganovic, Tarik; Stauffer, Dietrich, Annual Review of Sociology, 28

社会学年度回顾: 143–166, doi:[//doi.org/10.1146%2Fannurev.soc.28.110601.141117%0A%0A28.110601.141117 10.1146/annurev.soc.28.110601.141117 28.110601.141117] Check |doi= value (help) https://semanticscholar.org/paper/51f0e3fe5335e2c3a55e673a6adae646f0ad6e11 Text " 第28卷

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| last3=Schulze | first3=Christian
| year=2008
| last=Margitay-Becht | first=Andras | year=2005

| last = Margitay-Becht | first = Andras | year = 2005

| title=Boundary effects in a three-state modified voter model for languages
| title=Agent Based Modelling of AID

| title = 基于 Agent 的 AID 建模

| journal=Physica A: Statistical Mechanics and Its Applications
| journal= Interdisciplinary Description of Complex Systems

| 杂志 = 复杂系统的跨学科描述

| volume=387 |issue=13 | pages=3242–3252
| pages=84–93

| 页数 = 84-93

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| url=http://indecs.znanost.org/2005/indecs2005-pp84-93.pdf

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| last=Polhill | first=G. J. | last2=Edmonds | first2=B.
| year=2007
| last=National Research | first=C. | year=2006

| last = National Research | first = c.2006年

| title=Open Access for Social Simulation
| title=Defense Modeling, Simulation, and Analysis: Meeting the Challenge

| title = 防御建模、模拟和分析: 迎接挑战

| journal=Journal of Artificial Societies and Social Simulation
| publisher= 500 Fifth Street, N.W. Washington, DC: The National Academies Press

500 Fifth Street,n.w.华盛顿: 国家学术出版社

| volume=10 |issue=3
| isbn=978-0-309-10303-9

| isbn = 978-0-309-10303-9

| url=http://jasss.soc.surrey.ac.uk/10/3/10/10.pdf
| url=https://books.google.com/books?id=kkQnUwQW8UAC

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  • Stauffer, Dietrich (2003

2003年), "Sociophysics simulations 社会物理学模拟" (PDF), Computing in Science and Engineering 科学与工程中的计算, 5 (3): 71–75, doi:10.1109/MCISE. 2003.1196310 Check |doi= value (help) line feed character in |journal= at position 37 (help); line feed character in |title= at position 25 (help); line feed character in |year= at position 5 (help); Check date values in: |year= (help)

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  • Sun, Ron

最后 = 太阳 (2006

2006年), [http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0521839645

Http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0521839645 Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation 认知与多智能体交互: 从认知建模到社会模拟] Check |url= value (help), Cambridge University Press, New York

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  • Sylvan, Donald A. (1998), "Modeling the rise and fall of states", Mershon International Studies Review, 42 (Supplement_2): 377–379, doi:10.2307/254437, JSTOR 254437
  • {{Citation

Category:Social sciences

类别: 社会科学

| last=Stauffer | first=Dietrich | year=2003

Category:Simulation

类别: 模拟

| title=Sociophysics simulations

Category:Complex systems theory

范畴: 复杂系统理论


This page was moved from wikipedia:en:Social simulation. Its edit history can be viewed at 社会仿真/edithistory

  1. Hughes, H. P. N.; Clegg, C. W.; Robinson, M. A.; Crowder, R. M. (2012). "Agent-based modelling and simulation: The potential contribution to organizational psychology". Journal of Occupational and Organizational Psychology. 85 (3): 487–502. doi:10.1111/j.2044-8325.2012.02053.x.
  2. 2.0 2.1 Crowder, R. M.; Robinson, M. A.; Hughes, H. P. N.; Sim, Y. W. (2012). "The development of an agent-based modeling framework for simulating engineering team work". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 42 (6): 1425–1439. doi:10.1109/TSMCA.2012.2199304.
  3. Robert Axelrod (1986): An Evolutionary Approach to Norms
  4. Felix Flentge, Daniel Polani and Thomas Uthmann (2001) Modelling the Emergence of Possession Norms using Memes
  5. Alexander Staller and Paolo Petta (2001): Introducing Emotions into the Computational Study of Social Norms: A First Evaluation
  6. See Martin Neumann (2008): Homo Socionicus: a Case Study of Simulation Models of Norms for an overview of the recent (as of 2008) research.
  7. José Castro Caldas and Helder Coelho (1999): The Origin of Institutions: socio-economic processes, choice, norms and conventions
  8. Dan Miodownik, Britt Cartrite and Ravi Bhavnani (2010): Between Replication and Docking: "Adaptive Agents, Political Institutions, and Civic Traditions" Revisited
  9. Christian Hahn, Bettina Fley, Michael Florian, Daniela Spresny and Klaus Fischer (2007) : Social Reputation: a Mechanism for Flexible Self-Regulation of Multiagent Systems
  10. JASSS vol. 14: Special section: Simulating the Social Processes of Science
  11. Sung-youn Kim (2011): A Model of Political Judgment: An Agent-Based Simulation of Candidate Evaluation
  12. Ramzi Suleiman and Ilan Fischer (2000) When One Decides for Many: The Effect of Delegation Methods on Cooperation in Simulated Inter-group Conflicts
  13. Marie-Edith Bissey, Mauro Carini and Guido Ortona (2004) ALEX3, a Simulation Program to Compare Electoral Systems