Distributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.
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. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. 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.
NetLogo is a programming language and integrated development environment (IDE) for agent-based modeling.
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.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in 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 computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many 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 artificial society is an agent-based computational model for computer simulation in social analysis. It is mostly connected to the themes of complex systems, emergence, the Monte Carlo method, computational sociology, multi-agent systems, and evolutionary programming. While the concept was simple, actually realizing 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.
Joshua Morris Epstein is Professor of Epidemiology at the New York University College of Global Public Health. Formerly Professor of Emergency Medicine at Johns Hopkins University, with joint appointments in the departments of Applied Mathematics, Economics, Biostatistics, International Health, and Environmental Health Sciences and the Director of the JHU Center for Advanced Modeling in the Social, Behavioral, and Health Sciences. He is an External Professor at the Santa Fe Institute, a member of the New York Academy of Sciences, and a member of the Institute of Medicine's Committee on Identifying and Prioritizing New Preventive Vaccines.
AnyLogic is a multimethod simulation modeling tool developed by The AnyLogic Company. It supports agent-based, discrete event, and system dynamics simulation methodologies. AnyLogic is cross-platform simulation software that works on Windows, macOS and Linux. AnyLogic is used to simulate: markets and competition, healthcare, manufacturing, supply chains and logistics, retail, business processes, social and ecosystem dynamics, defense, project and asset management, pedestrian dynamics and road traffic, IT, and aerospace. It is considered to be among the major players in the simulation industry, especially within the domain of business processes is acknowledged to be a powerful tool.
Sugarscape is a model for artificially intelligent agent-based social simulation following some or all rules presented by Joshua M. Epstein & Robert Axtell in their book Growing Artificial Societies.
Robert Axtell is a professor at George Mason University, Krasnow Institute for Advanced Study, where he is departmental chair of the Department of Computational Social Science. He is also a member of the External Faculty of the Santa Fe Institute. Axtell is also the co-Director of the new Computational Public Policy Lab at Mason.
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.
Juan Pavón is a Spanish computer scientist, full professor of the Complutense University of Madrid (UCM). He is a pioneer researcher in the field of Software Agents, co-creator of the FIPA MESSAGE and INGENIAS methodologies, and founder and director of the research group GRASIA: GRoup of Agent-based, Social and Interdisciplinary Applications at UCM. He is known for his work in the field of Artificial Intelligence, specifically in agent-oriented software engineering. He has been often cited by mainstream media, as a reference in Artificial Intelligence.
Artificial Economics can be defined as ″a research field that aims at improving our understanding of socioeconomic processes with the help of computer simulation″. Like in Theoretical Economics, the approach followed in Artificial Economics to gain understanding of socioeconomic processes involves building and analysing formal models. However, in contrast with Theoretical Economics, models in Artificial Economics are implemented in a programming language so that computers can be employed to analyse them.
Crowd analysis is the practice of interpreting data on the natural movement of groups or objects. Masses of bodies, particularly humans, are the subjects of these crowd tracking analyses that include how a particular crowd moves and when a movement pattern changes. Researchers use the data to predict future crowd movement, crowd density, and plan responses to potential events such as those that require evacuation routes. Applications of crowd analysis can range from video game crowd simulation to security and surveillance.
