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A simulation is an imitative representation of a process or system that could exist in the real world. In this broad sense, simulation can often be used interchangeably with model. Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Another way to distinguish between the terms is to define simulation as experimentation with the help of a model. This definition includes time-independent simulations. Often, computers are used to execute the simulation.
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.
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.
Mathematical models can project how infectious diseases progress to show the likely outcome of an epidemic and help inform public health and plant health interventions. Models use basic assumptions or collected statistics along with mathematics to find parameters for various infectious diseases and use those parameters to calculate the effects of different interventions, like mass vaccination programs. The modelling can help decide which intervention(s) to avoid and which to trial, or can predict future growth patterns, etc.
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.
Evacuation simulation is a method to determine evacuation times for areas, buildings, or vessels. It is based on the simulation of crowd dynamics and pedestrian motion.
The behavioural sciences explore the cognitive processes within organisms and the behavioural interactions between organisms in the natural world. It involves the systematic analysis and investigation of human and animal behaviour through naturalistic observation, controlled scientific experimentation and mathematical modeling. It attempts to accomplish legitimate, objective conclusions through rigorous formulations and observation. Examples of behavioural sciences include psychology, psychobiology, anthropology, sociology, economics, and cognitive science. Generally, behavioural science primarily seeks to generalise about human behaviour as it relates to society and its impact on society as a whole.
In artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Microsimulation is a category of computerized analytical tools that perform analysis of activities such as highway traffic flowing through an intersection, financial transactions, or pathogens spreading disease through a population on the granularity level of individuals. Synonyms include microanalytic simulation and microscopic simulation. Microsimulation, with its emphasis on stochastic or rule-based structures, should not be confused with the similar complementary technique of multi-agent simulation, which focuses more on the behaviour of individuals.
A tax-benefit model is a form of microsimulation model. It is usually based on a representative or administrative data set and certain policy rules. These models are used to cost certain policy reforms and to determine the winners and losers of reform. One example is EUROMOD, which models taxes and benefits for 27 EU states, and its post-Brexit offshoot, UKMOD.
TRANSIMS is an integrated set of tools developed to conduct regional transportation system analyses. With the goal of establishing TRANSIMS as an ongoing public resource available to the transportation community, TRANSIMS is made available under the NASA Open Source Agreement Version 1.3
Traffic simulation or the simulation of transportation systems is the mathematical modeling of transportation systems through the application of computer software to better help plan, design, and operate transportation systems. Simulation of transportation systems started over forty years ago, and is an important area of discipline in traffic engineering and transportation planning today. Various national and local transportation agencies, academic institutions and consulting firms use simulation to aid in their management of transportation networks.
Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system. The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior.
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.
For pensions, a reliable Pension model is necessary for system simulations and projections, so it is important to have a sound database for pension system analyses. For an example of a complex pension model see e.g..
A dynamic microsimulation pension model is a type of a pension model projecting a pension system by means of a microsimulation and generating the complete history of each individual in a data set. The results of such model offer both the aggregate and individual indicators of the pension system. Thanks to complexity of results, there is a possibility to investigate the distribution of pensions, poverty of pensioners, impact of the changes of the pension formula, for more examples see e.g.. Detailed individual set of (administrative) data should serve as a model input.
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.
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.
References
- ↑ Huynh, N; Namazi-Rad, Mohammad-Reza; Perez, P.; Berryman, M.; Chen, Q.; Barthelemy, J. (1 January 2013). "Generating a synthetic population in support of agent-based modeling of transportation in Sydney". Faculty of Engineering and Information Sciences - Papers: Part A: 1357–1363.
- ↑ Hörl, Sebastian; Balac, Milos (2021). "Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data". Transportation Research Part C. 130: 103291. doi:10.1016/j.trc.2021.103291. hdl: 20.500.11850/495494 .
- ↑ Xu, Zhujing; Glass, Kathryn; Lau, Colleen L.; Geard, Nicholas; Graves, Patricia; Clements, Archie (2017). "A synthetic population for modelling the dynamics of infectious disease transmission in American Samoa". Scientific Reports. 7 (1): 16725. Bibcode:2017NatSR...716725X. doi:10.1038/s41598-017-17093-8. ISSN 2045-2322. PMC 5711879 . PMID 29196679. S2CID 256907125.
- ↑ Mueller, Kirill; Axhausen, Kay W. (September 2011). "Hierarchical IPF: Generating a synthetic population for Switzerland". ERSA Conference Papers.
- ↑ Zhu, Yi; Ferreira, Joseph (January 2014). "Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation". Transportation Research Record. 2429 (1): 168–177. doi:10.3141/2429-18. S2CID 16819119.
- ↑ Macal, Charles M.; Collier, Nicholson T.; Ozik, Jonathan; Tatara, Eric R.; Murphy, John T. (December 2018). "Chisim: An Agent-Based Simulation Model of Social Interactions in a Large Urban Area". 2018 Winter Simulation Conference (WSC). pp. 810–820. doi:10.1109/WSC.2018.8632409. ISBN 978-1-5386-6572-5. S2CID 59600666.
- ↑ "Fighting poverty with synthetic data". Brookings. 2023. Retrieved 6 July 2023.