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An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) 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),

 

An agent-based model (ABM) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) 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.

 

 

Agent-based models are a kind of [[Microscale and macroscale models|microscale model]]<ref>{{cite journal |first1=Leif |last1=Gustafsson |first2=Mikael |last2=Sternad |year=2010 |title=Consistent micro, macro, and state-based population modelling |journal=Mathematical Biosciences |volume=225 |issue=2 |pages=94–107 |doi=10.1016/j.mbs.2010.02.003 |pmid=20171974 }}</ref> 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).

Agent-based models are a kind of [[Microscale and macroscale models|microscale model]]<ref>{{cite journal |first1=Leif |last1=Gustafsson |first2=Mikael |last2=Sternad |year=2010 |title=Consistent micro, macro, and state-based population modelling |journal=Mathematical Biosciences |volume=225 |issue=2 |pages=94–107 |doi=10.1016/j.mbs.2010.02.003 |pmid=20171974 }}</ref> 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).

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).

Agent-based models are a kind of microscale model The journal Nature 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 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.

 

 

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,<ref>{{cite web |url=http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |title=Agent-Based Models of Industrial Ecosystems |publisher=[[Rutgers University]] |date=October 6, 2003 |archiveurl=https://web.archive.org/web/20110720041914/http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |archivedate=July 20, 2011 |url-status=dead}}</ref> using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.<ref name="Bonabeau 2002 ABM">{{cite journal |title=Agent-based modeling: Methods and techniques for simulating human systems |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=99 |pages=7280–7 |date=May 14, 2002 |doi=10.1073/pnas.082080899 |pmid=12011407 |pmc=128598 |last1=Bonabeau |first1=E. |bibcode=2002PNAS...99.7280B }}</ref>

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,<ref>{{cite web |url=http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |title=Agent-Based Models of Industrial Ecosystems |publisher=[[Rutgers University]] |date=October 6, 2003 |archiveurl=https://web.archive.org/web/20110720041914/http://policy.rutgers.edu/andrews/projects/abm/abmarticle.htm |archivedate=July 20, 2011 |url-status=dead}}</ref> using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.<ref name="Bonabeau 2002 ABM">{{cite journal |title=Agent-based modeling: Methods and techniques for simulating human systems |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=99 |pages=7280–7 |date=May 14, 2002 |doi=10.1073/pnas.082080899 |pmid=12011407 |pmc=128598 |last1=Bonabeau |first1=E. |bibcode=2002PNAS...99.7280B }}</ref>

大多数基于代理的模型由以下几个部分组成: (1)在不同尺度上指定的众多代理(通常称为代理粒度) ; (2)决策启发法; (3)学习规则或自适应过程; (4)交互拓扑; (5)环境。Abm 通常是以计算机模拟的形式实现的，可以是定制的软件，也可以通过 ABM 工具包，然后这个软件可以用来测试个人行为的改变将如何影响系统的整体行为。

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 [[network topology|interaction topology]]; and (5) an environment. ABMs are typically implemented as [[computer simulation]]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==

==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.

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.

基于 agent 的建模思想是在20世纪40年代后期作为一个相对简单的概念发展起来的。由于它需要计算密集型的过程，直到20世纪90年代才广泛使用。

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.

 

===Early developments===

===Early developments===

The history of the agent-based model can be traced back to the [[Von Neumann universal constructor|Von Neumann machine]], a theoretical machine capable of reproduction. The device [[John von Neumann|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]], 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]].

The history of the agent-based model can be traced back to the [[Von Neumann universal constructor|Von Neumann machine]], a theoretical machine capable of reproduction. The device [[John von Neumann|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]], 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 Horton Conway|John Conway]].  He constructed the well-known [[Conway's Game of Life|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]].

Another advance was introduced by the mathematician [[John Horton Conway|John Conway]].  He constructed the well-known [[Conway's Game of Life|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]].

===1970s and 1980s: the first models===

===1970s and 1980s: the first models===

One of the earliest agent-based models in concept was [[Thomas Schelling]]'s segregation model,<ref name="Thomas">{{cite journal |last=Schelling |first=Thomas C. |title=Dynamic Models of Segregation |year=1971 |journal=Journal of Mathematical Sociology |volume=1 |issue=2 |pages=143–186 |url=http://zolaist.org/wiki/images/c/cf/Models_of_Segregation.pdf |doi=10.1080/0022250x.1971.9989794}}</ref> 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.

One of the earliest agent-based models in concept was [[Thomas Schelling]]'s segregation model,<ref name="Thomas">{{cite journal |last=Schelling |first=Thomas C. |title=Dynamic Models of Segregation |year=1971 |journal=Journal of Mathematical Sociology |volume=1 |issue=2 |pages=143–186 |url=http://zolaist.org/wiki/images/c/cf/Models_of_Segregation.pdf |doi=10.1080/0022250x.1971.9989794}}</ref> 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]] to the dissemination of culture.<ref name="Axelrod_1997">{{Cite book |last=Axelrod |given=Robert |authorlink=Robert Axelrod |year=1997 |title=The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration |publisher=Princeton: Princeton University Press |isbn=978-0-691-01567-5 }}</ref>

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]] to the dissemination of culture.<ref name="Axelrod_1997">{{Cite book |last=Axelrod |given=Robert |authorlink=Robert Axelrod |year=1997 |title=The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration |publisher=Princeton: Princeton University Press |isbn=978-0-691-01567-5 }}</ref>

By the late 1980s, [[Craig Reynolds (computer graphics)|Craig Reynolds]]' work on [[flocking behavior|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]].

By the late 1980s, [[Craig Reynolds (computer graphics)|Craig Reynolds]]' work on [[flocking behavior|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]].

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 Henry Holland|John Holland]] and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory",<ref name="Holland">{{cite journal |last1=Holland |first1=J.H. |last2=Miller |first2=J.H. |title=Artificial Adaptive Agents in Economic Theory |year=1991 |journal=American Economic Review |volume=81 |issue=2 |pages=365–71 |url=http://zia.hss.cmu.edu/miller/papers/aaa.pdf |archive-url=https://web.archive.org/web/20051027152415/http://zia.hss.cmu.edu/miller/papers/aaa.pdf |url-status=dead |archive-date=2005-10-27 }}</ref> based on an earlier conference presentation of theirs.

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 Henry Holland|John Holland]] and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory",<ref name="Holland">{{cite journal |last1=Holland |first1=J.H. |last2=Miller |first2=J.H. |title=Artificial Adaptive Agents in Economic Theory |year=1991 |journal=American Economic Review |volume=81 |issue=2 |pages=365–71 |url=http://zia.hss.cmu.edu/miller/papers/aaa.pdf |archive-url=https://web.archive.org/web/20051027152415/http://zia.hss.cmu.edu/miller/papers/aaa.pdf |url-status=dead |archive-date=2005-10-27 }}</ref> 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).

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===

===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

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]] to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.<ref name="GAS">{{cite book |first1=Joshua M. |last1=Epstein |authorlink1=Joshua M. Epstein |first2=Robert |last2=Axtell |authorlink2=Robert Axtell |title=Growing artificial societies: social science from the bottom up |publisher=Brookings Institution Press |date=October 11, 1996 |pages=[https://archive.org/details/growingartificia00epst/page/224 224] |isbn=978-0-262-55025-3 |url-access=registration |url=https://archive.org/details/growingartificia00epst/page/224 }}</ref> Other notable 1990s developments included [[Carnegie Mellon University]]'s [[Kathleen Carley]] ABM,<ref>{{cite web |url=http://www.casos.cs.cmu.edu/projects/construct/index.php |title=Construct |publisher=Computational Analysis of Social Organizational Systems |accessdate=}}</ref> to explore the co-evolution of social networks and culture.

[[Joshua M. Epstein]] and [[Robert Axtell]] to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.<ref name="GAS">{{cite book |first1=Joshua M. |last1=Epstein |authorlink1=Joshua M. Epstein |first2=Robert |last2=Axtell |authorlink2=Robert Axtell |title=Growing artificial societies: social science from the bottom up |publisher=Brookings Institution Press |date=October 11, 1996 |pages=[https://archive.org/details/growingartificia00epst/page/224 224] |isbn=978-0-262-55025-3 |url-access=registration |url=https://archive.org/details/growingartificia00epst/page/224 }}</ref> Other notable 1990s developments included [[Carnegie Mellon University]]'s [[Kathleen Carley]] ABM,<ref>{{cite web |url=http://www.casos.cs.cmu.edu/projects/construct/index.php |title=Construct |publisher=Computational Analysis of Social Organizational Systems |accessdate=}}</ref> to explore the co-evolution of social networks and culture.

During this 1990s timeframe [[Nigel Gilbert]] 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]]'' (CASM).<ref>[http://www.casmodeling.com Springer Complex Adaptive Systems Modeling Journal (CASM)]</ref>

During this 1990s timeframe [[Nigel Gilbert]] 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]]'' (CASM).<ref>[http://www.casmodeling.com Springer Complex Adaptive Systems Modeling Journal (CASM)]</ref>

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,<ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2000/orms-12-00/Designing-Organizations|last=Samuelson |first=Douglas A. |title=Designing Organizations |journal=OR/MS Today |date=December 2000 }}</ref> and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.<ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2005/orms-2-05/Agents-of-Change|last=Samuelson |first=Douglas A. |title=Agents of Change |journal=OR/MS Today |date=February 2005 }}</ref><ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2006/orms-8-06/Agent-Based-Simulation-Comes-of-Age |last1=Samuelson |first1=Douglas A. |last2=Macal |first2=Charles M. |title=Agent-Based Modeling Comes of Age |journal=OR/MS Today |date=August 2006 }}</ref>

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,<ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2000/orms-12-00/Designing-Organizations|last=Samuelson |first=Douglas A. |title=Designing Organizations |journal=OR/MS Today |date=December 2000 }}</ref> and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.<ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2005/orms-2-05/Agents-of-Change|last=Samuelson |first=Douglas A. |title=Agents of Change |journal=OR/MS Today |date=February 2005 }}</ref><ref>{{cite journal |url=https://www.informs.org/ORMS-Today/Archived-Issues/2006/orms-8-06/Agent-Based-Simulation-Comes-of-Age |last1=Samuelson |first1=Douglas A. |last2=Macal |first2=Charles M. |title=Agent-Based Modeling Comes of Age |journal=OR/MS Today |date=August 2006 }}</ref>

In the late 1990s, the merger of TIMS and ORSA to form [[INFORMS]], 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]] funding for the annual conference and serving as the first President of NAACSOS.  She was succeeded by David Sallach of the [[University of Chicago]] and [[Argonne National Laboratory]], and then by Michael Prietula of [[Emory University]]. 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.{{citation needed|date=April 2012}} 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.

In the late 1990s, the merger of TIMS and ORSA to form [[INFORMS]], 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]] funding for the annual conference and serving as the first President of NAACSOS.  She was succeeded by David Sallach of the [[University of Chicago]] and [[Argonne National Laboratory]], and then by Michael Prietula of [[Emory University]]. 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.{{citation needed|date=April 2012}} 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===

===2000s and later===

More recently, [[Ron Sun]] developed methods for basing agent-based simulation on models of human cognition, known as [[cognitive social simulation]].<ref>{{cite book |editor1-last=Sun |editor1-first=Ron |editor1-link=Ron Sun |title=Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation |date=March 2006 |publisher=[[Cambridge University Press]] |isbn=978-0-521-83964-8 |url=https://www.cambridge.org/0521839645}}</ref> 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.{{citation needed|date=April 2012}}

More recently, [[Ron Sun]] developed methods for basing agent-based simulation on models of human cognition, known as [[cognitive social simulation]].<ref>{{cite book |editor1-last=Sun |editor1-first=Ron |editor1-link=Ron Sun |title=Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation |date=March 2006 |publisher=[[Cambridge University Press]] |isbn=978-0-521-83964-8 |url=https://www.cambridge.org/0521839645}}</ref> 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.{{citation needed|date=April 2012}}

==Theory==

==Theory==

Most computational modeling research describes systems in [[Steady state|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]].

Most computational modeling research describes systems in [[Steady state|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]].

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

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]] 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]]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 forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water).

[[situated]] 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]]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 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 reasoning|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.

The modeling process is best described as [[inductive reasoning|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 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.

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 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.

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===

===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.<ref>{{cite journal |author=Aditya Kurve |author2=Khashayar Kotobi |author3=George Kesidis |title=An agent-based framework for performance modeling of an optimistic parallel discrete event simulator |journal=Complex Adaptive Systems Modeling |volume=1 |pages=12 |doi=10.1186/2194-3206-1-12 |year=2013 |doi-access=free }}</ref><ref>{{cite journal |first=Muaz A. K. |last=Niazi |title=Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems |hdl=1893/3365 |date=2011-06-30 }} (PhD Thesis)</ref><ref>Niazi, M.A. and Hussain, A (2012), Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods [https://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 Cognitive Agent-based Computing]</ref> describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models.<ref>{{cite journal |author=Aditya Kurve |author2=Khashayar Kotobi |author3=George Kesidis |title=An agent-based framework for performance modeling of an optimistic parallel discrete event simulator |journal=Complex Adaptive Systems Modeling |volume=1 |pages=12 |doi=10.1186/2194-3206-1-12 |year=2013 |doi-access=free }}</ref><ref>{{cite journal |first=Muaz A. K. |last=Niazi |title=Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems |hdl=1893/3365 |date=2011-06-30 }} (PhD Thesis)</ref><ref>Niazi, M.A. and Hussain, A (2012), Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods [https://www.springer.com/biomed/neuroscience/book/978-94-007-3851-5 Cognitive Agent-based Computing]</ref> 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.

# 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.

# 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.

# 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.

# 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<ref>{{cite web |title=Swarm code templates for model comparison |url=http://www.swarm.org/index.php/Software_templates |publisher=[[Swarm Development Group]] |archiveurl=https://web.archive.org/web/20080803125909/http://www.swarm.org/index.php/Software_templates |archivedate=August 3, 2008 |url-status=dead}}</ref> and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.<ref>{{cite journal |author1=Volker Grimm |author2=Uta Berger |author3=Finn Bastiansen |author4=Sigrunn Eliassen |author5=Vincent Ginot |author6=Jarl Giske |author7=John Goss-Custard |author8=Tamara Grand |author9=Simone K. Heinz |author10=Geir Huse |author11=Andreas Huth |author12=Jane U. Jepsen |author13=Christian Jørgensen |author14=Wolf M. Mooij |author15=Birgit Müller |author16=Guy Pe'er |author17=Cyril Piou |author18=Steven F. Railsback |author19=Andrew M. Robbins |author20=Martha M. Robbins |author21=Eva Rossmanith |author22=Nadja Rüger |author23=Espen Strand |author24=Sami Souissi |author25=Richard A. Stillman |author26=Rune Vabø |author27=Ute Visser |author28=Donald L. DeAngelis |display-authors=3 |title=A standard protocol for describing individual-based and agent-based models |journal=Ecological Modelling |volume=198 |issue=1–2 |date=September 15, 2006 |pages=115–126 |doi=10.1016/j.ecolmodel.2006.04.023 }} (ODD Paper)</ref>

 

 

Other methods of describing agent-based models include code templates<ref>{{cite web |title=Swarm code templates for model comparison |url=http://www.swarm.org/index.php/Software_templates |publisher=[[Swarm Development Group]] |archiveurl=https://web.archive.org/web/20080803125909/http://www.swarm.org/index.php/Software_templates |archivedate=August 3, 2008 |url-status=dead}}</ref> and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.<ref>{{cite journal |authors=Volker Grimm, Uta Berger, Finn Bastiansen, Sigrunn Eliassen, Vincent Ginot, Jarl Giske, John Goss-Custard, Tamara Grand, Simone K. Heinz, Geir Huse, Andreas Huth, Jane U. Jepsen, Christian Jørgensen, Wolf M. Mooij, Birgit Müller, Guy Pe'er, Cyril Piou, Steven F. Railsback, Andrew M. Robbins, Martha M. Robbins, Eva Rossmanith, Nadja Rüger, Espen Strand, Sami Souissi, Richard A. Stillman, Rune Vabø, Ute Visser, Donald L. DeAngelis |title=A standard protocol for describing individual-based and agent-based models |journal=Ecological Modelling |volume=198 |issue=1–2 |date=September 15, 2006 |pages=115–126 |doi=10.1016/j.ecolmodel.2006.04.023 }} (ODD Paper)</ref>

 

 

 

 

The role of the environment where agents live, both macro and micro,<ref>Ch'ng, E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, November 20–24, 2012, Kobe, Japan. [http://complexity.io/Publications/chng-MacroMicroEnv.pdf Macro and Micro Environment] {{Webarchive|url=https://web.archive.org/web/20131113173313/http://complexity.io/Publications/chng-MacroMicroEnv.pdf |date=November 13, 2013 }}</ref> 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.<ref>Simon, Herbert A. The sciences of the artificial. MIT press, 1996.</ref>

The role of the environment where agents live, both macro and micro,<ref>Ch'ng, E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, November 20–24, 2012, Kobe, Japan. [http://complexity.io/Publications/chng-MacroMicroEnv.pdf Macro and Micro Environment] {{Webarchive|url=https://web.archive.org/web/20131113173313/http://complexity.io/Publications/chng-MacroMicroEnv.pdf |date=November 13, 2013 }}</ref> 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.<ref>Simon, Herbert A. The sciences of the artificial. MIT press, 1996.</ref>

==Applications==

==Applications==

===In biology===

===In biology===

{{main|Agent-based model in biology}}

{{main|Agent-based model in biology}}

 

Agent-based modeling has been used extensively in biology, including the analysis of the spread of [[epidemics]],<ref>{{cite arxiv |eprint=nlin/0403035 |last1=Situngkir |first1=Hokky |title=Epidemiology Through Cellular Automata: Case of Study Avian Influenza in Indonesia |year=2004}}</ref> and the threat of [[biowarfare]], [[Agent-based model in biology|biological applications]] including [[population dynamics]],<ref>{{cite journal |last=Caplat |first=Paul |author2=Anand, Madhur |author3=Bauch, Chris |title=Symmetric competition causes population oscillations in an individual-based model of forest dynamics |journal=Ecological Modelling |date=March 10, 2008 |volume=211 |issue=3–4 |pages=491–500 |doi=10.1016/j.ecolmodel.2007.10.002}}</ref> stochastic gene expression,<ref>{{Cite journal|last=Thomas|first=Philipp|date=December 2019|title=Intrinsic and extrinsic noise of gene expression in lineage trees|journal=Scientific Reports|volume=9|issue=1|pages=474|doi=10.1038/s41598-018-35927-x|issn=2045-2322|pmc=6345792|pmid=30679440|bibcode=2019NatSR...9..474T}}</ref> plant-animal interactions,<ref>Fedriani JM, T Wiegand, D Ayllón, F Palomares, A Suárez-Esteban and V. Grimm.  2018. Assisting seed dispersers to restore old-fields: an individual-based model of the interactions among badgers, foxes, and Iberian pear trees. Journal of Applied Ecology 55: 600–611.</ref>  vegetation ecology,<ref name="Ch'ng, E. (2009)">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 {{Webarchive|url=https://web.archive.org/web/20131113173223/http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf |date=November 13, 2013 }}</ref> landscape diversity,<ref>{{Cite journal |last1=Wirth |first1=E. |last2=Szabó |first2=Gy. |last3=Czinkóczky |first3=A. |date=2016-06-07 |journal=ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |volume=III-8 |pages=145–151 |doi=10.5194/isprs-annals-iii-8-145-2016 |bibcode=2016ISPAnIII8..145W|title=Measure of Landscape Heterogeneity by Agent-Based Methodology |doi-access=free }}</ref> the growth and decline of ancient civilizations, evolution of ethnocentric behavior,<ref>{{cite journal |last1=Lima |first1=Francisco W. S. |last2=Hadzibeganovic |first2=Tarik |last3=Stauffer |first3=Dietrich |year=2009 |title=Evolution of ethnocentrism on undirected and directed Barabási–Albert networks |journal=Physica A |volume=388 |issue=24 |pages=4999–5004 |doi=10.1016/j.physa.2009.08.029 |bibcode=2009PhyA..388.4999L |arxiv=0905.2672 |s2cid=18233740 }}</ref> forced displacement/migration,<ref>{{cite book |title=The Chaos of Forced Migration: A Modeling Means to an Humanitarian End |first=Scott |last=Edwards |date=June 9, 2009 |publisher=[[VDM Verlag]] |pages=168 |isbn=978-3-639-16516-6}}</ref> language choice dynamics,<ref>{{cite journal |last1=Hadzibeganovic |first1=Tarik |last2=Stauffer |first2=Dietrich |last3=Schulze |first3=Christian |year=2009 |title=Agent-based computer simulations of language choice dynamics |url=|journal=Annals of the New York Academy of Sciences |volume=1167 |issue=1|pages=221–229 |doi=10.1111/j.1749-6632.2009.04507.x |pmid=19580569 |bibcode=2009NYASA1167..221H }}</ref> [[Cognitive model#Dynamical systems|cognitive modeling]], and biomedical applications including modeling 3D breast tissue formation/morphogenesis,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Enderling |first2=Heiko |last3=Becker-Weimann |first3=Sabine |last4=Pham |first4=Christopher |last5=Polyzos |first5=Aris |last6=Chen |first6=Charlie |last7=Costes |first7=Sylvain |year=2011 |title=Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling |url=|journal=Integrative Biology |volume=3 |issue=4 |pages=408–21 |doi=10.1039/c0ib00092b |pmid=21373705 |pmc=4009383 }}</ref> the effects of ionizing radiation on mammary stem cell subpopulation dynamics,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Fernando-Garcia |first2=Ignacio |last3=Vijayakumar |first3=Sangeetha |last4=Martinez-Ruis |first4=Haydeliz |last5=Illa-Bochaca |first5=Irineu |last6=Nguyen |first6=David |last7=Mao |first7=Jian-Hua |last8=Costes |first8=Sylvain |last9=Barcellos-Hoff |first9=Mary Helen |year=2014 |title=Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors |url=|journal=Stem Cells |volume=32 |issue=3 |pages=649–61 |doi=10.1002/stem.1533 |pmid=24038768  }}</ref> inflammation,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Ley |first2=Klaus |last3=Hunt |first3=C. Anthony |year=2007 |title=Dynamics of in silico leukocyte rolling, activation, and adhesion |url=|journal=BMC Systems Biology |volume=1 |issue=14 |pages=14 |doi=10.1186/1752-0509-1-14 |pmid=17408504 |pmc=1839892 }}</ref>

 

 

 

Agent-based modeling has been used extensively in biology, including the analysis of the spread of [[epidemics]],<ref>{{cite arxiv |eprint=nlin/0403035 |last1=Situngkir |first1=Hokky |title=Epidemiology Through Cellular Automata: Case of Study Avian Influenza in Indonesia |year=2004}}</ref> and the threat of [[biowarfare]], [[Agent-based model in biology|biological applications]] including [[population dynamics]],<ref>{{cite journal |last=Caplat |first=Paul |author2=Anand, Madhur |author3=Bauch, Chris |title=Symmetric competition causes population oscillations in an individual-based model of forest dynamics |journal=Ecological Modelling |date=March 10, 2008 |volume=211 |issue=3–4 |pages=491–500 |doi=10.1016/j.ecolmodel.2007.10.002}}</ref> stochastic gene expression,<ref>{{Cite journal|last=Thomas|first=Philipp|date=December 2019|title=Intrinsic and extrinsic noise of gene expression in lineage trees|journal=Scientific Reports|volume=9|issue=1|pages=474|doi=10.1038/s41598-018-35927-x|issn=2045-2322|pmc=6345792|pmid=30679440|bibcode=2019NatSR...9..474T}}</ref>, plant-animal interactions<ref>Fedriani JM, T Wiegand, D Ayllón, F Palomares, A Suárez-Esteban and V. Grimm.  2018. Assisting seed dispersers to restore old-fields: an individual-based model of the interactions among badgers, foxes, and Iberian pear trees. Journal of Applied Ecology 55: 600–611. </ref>, vegetation ecology,<ref name="Ch'ng, E. (2009)">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 {{Webarchive|url=https://web.archive.org/web/20131113173223/http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf |date=November 13, 2013 }}</ref> landscape diversity,<ref>{{Cite journal |last=Wirth |first=E. |last2=Szabó |first2=Gy. |last3=Czinkóczky |first3=A. |date=2016-06-07 |journal=ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences |volume=III-8 |pages=145–151 |doi=10.5194/isprs-annals-iii-8-145-2016 |bibcode=2016ISPAnIII8..145W|title=Measure of Landscape Heterogeneity by Agent-Based Methodology |doi-access=free }}</ref> the growth and decline of ancient civilizations, evolution of ethnocentric behavior,<ref>{{cite journal |last1=Lima |first1=Francisco W. S. |last2=Hadzibeganovic |first2=Tarik |last3=Stauffer |first3=Dietrich |year=2009 |title=Evolution of ethnocentrism on undirected and directed Barabási–Albert networks |journal=Physica A |volume=388 |issue=24 |pages=4999–5004 |doi=10.1016/j.physa.2009.08.029 |bibcode=2009PhyA..388.4999L |arxiv=0905.2672 }}</ref> forced displacement/migration,<ref>{{cite book |title=The Chaos of Forced Migration: A Modeling Means to an Humanitarian End |first=Scott |last=Edwards |date=June 9, 2009 |publisher=[[VDM Verlag]] |pages=168 |isbn=978-3-639-16516-6}}</ref> language choice dynamics,<ref>{{cite journal |last1=Hadzibeganovic |first1=Tarik |last2=Stauffer |first2=Dietrich |last3=Schulze |first3=Christian |year=2009 |title=Agent-based computer simulations of language choice dynamics |url=|journal=Annals of the New York Academy of Sciences |volume=1167 |issue=1|pages=221–229 |doi=10.1111/j.1749-6632.2009.04507.x |pmid=19580569 |bibcode=2009NYASA1167..221H }}</ref> [[Cognitive model#Dynamical systems|cognitive modeling]], and biomedical applications including modeling 3D breast tissue formation/morphogenesis,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Enderling |first2=Heiko |last3=Becker-Weimann |first3=Sabine |last4=Pham |first4=Christopher |last5=Polyzos |first5=Aris |last6=Chen |first6=Charlie |last7=Costes |first7=Sylvain |year=2011 |title=Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling |url=|journal=Integrative Biology |volume=3 |issue=4 |pages=408–21 |doi=10.1039/c0ib00092b |pmid=21373705 |pmc=4009383 }}</ref> the effects of ionizing radiation on mammary stem cell subpopulation dynamics,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Fernando-Garcia |first2=Ignacio |last3=Vijayakumar |first3=Sangeetha |last4=Martinez-Ruis |first4=Haydeliz |last5=Illa-Bochaca |first5=Irineu |last6=Nguyen |first6=David |last7=Mao |first7=Jian-Hua |last8=Costes |first8=Sylvain |last9=Barcellos-Hoff |first9=Mary Helen |year=2014 |title=Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors |url=|journal=Stem Cells |volume=32 |issue=3 |pages=649–61 |doi=10.1002/stem.1533 |pmid=24038768  }}</ref> inflammation,<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Ley |first2=Klaus |last3=Hunt |first3=C. Anthony |year=2007 |title=Dynamics of in silico leukocyte rolling, activation, and adhesion |url=|journal=BMC Systems Biology |volume=1 |issue=14 |pages=14 |doi=10.1186/1752-0509-1-14 |pmid=17408504 |pmc=1839892 }}</ref>

 

 

<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Hunt |first2=C. Anthony |year=2010 |title=Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion |url=|journal=PLOS Computational Biology |volume=6 |issue=2 |pages=e1000681 |doi=10.1371/journal.pcbi.1000681 |pmid=20174606 |pmc=2824748 |bibcode=2010PLSCB...6E0681T }}</ref>  

<ref>{{cite journal |last1=Tang |first1=Jonathan |last2=Hunt |first2=C. Anthony |year=2010 |title=Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion |url=|journal=PLOS Computational Biology |volume=6 |issue=2 |pages=e1000681 |doi=10.1371/journal.pcbi.1000681 |pmid=20174606 |pmc=2824748 |bibcode=2010PLSCB...6E0681T }}</ref>  

and the human [[immune system]].<ref>{{cite book |last1=Castiglione |first1=Filippo |first2=Franco |last2=Celada |url=http://www.crcpress.com/product/isbn/9781466597488 |title=Immune System Modeling and Simulation |publisher=CRC Press, Boca Raton |year=2015 |pages=274 |isbn=978-1-4665-9748-8}}

and the human [[immune system]].<ref>{{cite book |last1=Castiglione |first1=Filippo |first2=Franco |last2=Celada |url=http://www.crcpress.com/product/isbn/9781466597488 |title=Immune System Modeling and Simulation |publisher=CRC Press, Boca Raton |year=2015 |pages=274 |isbn=978-1-4665-9748-8}}

and the human immune system.<ref>

</ref> Agent-based models have also been used for developing decision support systems such as for breast cancer.<ref>{{Cite book |doi=10.1109/ICICT.2009.5267202 |chapter-url=http://www.cs.stir.ac.uk/~man/papers/ICICT_Cameraready_June20_09.pdf |url-status=dead |archiveurl=https://web.archive.org/web/20110614051810/http://www.cs.stir.ac.uk/~man/papers/ICICT_Cameraready_June20_09.pdf |archivedate=June 14, 2011 |df=mdy-all |chapter=A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis |title=2009 International Conference on Information and Communication Technologies |pages=134–139 |year=2009 |last1=Siddiqa |first1=Amnah |last2=Niazi |first2=Muaz |last3=Mustafa |first3=Farah |last4=Bokhari |first4=Habib |last5=Hussain |first5=Amir |last6=Akram |first6=Noreen |last7=Shaheen |first7=Shabnum |last8=Ahmed |first8=Fouzia |last9=Iqbal |first9=Sarah |isbn=978-1-4244-4608-7 |s2cid=14433449 }} (Breast Cancer DSS)</ref> 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''.<ref name=CPT>{{cite journal |last1=Butler |first1=James |last2=Cosgrove |first2=Jason |last3=Alden |first3=Kieran |last4=Read |first4=Mark |last5=Kumar |first5=Vipin |last6=Cucurull‐Sanchez |first6=Lourdes |last7=Timmis |first7=Jon |last8=Coles |first8=Mark |title=Agent‐Based Modeling in Systems Pharmacology |journal=CPT: Pharmacometrics & Systems Pharmacology |date=2015 |volume=4 |issue=11 |pages=615–629 |doi=10.1002/psp4.12018 |pmid = 26783498|pmc=4716580 }}</ref> Military applications have also been evaluated.<ref>{{cite book |title=Engineering Principles of Combat Modeling and Distributed Simulation |first1=Gnana |last1=Barathy |first2=Levent |last2=Yilmaz |first3=Andreas |last3=Tolk |location=Hoboken, NJ |publisher=[[John Wiley & Sons|Wiley]] |pages=669–714 |date=March 2012 |doi=10.1002/9781118180310.ch27 |chapter=Agent Directed Simulation for Combat Modeling and Distributed Simulation |isbn=9781118180310}}</ref> Moreover, agent-based models have been recently employed to study molecular-level biological systems.<ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Jamali |first2=Yousef |last3=Mofrad |first3=Mohammad R. K. |title=Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion |journal=PLOS ONE |volume=6 |issue=9 |pages=e25306 |doi=10.1371/journal.pone.0025306 |pmc=3179499 |pmid=21966493 |year=2011 |bibcode=2011PLoSO...625306A}}</ref><ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Mofrad |first2=Mohammad R. K. |title=Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import |journal=PLOS ONE |volume=8 |issue=11 |pages=e81741 |doi=10.1371/journal.pone.0081741 |pmc=3840022 |pmid=24282617 |year=2013 |bibcode=2013PLoSO...881741A}}</ref><ref>{{Cite journal |last1=Azimi |first1=Mohammad |last2=Bulat |first2=Evgeny |last3=Weis |first3=Karsten |last4=Mofrad |first4=Mohammad R. K. |date=2014-11-05 |title=An agent-based model for mRNA export through the nuclear pore complex |journal=Molecular Biology of the Cell |volume=25 |issue=22 |pages=3643–3653 |doi=10.1091/mbc.E14-06-1065 |pmc=4230623 |pmid=25253717}}</ref>

===In business, technology and network theory===

===In business, technology and network theory===

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.<ref>{{Cite book |last1=Sarker |first1=R. A. |last2=Ray |first2=T. |chapter=Agent Based Evolutionary Approach: An Introduction |doi=10.1007/978-3-642-13425-8_1 |title=Agent-Based Evolutionary Search |series=Adaptation, Learning, and Optimization |volume=5 |pages=1 |year=2010 |isbn=978-3-642-13424-1 |pmid=|pmc=}}</ref>

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.<ref>{{Cite book |last1=Sarker |first1=R. A. |last2=Ray |first2=T. |chapter=Agent Based Evolutionary Approach: An Introduction |doi=10.1007/978-3-642-13425-8_1 |title=Agent-Based Evolutionary Search |series=Adaptation, Learning, and Optimization |volume=5 |pages=1 |year=2010 |isbn=978-3-642-13424-1 |pmid=|pmc=}}</ref>

===In economics and social sciences===

===In economics and social sciences===

{{main|Agent-based computational economics|Agent-based social simulation}}Prior to, and in the wake of the financial crisis, interest has grown in ABMs as possible tools for economic analysis.<ref>{{cite book |first=Scott E. |last=Page |year=2008 |title=Agent-Based Models |work=[[The New Palgrave Dictionary of Economics]] |edition=2 |url=http://www.dictionaryofeconomics.com/article?id=pde2008_A000218&edition=current&q=agent-based%20computational%20modeling&topicid=&result_number=1}}</ref><ref>{{cite book |editor1-first=Leigh |editor1-last=Testfatsion |editor1-link=Leigh Tesfatsion |editor2-first=Kenneth |editor2-last=Judd |editor2-link=Kenneth Judd |date=May 2006 |title=Handbook of Computational Economics |volume=2 |publisher=[[Elsevier]] |pages=904 |url=http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |isbn=978-0-444-51253-6 |access-date=January 29, 2012 |archive-url=https://web.archive.org/web/20120306100156/http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |archive-date=March 6, 2012 |url-status=dead |df=mdy-all }} ([https://www.sciencedirect.com/science/journal/15740021/2 Chapter preview)]</ref> ABMs do not assume the economy can achieve equilibrium and "[[representative agent]]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]] (disproportionate) responses to proportionally small changes.<ref name="agents">{{cite news |url=http://www.economist.com/node/16636121 |title=Agents of change |date=July 22, 2010 |work=The Economist |accessdate=February 16, 2011 }}</ref> A July 2010 article in ''[[The Economist]]'' looked at ABMs as alternatives to [[Dynamic stochastic general equilibrium|DSGE]] models.<ref name="agents"/> The journal ''[[Nature (journal)|Nature]]'' 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<ref>{{cite journal |title=A model approach |journal=[[Nature (journal)|Nature]] |volume=460 |issue=7256 |page=667 |date=August 6, 2009 |doi=10.1038/460667a |pmid=19661863 |bibcode=2009Natur.460Q.667.|doi-access=free }}</ref> along with an essay by [[J. Doyne Farmer]] 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.{{sfn|Farmer|Foley|2009|p=685}} 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.{{sfn|Farmer|Foley|2009|p=686}} 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.<ref>Stefan, F., & Atman, A. (2015). Is there any connection between the network morphology and the fluctuations of the stock market index? Physica A: Statistical Mechanics and Its Applications, (419), 630-641.</ref>

 

{{main|Agent-based computational economics|Agent-based social simulation}}Prior to, and in the wake of the financial crisis, interest has grown in ABMs as possible tools for economic analysis.<ref>{{cite book |first=Scott E. |last=Page |year=2008 |title=Agent-Based Models |work=[[The New Palgrave Dictionary of Economics]] |edition=2 |url=http://www.dictionaryofeconomics.com/article?id=pde2008_A000218&edition=current&q=agent-based%20computational%20modeling&topicid=&result_number=1}}</ref><ref>{{cite book |editor1-first=Leigh |editor1-last=Testfatsion |editor1-link=Leigh Tesfatsion |editor2-first=Kenneth |editor2-last=Judd |editor2-link=Kenneth Judd |date=May 2006 |title=Handbook of Computational Economics |volume=2 |publisher=[[Elsevier]] |pages=904 |url=http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |isbn=978-0-444-51253-6 |access-date=January 29, 2012 |archive-url=https://web.archive.org/web/20120306100156/http://www.elsevier.com/wps/find/bookdescription.cws_home/660847/description#description |archive-date=March 6, 2012 |url-status=dead |df=mdy-all }} ([https://www.sciencedirect.com/science/journal/15740021/2 Chapter preview)]</ref> ABMs do not assume the economy can achieve equilibrium and "[[representative agent]]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]] (disproportionate) responses to proportionally small changes.<ref name="agents">{{cite news |url=http://www.economist.com/node/16636121 |title=Agents of change |date=July 22, 2010 |work=The Economist |accessdate=February 16, 2011 |ref=harv}}</ref> A July 2010 article in ''[[The Economist]]'' looked at ABMs as alternatives to [[Dynamic stochastic general equilibrium|DSGE]] models.<ref name="agents"/> The journal ''[[Nature (journal)|Nature]]'' 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<ref>{{cite journal |title=A model approach |journal=[[Nature (journal)|Nature]] |volume=460 |issue=7256 |page=667 |date=August 6, 2009 |doi=10.1038/460667a |pmid=19661863 |bibcode=2009Natur.460Q.667.|doi-access=free }}</ref> along with an essay by [[J. Doyne Farmer]] 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.{{sfn|Farmer|Foley|2009|p=685}} 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.{{sfn|Farmer|Foley|2009|p=686}} 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.<ref>Stefan, F., & Atman, A. (2015). Is there any connection between the network morphology and the fluctuations of the stock market index? Physica A: Statistical Mechanics and Its Applications, (419), 630-641.</ref>

===Organizational ABM: agent-directed simulation===

===Organizational ABM: agent-directed simulation===

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems."<ref>{{cite web |url=http://www.eng.auburn.edu/~yilmaz/ADS.html |title=Agent-Directed Simulation |accessdate=}}</ref> 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).

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems."<ref>{{cite web |url=http://www.eng.auburn.edu/~yilmaz/ADS.html |title=Agent-Directed Simulation |accessdate=}}</ref> 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).

Category:Models of computation

Category:Models of computation

类别: 计算模型

类别: 计算模型

Category:Complex systems theory

Category:Complex systems theory

范畴: 复杂系统理论

范畴: 复杂系统理论

[[Category:Scientific modeling]]

[[Waymo]] has created a multi-agent simulation environment Carcraft to test algorithms for [[self-driving car]]s.<ref>{{cite news |last1=Madrigal |first1=Story by Alexis C. |title=Inside Waymo's Secret World for Training Self-Driving Cars |url=https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-testing-and-simulation-facilities/537648/ |accessdate=14 August 2020 |work=The Atlantic}}</ref><ref>{{cite journal |last1=Connors |first1=J. |last2=Graham |first2=S. |last3=Mailloux |first3=L. |title=Cyber Synthetic Modeling for Vehicle-to-Vehicle Applications |journal=In International Conference on Cyber Warfare and Security |date=2018 |page=594-XI |publisher=Academic Conferences International Limited}}</ref> 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.

Category:Scientific modeling

Category:Scientific modeling

类别: 科学建模

类别: 科学建模

 

Category:Multi-agent systems

Category:Multi-agent systems

类别: 多代理系统

类别: 多代理系统

Category:Methods in sociology

Category:Methods in sociology

范畴: 社会学方法

范畴: 社会学方法

 

Category:Artificial life

Category:Artificial life

类别: 人工生命

类别: 人工生命

[[Category:Simulation]]

Many [[Comparison of agent-based modeling software|ABM frameworks]] are designed for serial [[Von Neumann architecture|von-Neumann computer architectures]], limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size,<ref name="Lysenko 2008 MegaScale">{{cite journal |last1=Lysenko |first1=Mikola |last2=D'Souza |first2=Roshan M. |title=A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units |journal=Journal of Artificial Societies and Social Simulation |date=2008 |volume=11 |issue=4 |pages=10 |url=http://jasss.soc.surrey.ac.uk/11/4/10.html |accessdate=16 April 2019 |issn=1460-7425}}</ref> scalability restrictions may hinder model validation.<ref>{{cite journal |last1=Gulyás |first1=László |last2=Szemes |first2=Gábor |last3=Kampis |first3=George |last4=de Back |first4=Walter |title=A Modeler-Friendly API for ABM Partitioning |journal=Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009 |date=2009 |volume=2 |pages=219–226 |url=https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1649189 |location=San Diego, California, USA}}</ref> Such limitations have mainly been addressed using [[distributed computing]], with frameworks such as Repast HPC<ref>{{cite journal |last1=Collier |first1=N. |last2=North |first2=M. |title=Parallel agent-based simulation with Repast for High Performance Computing |journal=Simulation |volume=89 |issue=10 |pages=1215–1235 |doi=10.1177/0037549712462620 |year=2013 |s2cid=29255621 }}</ref> specifically dedicated to these type of implementations. While such approaches map well to [[Computer cluster|cluster]] and [[Supercomputer architecture|supercomputer]] architectures, issues related to communication and synchronization,<ref>{{cite journal |last1=Fujimoto |first1=R. |title=Parallel and distributed simulation |journal=2015 Winter Simulation Conference (WSC) |date=2015 |pages=45–59 |doi=10.1109/WSC.2015.7408152 |location=Huntington Beach, CA, USA|isbn=978-1-4673-9743-8 |url=http://www.lib.ncsu.edu/resolver/1840.4/5268 }}</ref><ref>{{cite journal |last1=Shook |first1=E. |last2=Wang |first2=S. |last3=Tang |first3=W. |title=A communication-aware framework for parallel spatially explicit agent-based models |journal=International Journal of Geographical Information Science |date=2013 |volume=27 |issue=11 |pages=2160–2181 |doi=10.1080/13658816.2013.771740 |publisher=Taylor & Francis|s2cid=41702653 }}</ref> as well as deployment complexity,<ref>{{cite journal |last1=Jonas |first1=E. |last2=Pu |first2=Q. |last3=Venkataraman |first3=S. |last4=Stoica |first4=I. |last5=Recht |first5=B. |title=Occupy the Cloud: Distributed Computing for the 99% |journal=Proceedings of the 2017 Symposium on Cloud Computing (SoCC '17) |date=2017 |pages=445–451 |doi=10.1145/3127479.3128601 |arxiv=1702.04024 |bibcode=2017arXiv170204024J |publisher=ACM |location=Santa Clara, CA, USA|s2cid=854354 }}</ref> remain potential obstacles for their widespread adoption.

Category:Simulation

Category:Simulation

类别: 模拟

类别: 模拟

 

Category:Systems theory

Category:Systems theory