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, ( Takahashi, Sallach & Rouchier 2007 ).organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics
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.
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.
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.
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.
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.
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.
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.
More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation (see ( Sun 2006 ))
Here are some sample topics that have been explored with social simulation:
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.
There are four major types of social simulation:
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 ( Polhill & Edmonds 2007 ) 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 (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. ( National Research 2006 )
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.
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.
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.
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.
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.
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.
There are several current research projects that relate directly to modeling and agent-based simulation the following are listed below with a brief overview.
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 ( Macy & Willer 2002 ). Below is a brief description of each problem Macy and Willer believe there to be;
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.
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.
Most of the criticism seems to be aimed at agent-based models and simulation and how they work:
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.
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of and/or the outcome of a real-world or physical system. Since they allow to check the reliability of chosen mathematical models, computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.
Systems science is an interdisciplinary field that studies the nature of systems—from simple to complex—in nature, society, cognition, engineering, technology and science itself. To systems scientists, the world can be understood as a system of systems. The field aims to develop interdisciplinary foundations that are applicable in a variety of areas, such as psychology, biology, medicine, communication, business management, computer science, engineering, and social sciences.
Crowd simulation is the process of simulating the movement of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation.
In sociology, social complexity is a conceptual framework used in the analysis of society. Contemporary definitions of complexity in the sciences are found in relation to systems theory, in which a phenomenon under study has many parts and many possible arrangements of the relationships between those parts. At the same time, what is complex and what is simple is relative and may change with time.
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents 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.
Artificial society is the specific agent based computational model for computer simulation in social analysis. It is mostly connected to the theme in complex system, emergence, Monte Carlo method, computational sociology, multi-agent system, and evolutionary programming. The concept itself is simple enough. Actually reaching this conceptual point took a while. Complex mathematical models have been, and are, common; deceivingly simple models only have their roots in the late forties, and took the advent of the microcomputer to really get up to speed.
Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena". By modelling such interactions, it can suggest that properties exist in the system that had not been noticed in the real world situation. An example field of study is how unintended consequences arise in social processes.
Mathematical sociology is the area of sociology that uses mathematics to construct social theories. Mathematical sociology aims to take sociological theory, which is strong in intuitive content but weak from a formal point of view, and to express it in formal terms. The benefits of this approach include increased clarity and the ability to use mathematics to derive implications of a theory that cannot be arrived at intuitively. In mathematical sociology, the preferred style is encapsulated in the phrase "constructing a mathematical model." This means making specified assumptions about some social phenomenon, expressing them in formal mathematics, and providing an empirical interpretation for the ideas. It also means deducing properties of the model and comparing these with relevant empirical data. Social network analysis is the best-known contribution of this subfield to sociology as a whole and to the scientific community at large. The models typically used in mathematical sociology allow sociologists to understand how predictable local interactions are and they are often able to elicit global patterns of social structure.
Computer simulation is a prominent method in organizational studies and strategic management. While there are many uses for computer simulation, most academics in the fields of strategic management and organizational studies have used computer simulation to understand how organizations or firms operate. More recently, however, researchers have also started to apply computer simulation to understand organizational behaviour at a more micro-level, focusing on individual and interpersonal cognition and behavior such as team working.
AgentSheets is a Cyberlearning tool to teach students programming and related information technology skills through game design.
Agent-based social simulation consists of social simulations that are based on agent-based modeling, and implemented using artificial agent technologies. Agent-based social simulation is a scientific discipline concerned with simulation of social phenomena, using computer-based multiagent models. In these simulations, persons or group of persons are represented by agents. MABSS is combination of social science, multiagent simulation and computer simulation.
Geoffrey Nigel Gilbert is a British sociologist and a pioneer in the use of agent-based models in the social sciences. He is the founder and director of the Centre for Research in Social Simulation, author of several books on computational social science, social simulation and social research and past editor of the Journal of Artificial Societies and Social Simulation (JASSS), the leading journal in the field.
The European Social Simulation Association (ESSA) is a scientific society aimed at promoting the development of social simulation research, education and application in Europe. It has over 350 members from several European countries. The association organizes a European conference every two years, and — in joint action with the Computational Social Science Society of the Americas (CSSSA) and the Pacific Asian Association for Agent-based Approach in Social Systems Sciences (PAAA) — a World Congress on Social Simulation (WCSS) every other year.
Kathleen M. Carley is an American social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Institute for Software Research International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and Public Policy, and the Department of Social and Decision Sciences.
Rosaria Conte was an Italian social scientist. She was the head of the Laboratory of Agent Based Social Simulation at the ISTC-CNR in Rome, which hosts an interdisciplinary research group working at the intersection among cognitive, social and computational sciences. She was President of European Social Simulation Association and AISC. Rosaria Conte published more than 130 works among volumes, papers in scientific journals, conference proceedings and book chapters. Her scientific activity aims at explaining social behaviour among intelligent autonomous systems, and modeling the dynamics of norms and norm-enforcement mechanisms. Her research was characterized by a highly interdisciplinary approach, at the intersection among cognitive, social and computational sciences. In her name, the European Social Simulation Association assigns every other year the Outstanding Contribution Award for Social Simulation, whose first recipients are Nigel Gilbert and Uri Wilensky.
Cristiano Castelfranchi is an Associate Researcher at the Institute of Psychology of the Italian National Research Council. He teaches Cognitive Psychology and Artificial Intelligence at the University of Siena. In 2003, he was made a fellow at the European Coordinating Committee for Artificial Intelligence for pioneering work in Artificial Intelligence.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics, culturomics, and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis.
Historical dynamics broadly includes the scientific modeling of history. This might also be termed computer modeling of history, historical simulation, or simulation of history - allowing for an extensive range of techniques in simulation and estimation. Historical dynamics does not exist as a separate science, but there are individual efforts such as long range planning, population modeling, economic forecasting, demographics, global modeling, country modeling, regional planning, urban planning and many others in the general categories of computer modeling, planning, forecasting, and simulations.