Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, meaning there is no reasonable higher instruction to define the various possible interactions.
Social dynamics can refer to the behavior of groups that results from the interactions of individual group members as well to the study of the relationship between individual interactions and group level behaviors. The field of social dynamics brings together ideas from Economics, Sociology, Social Psychology, and other disciplines, and is a sub-field of complex adaptive systems or complexity science. The fundamental assumption of the field is that individuals are influenced by one another's behavior. The field is closely related to system dynamics. Like system dynamics, social dynamics is concerned with changes over time and emphasizes the role of feedbacks. However, in social dynamics individual choices and interactions are typically viewed as the source of aggregate level behavior, while system dynamics posits that the structure of feedbacks and accumulations are responsible for system level dynamics. Research in the field typically takes a behavioral approach, assuming that individuals are boundedly rational and act on local information. Mathematical and computational modeling are important tools for studying social dynamics. This field grew out of work done in the 1940s by game theorists such as Duncan & Luce, and even earlier works by mathematician Armand Borel. Because social dynamics focuses on individual level behavior, and recognizes the importance of heterogeneity Carlos Gilmore, individuals, strict analytic results are often impossible. Instead, approximation techniques, such as mean field approximations from statistical physics, or computer simulations are used to understand the behaviors of the system. In contrast to more traditional approaches in economics, scholars of social dynamics are often interested in non-equilibrium, or dynamic, behavior. That is, behavior that changes over time.
A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, social and economic organizations, an ecosystem, a living cell, and ultimately the entire universe.
Computational archaeology describes computer-based analytical methods for the study of long-term human behaviour and behavioural evolution. As with other sub-disciplines that have prefixed 'computational' to their name, the term is reserved for methods that could not realistically be performed without the aid of a computer.
The following outline is provided as an overview of and topical guide to academic disciplines:
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of 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.
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, archaeology and linguistics.
The Santa Fe Institute (SFI) is an independent, nonprofit theoretical research institute located in Santa Fe and dedicated to the multidisciplinary study of the fundamental principles of complex adaptive systems, including physical, computational, biological, and social systems. The Institute is ranked 25th among the world's "Top Science and Technology Think Tanks" and 25th among the world's "Best Transdisciplinary Research Think Tanks" according to the 2018 edition of the Global Go To Think Tank Index Reports, published annually by the University of Pennsylvania.
The New England Complex Systems Institute (NECSI) is a small American research institution and think tank dedicated to advancing the study of complex systems. NECSI offers educational programs, conducts research, and hosts the International Conference on Complex Systems. It was founded in 1996 and is located in Cambridge, Massachusetts.
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.
Computational science, also known as scientific computing or scientific computation (SC), is a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems. It is an area of science which spans many disciplines, but at its core, it involves the development of models and simulations to understand natural systems.
Computational economics is a research discipline at the interface of computer science, economics, and management science. This subject encompasses computational modeling of economic systems, whether agent-based, general-equilibrium, macroeconomic, or rational-expectations, computational econometrics and statistics, computational finance, computational tools for the design of automated internet markets, programming tool specifically designed for computational economics and the teaching of computational economics. Some of these areas are unique, while others extend traditional areas of economics by solving problems that are tedious to study without computers and associated numerical methods.
The Biocomplexity Institute of Virginia Tech is a research organization specializing in bioinformatics, computational biology, and systems biology. The Institute has more than 250 personnel, including over 50 tenured and research faculty. Research at the Institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology and medicine. The institute develops -omic and bioinformatic tools and databases that can be applied to the study of human, animal and plant diseases as well as the discovery of new vaccine, drug and diagnostic targets.
The Krasnow Institute for Advanced Study brings together researchers from many disciplines to study the phenomenon known as the mind. A unit of George Mason University, the Institute for Advanced Study also serves as a center for doctoral education in neuroscience. Research at the Institute is funded by agencies such as the National Institutes of Health, the National Science Foundation and the Department of Defense.
The Computer Journal is a peer-reviewed scientific journal covering computer science and information systems. It is published by Oxford University Press on behalf of the British Computer Society. It was established in 1958. Several breakthroughs in computer science were first reported in the journal, including the Quicksort algorithm proposed by C. A. R. Hoare. The authors of the best paper in each volume receive the Wilkes Award and Medal granted by the British Computer Society.
Neuroinformatics is a research field concerned with the organization of neuroscience data by the application of computational models and analytical tools. These areas of research are important for the integration and analysis of increasingly large-volume, high-dimensional, and fine-grain experimental data. Neuroinformaticians provide computational tools, mathematical models, and create interoperable databases for clinicians and research scientists. Neuroscience is a heterogeneous field, consisting of many and various sub-disciplines. In order for our understanding of the brain to continue to deepen, it is necessary that these sub-disciplines are able to share data and findings in a meaningful way; Neuroinformaticians facilitate this.
Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods.
MASCOS, or the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems, was established in 2003 with about $11 million in funding over five years from the Australian Research Council (ARC) to research Complex/Intelligent Systems.
