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An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. It combines elements of
game theory Game theory is the study of mathematical models of strategic interactions among rational agents. Myerson, Roger B. (1991). ''Game Theory: Analysis of Conflict,'' Harvard University Press, p.&nbs1 Chapter-preview links, ppvii–xi It has appli ...
, complex systems, emergence, computational sociology,
multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
s, and evolutionary programming.
Monte Carlo method Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deter ...
s 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 Biology is the scientific study of life. It is a natural science with a broad scope but has several unifying themes that tie it together as a single, coherent field. For instance, all organisms are made up of cells that process hereditary ...
, ecology and
social science Social science is one of the branches of science, devoted to the study of societies and the relationships among individuals within those societies. The term was formerly used to refer to the field of sociology, the original "science of s ...
. Agent-based modeling is related to, but distinct from, the concept of
multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
s 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. Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level). Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction. Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as
computer simulation 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 deter ...
s, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.


History

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.


Early developments

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend
Stanislaw Ulam Stanisław Marcin Ulam (; 13 April 1909 – 13 May 1984) was a Polish-American scientist in the fields of mathematics and nuclear physics. He participated in the Manhattan Project, originated the Teller–Ulam design of thermonuclear weapon ...
, 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 the devices later termed cellular automata. Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life. Unlike 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 A checkerboard (American English) or chequerboard (British English; see spelling differences) is a board of checkered pattern on which checkers (also known as English draughts) is played. Most commonly, it consists of 64 squares (8×8) of altern ...
. The Simula programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the first framework for automating step-by-step agent simulations.


1970s and 1980s: the first models

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome. In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from
ethnocentrism Ethnocentrism in social science and anthropology—as well as in colloquial English discourse—means to apply one's own culture or ethnicity as a frame of reference to judge other cultures, practices, behaviors, beliefs, and people, instead o ...
to the dissemination of culture. By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as artificial life, a term coined by
Christopher Langton __NOTOC__ Christopher Gale Langton (born 1948/49) is an American computer scientist and one of the founders of the field of artificial life. He coined the term in the late 1980s when he organized the first "Workshop on the Synthesis and Simulati ...
. The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs. At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).


1990s: expansion

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by Joshua M. Epstein and
Robert Axtell 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 ...
to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture. Other notable 1990s developments included Carnegie Mellon University's
Kathleen Carley 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 at Carnegie Mellon University and also holds appointments i ...
ABM, to explore the co-evolution of social networks and culture. During this 1990s timeframe
Nigel Gilbert Geoffrey Nigel Gilbert (born 21 March 1950) 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'' (University of Surre ...
published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the '' Journal of Artificial Societies and Social Simulation'' (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal ''
Complex Adaptive Systems Modeling Complex commonly refers to: * Complexity, the behaviour of a system whose components interact in multiple ways so possible interactions are difficult to describe ** Complex system, a system composed of many components which may interact with each ...
'' (CASM). Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history, and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments. In the late 1990s, the merger of TIMS and ORSA to form
INFORMS The Institute for Operations Research and the Management Sciences (INFORMS) is an international society for practitioners in the fields of operations research (O.R.), management science, and analytics. It was established in 1995 with the merger ...
, and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining
National Science Foundation The National Science Foundation (NSF) is an independent agency of the United States government that supports fundamental research and education in all the non-medical fields of science and engineering. Its medical counterpart is the National ...
funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the
University of Chicago The University of Chicago (UChicago, Chicago, U of C, or UChi) is a private research university in Chicago, Illinois. Its main campus is located in Chicago's Hyde Park neighborhood. The University of Chicago is consistently ranked among the b ...
and
Argonne National Laboratory Argonne National Laboratory is a science and engineering research national laboratory operated by UChicago Argonne LLC for the United States Department of Energy. The facility is located in Lemont, Illinois, outside of Chicago, and is the l ...
, and then by Michael Prietula of
Emory University Emory University is a private research university in Atlanta, Georgia. Founded in 1836 as "Emory College" by the Methodist Episcopal Church and named in honor of Methodist bishop John Emory, Emory is the second-oldest private institution of ...
. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006. The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.


2000s and later

More recently,
Ron Sun Ron Sun is a cognitive scientist who made significant contributions to computational psychology and other areas of cognitive science and artificial intelligence. He is currently professor of cognitive sciences at Rensselaer Polytechnic Institute, ...
developed methods for basing agent-based simulation on models of human cognition, known as
cognitive social simulation Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses all aspects of intellectual functions and processes such as: perception, attention, though ...
. Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making. Since 2001, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field.


Theory

Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and
complexity Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, leading to nonlinearity, randomness, collective dynamics, hierarchy, and emergence. The term is generally used to ch ...
. Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are
situated {{dictionary In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term ''situated'' is commonly used to refer to robots, but some researchers argue that software agents c ...
in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in
algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...
ic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in a forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water). The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle. In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet,
power-law distributions In statistics, a power law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one q ...
in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency. Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.


Framework

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies: # Complex Network Modeling Level for developing models using interaction data of various system components. # Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers. # Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines. # Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner. Other methods of describing agent-based models include code templates and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol. The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour.


Multi-Scale modelling

One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system), the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales.


Applications


In modeling complex adaptive systems

We live in a very complex world where we face complex phenomena such as the formation of social norms and emergence of new disruptive technologies. To better understand such phenomena, social scientists often use a reductionism approach where they reduce complex systems to lower-lever variables and model the relationships among them through a scheme of equations such as partial differential equation (PDE). This approach that is called equation-based modeling (EBM) has some basic weaknesses in modeling real complex systems. EBMs emphasize nonrealistic assumptions, such as unbounded rationality and perfect information, while adaptability, evolvability, and network effects go unaddressed. In tackling deficiencies of reductionism, the framework of complex adaptive systems (CAS) has proven very influential in the past two decades. In contrast to reductionism, in the CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As a powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale.


In biology

Agent-based modeling has been used extensively in biology, including the analysis of the spread of
epidemics An epidemic (from Greek ἐπί ''epi'' "upon or above" and δῆμος ''demos'' "people") is the rapid spread of disease to a large number of patients among a given population within an area in a short period of time. Epidemics of infectiou ...
, and the threat of biowarfare, biological applications including population dynamics, stochastic gene expression, plant-animal interactions, vegetation ecology,Ch'ng, E. (2009) An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research, in Nature-Inspired informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, R. Chiong, Editor. 2009, IGI Global: Hershey, PA. http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf landscape diversity, sociobiology, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics,
cognitive modeling A cognitive model is an approximation of one or more cognitive processes in humans or other animals for the purposes of comprehension and prediction. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set o ...
, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation, and the human
immune system The immune system is a network of biological processes that protects an organism from diseases. It detects and responds to a wide variety of pathogens, from viruses to parasitic worms, as well as cancer cells and objects such as wood splinte ...
. Agent-based models have also been used for developing decision support systems such as for breast cancer. Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible ''a priori''. Military applications have also been evaluated. Moreover, agent-based models have been recently employed to study molecular-level biological systems. Agent-based models have also been written to describe ecological processes at work in ancient systems, such as those in dinosaur environments and more recent ancient systems as well.


In epidemiology

Agent-based models now complement traditional compartmental models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions. Recently, ABMs such as CovidSim by epidemiologist Neil Ferguson, have been used to inform public health (nonpharmaceutical) interventions against the spread of
SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) is a strain of coronavirus that causes COVID-19 (coronavirus disease 2019), the respiratory illness responsible for the ongoing COVID-19 pandemic. The virus previously had a ...
. Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions. Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated.


In business, technology and network theory

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include
marketing Marketing is the process of exploring, creating, and delivering value to meet the needs of a target market in terms of goods and services; potentially including selection of a target audience; selection of certain attributes or themes to emph ...
, organizational behaviour and
cognition Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses all aspects of intellectual functions and processes such as: perception, attention, though ...
, team working, supply chain optimization and logistics, modeling of
consumer behavior Consumer behavior is the study of individuals, groups, or organizations and all the activities associated with the purchase, use and disposal of goods and services. Consumer behaviour consists of how the consumer's emotions, attitudes, and p ...
, including
word of mouth Word of mouth, or ''viva voce'', is the passing of information from person to person using oral communication, which could be as simple as telling someone the time of day. Storytelling is a common form of word-of-mouth communication where one pe ...
,
social network A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods fo ...
effects,
distributed computing A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another from any system. Distributed computing is a field of computer sci ...
, workforce management, and portfolio management. They have also been used to analyze traffic congestion. Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences). In addition, ABMs have been used to simulate information delivery in ambient assisted environments. A November 2016 article in arXiv analyzed an agent based simulation of posts spread in
Facebook Facebook is an online social media and social networking service owned by American company Meta Platforms. Founded in 2004 by Mark Zuckerberg with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dust ...
. In the domain of peer-to-peer, ad hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown. The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated. Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.


In economics and social sciences

Prior to, and in the wake of the
2008 financial crisis 8 (eight) is the natural number following 7 and preceding 9. In mathematics 8 is: * a composite number, its proper divisors being , , and . It is twice 4 or four times 2. * a power of two, being 2 (two cubed), and is the first number of ...
, interest has grown in ABMs as possible tools for economic analysis. ABMs do not assume the economy can achieve equilibrium and "
representative agent Economists use the term representative agent to refer to the typical decision-maker of a certain type (for example, the typical consumer, or the typical firm). More technically, an economic model is said to have a representative agent if all age ...
s" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "bottom-up" approach and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-
linear Linearity is the property of a mathematical relationship ('' function'') that can be graphically represented as a straight line. Linearity is closely related to '' proportionality''. Examples in physics include rectilinear motion, the linear ...
(disproportionate) responses to proportionally small changes. A July 2010 article in ''
The Economist ''The Economist'' is a British weekly newspaper printed in demitab format and published digitally. It focuses on current affairs, international business, politics, technology, and culture. Based in London, the newspaper is owned by The Eco ...
'' looked at ABMs as alternatives to
DSGE Dynamic stochastic general equilibrium modeling (abbreviated as DSGE, or DGE, or sometimes SDGE) is a macroeconomic method which is often employed by monetary and fiscal authorities for policy analysis, explaining historical time-series data, as we ...
models. The journal ''
Nature Nature, in the broadest sense, is the physical world or universe. "Nature" can refer to the phenomena of the physical world, and also to life in general. The study of nature is a large, if not the only, part of science. Although humans are ...
'' also encouraged agent-based modeling with an editorial that suggested ABMs can do a better job of representing financial markets and other economic complexities than standard models along with an essay by
J. Doyne Farmer J. Doyne Farmer (born 22 June 1952) is an American complex systems scientist and entrepreneur with interests in chaos theory, complexity and econophysics. He is Baillie Gifford Professor of Mathematics at Oxford University, where he is also Direct ...
and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations. Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models. By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index. However, the ABM approach has been criticized for its lack of robustness between models, where similar models can yield very different results. ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment and the examination of public policy applications to land-use. There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network. Heterogeneity and dynamics can be easily built in ABM models to address wealth inequality and social mobility.


In water management

ABMs have also been applied in water resources planning and management, particularly for exploring, simulating, and predicting the performance of infrastructure design and policy decisions, and in assessing the value of cooperation and information exchange in large water resources systems.


Organizational ABM: agent-directed simulation

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems." Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).


Self-driving cars

Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents. Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars. It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.


Implementation

Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size, scalability restrictions may hinder model validation. Such limitations have mainly been addressed using
distributed computing A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another from any system. Distributed computing is a field of computer sci ...
, with frameworks such as Repast HPC specifically dedicated to these type of implementations. While such approaches map well to
cluster may refer to: Science and technology Astronomy * Cluster (spacecraft), constellation of four European Space Agency spacecraft * Asteroid cluster, a small asteroid family * Cluster II (spacecraft), a European Space Agency mission to study th ...
and
supercomputer A supercomputer is a computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second ( FLOPS) instead of million instructio ...
architectures, issues related to communication and synchronization, as well as deployment complexity, remain potential obstacles for their widespread adoption. A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation. The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.


Integration with other modeling forms

Since Agent-Based Modeling is more of a modeling framework than a particular piece of software or platform, it has often been used in conjunction with other modeling forms. For instance, agent-based models have also been combined with
Geographic Information Systems A geographic information system (GIS) is a type of database containing geographic data (that is, descriptions of phenomena for which location is relevant), combined with software tools for managing, analyzing, and visualizing those data. In a ...
(GIS). This provides a useful combination where the ABM serves as a process model and the GIS system can provide a model of pattern. Similarly, Social Network Analysis (SNA) tools and agent-based models are sometimes integrated, where the ABM is used to simulate the dynamics on the network while the SNA tool models and analyzes the network of interactions.


Verification and validation

Verification and validation (V&V) of simulation models is extremely important. Verification involves making sure the implemented model matches the conceptual model, whereas validation ensures that the implemented model has some relationship to the real-world. Face validation, sensitivity analysis, calibration, and statistical validation are different aspects of validation. A discrete-event simulation framework approach for the validation of agent-based systems has been proposed. A comprehensive resource on empirical validation of agent-based models can be found here. As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system), a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model. Another software engineering method, i.e. Test-Driven Development has been adapted to for agent-based model validation. This approach has another advantage that allows an automatic validation using unit test tools.


See also

*
Agent-based computational economics 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. I ...
* Agent-based model in biology *
Agent-based social simulation Agent-based social simulation (or ABSS) 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 o ...
(ABSS) *
Artificial society 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, a ...
* Boids * Comparison of agent-based modeling software * Complex system * Complex adaptive system * Computational sociology * Conway's Game of Life * Dynamic network analysis * Emergence * Evolutionary algorithm * Flocking * Internet bot * Kinetic exchange models of markets *
Multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...
*
Simulated reality The simulation theory is the hypothesis that reality could be simulated—for example by quantum computer simulation—to a degree indistinguishable from "true" reality. It could contain conscious minds that may or may not know that they live i ...
* Social complexity *
Social 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, e ...
* Sociophysics * Software agent *
Swarming behaviour Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving ''en masse'' or migrating in some direction. ...
* Web-based simulation * TOTREP


References


General

* * * * first edition, 1999. * * * * * * * * * * * * * * * Available online. * *


External links


Articles/general information


Agent-based models of social networks, java applets.


* ttp://www-unix.mcs.anl.gov/~leyffer/listn/slides-06/MacalNorth.pdf Introduction to Agent-based Modeling and Simulation
Argonne National Laboratory Argonne National Laboratory is a science and engineering research national laboratory operated by UChicago Argonne LLC for the United States Department of Energy. The facility is located in Lemont, Illinois, outside of Chicago, and is the l ...
, November 29, 2006.
Agent-based models in Ecology – Using computer models as theoretical tools to analyze complex ecological systems

Network for Computational Modeling in the Social and Ecological Sciences' Agent Based Modeling FAQ


– Article on the convergence of SOA, BPM and Multi-Agent Technology in the domain of the Enterprise Information Systems. Jose Manuel Gomez Alvarez, Artificial Intelligence, Technical University of Madrid – 2006
Artificial Life Framework

Article providing methodology for moving real world human behaviors into a simulation model where agent behaviors are represented

Agent-based Modeling Resources
an information hub for modelers, methods, and philosophy for agent-based modeling
An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications
Tommi A. Vuorenmaa (Valo Research and Trading), Liang Wang (University of Helsinki - Department of Computer Science), October, 2013


Simulation models


Multi-agent Meeting Scheduling System Model by Qasim Siddique


* List of COVID-19 simulation models {{Swarming Models of computation Complex systems theory Multi-agent systems Methods in sociology Artificial life Simulation