Agent based model
Encyclopedia
An agent-based model (also sometimes related to the term multi-agent system
or multi-agent simulation) 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 system
s, and evolutionary programming
. Monte Carlo Method
s are used to introduce randomness. ABMs are also called individual-based models. A review of recent literature on individual-based models, agent-based models and multiagent systems is given in
The models 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
from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S.
("Keep it simple and short") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. 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) a non-agent environment.
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 improved by von Neumann's friend Stanisław 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 improvement 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 tremendously simple rules in a virtual world in the form of a 2-dimensional checkerboard
.
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
to the dissemination of culture .
In 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
.
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" which is 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). Through the mid-1990s, the field focused on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. With the appearance of StarLogo in 1990, SWARM
and NetLogo
in the mid-1990s and RePast and AnyLogic
in 2000, as well as some custom-designed code, 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. Bonabeau (2002) is a good survey of the potential of agent-based modeling as of the time that its modelling software became widely available.
Kathleen M. Carley
developed an early ABM, Construct , to explore the co-evolution of social networks and culture.
Joshua M. Epstein
and Robert Axtell
developed a large-scale ABM, the Sugarscape
, to simulate and explore the role of social phenomenon such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.
Nigel Gilbert
published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established its most relevant journal: the Journal of Artificial Societies and Social Simulation
.
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
, of Carnegie Mellon University
, 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. Nowadays, 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.
More recently, Ron Sun
developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation (see Sun 2006). 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.
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.
These agents are:
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, the 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.
Other methods of describing agent-based models include code templates and text-based methods such as the ODD protocol.
and logistics
, modeling of consumer behavior, including word of mouth
, social network
effects, distributed computing
, workforce management
, and portfolio management
. They have also been used to analyze traffic congestion
. In these and other applications, the system of interest is simulated by capturing the behavior of individual agents and their interconnections. Agent-based modeling tools can be used to test how changes in individual behaviors will affect the system's emerging overall behavior.
Other models have analyzed the spread of epidemics, the threat of biowarfare, biological applications
including population dynamics , the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, and biomedical applications including inflammation and the human immune system
. Agent-based models have also been used for developing decision support systems such as for breast cancer.
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 has been used to simulate information delivery in ambient assisted environments. 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 Computer Science based Formal Specification framework coupled with Wireless sensor networks and an Agent-based simulation has recently been demonstrated in.
Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems. Further details on the topic can be found in R. Sarker and T. Ray (2010) Agent based Evolutionary Approach: An Introduction, Agent Based Evolutionary Search, Springer series in Evolutionary Learning and Optimization, Springer, pp. 1–12.
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. A July 2010 article in The Economist
looked at ABMs as alternatives to the DGSE models. 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 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. 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.
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).
A formal approach for V&V of all agent-based models is based on building a 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. The agents in the Multi-Agent System are able to gather data by generation of logs as well as provide run-time validation and verification support by watch agents and also agents to check any violation of invariants at run-time. These are set by the Simulation Specialist with help from the SME (Subject Matter Expert). An example of using VOMAS for Verification and Validation of a Forest Fire simulation model is given in
VOMAS provides a formal way of Validation and Verification. If you want to develop a VOMAS, you need to start by designing VOMAS agents along with the agents in the actual simulation preferably from the start. So, in essence, by the time your simulation model is complete, you essentially can consider to have one model which contains two models:
Unlike all previous work on Verification and Validation, VOMAS agents ensure that the simulations are validated in-simulation i.e. even during execution. In case of any exceptional situations, which are programmed on the directive of the Simulation Specialist (SS), the VOMAS agents can report them. In addition, the VOMAS agents can be used to log key events for the sake of debugging and subsequent analysis of simulations. In other words, VOMAS allows for a flexible use of any given technique for the sake of Verification and Validation of an Agent-based Model in any domain.
Details of Validated agent-based modeling using VOMAS along with several case studies are given in . This thesis also gives details of "Exploratory Agent-based Modeling", "Descriptive Agent-based Modeling" in addition to "Validated Agent-based Modeling" using several worked case study examples.
Multi-agent system
A multi-agent system is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve...
or multi-agent simulation) is a class of computational models for simulating
Computer simulation
A computer simulation, a computer model, or a computational model is a computer program, or network of computers, that attempts to simulate an abstract model of a particular system...
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
Game theory
Game theory is a mathematical method for analyzing calculated circumstances, such as in games, where a person’s success is based upon the choices of others...
, complex systems
Complex systems
Complex systems present problems in mathematical modelling.The equations from which complex system models are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of systems of nature that are considered...
, emergence
Emergence
In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems....
, computational sociology
Computational sociology
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and new analytic approaches like social network analysis, computational sociology...
, multi-agent system
Multi-agent system
A multi-agent system is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve...
s, and evolutionary programming
Evolutionary programming
Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve....
. Monte Carlo Method
Monte Carlo method
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in computer simulations of physical and mathematical systems...
s are used to introduce randomness. ABMs are also called individual-based models. A review of recent literature on individual-based models, agent-based models and multiagent systems is given in
The models 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
Emergence
In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems....
from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S.
KISS principle
KISS is an acronym for the design principle Keep it simple, Stupid!. Other variations include "keep it simple and stupid", "keep it short and simple", "keep it simple sir", "keep it simple or be stupid" or "keep it simple and straightforward"...
("Keep it simple and short") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. 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) a non-agent environment.
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 history of the agent-based model can be traced back to the Von Neumann machine
Von Neumann machine
Von Neumann machine may refer to:.* Von Neumann architecture, a conceptual model of a computer architecture* The IAS machine, a computer designed in the 1940s based on von Neuman's design...
, 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 improved by von Neumann's friend Stanisław 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 improvement was introduced by the mathematician John Conway
John Horton Conway
John Horton Conway is a prolific mathematician active in the theory of finite groups, knot theory, number theory, combinatorial game theory and coding theory...
. He constructed the well-known Game of Life
Conway's Game of Life
The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970....
. Unlike von Neumann's machine, Conway's Game of Life operated by tremendously simple rules in a virtual world in the form of a 2-dimensional checkerboard
Checkerboard
A checkerboard or chequerboard is a board of chequered pattern on which English draughts is played. It is an 8×8 board and the 64 squares are of alternating dark and light color, often red and black....
.
One of the earliest agent-based models in concept was Thomas Schelling
Thomas Schelling
Thomas Crombie Schelling is an American economist and professor of foreign affairs, national security, nuclear strategy, and arms control at the School of Public Policy at University of Maryland, College Park. He is also co-faculty at the New England Complex Systems Institute...
'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
Robert Axelrod
Robert M. Axelrod is an American political scientist. He is Professor of Political Science and Public Policy at the University of Michigan where he has been since 1974. He is best known for his interdisciplinary work on the evolution of cooperation, which has been cited in numerous articles...
hosted a tournament of Prisoner's Dilemma
Prisoner's dilemma
The prisoner’s dilemma is a canonical example of a game, analyzed in game theory that shows why two individuals might not cooperate, even if it appears that it is in their best interest to do so. It was originally framed by Merrill Flood and Melvin Dresher working at RAND in 1950. Albert W...
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
Ethnocentrism is the tendency to believe that one's ethnic or cultural group is centrally important, and that all other groups are measured in relation to one's own. The ethnocentric individual will judge other groups relative to his or her own particular ethnic group or culture, especially with...
to the dissemination of culture .
In the late 1980s, Craig Reynolds
Craig Reynolds (computer graphics)
Craig W. Reynolds , is an artificial life and computer graphics expert, who created the Boids artificial life simulation in 1986. Reynolds worked on the film Tron as a scene programmer, and on Batman Returns as part of the video image crew. He is the author of the OpenSteer library.-External...
' 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
Artificial life
Artificial life is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986...
, a term coined by Christopher Langton
Christopher Langton
Christopher Langton 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 Simulation of Living Systems" at the Los Alamos National Laboratory in 1987.Langton made...
.
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
John Henry Holland
John Henry Holland is an American scientist and Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He is a pioneer in complex systems and nonlinear science. He is known as the father of genetic algorithms. He was awarded...
and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory" which is 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). Through the mid-1990s, the field focused on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. With the appearance of StarLogo in 1990, SWARM
Swarm (simulation)
Swarm is the name of a multi-agent simulation package, useful for simulating the interaction of agents and their emergent collective behaviour. Swarm was initially developed at the Santa Fe Institute in the mid-1990s, and since 1999 has been maintained by the non-profit Swarm Development...
and NetLogo
NetLogo
NetLogo is a multi-agent programming language and integrated modeling environment.-About:NetLogo was designed in the spirit of the Logo programming language to be "low threshold and no ceiling," that is to enable easy entry by novices and yet meet the needs of high powered users. The NetLogo...
in the mid-1990s and RePast and AnyLogic
AnyLogic
-History of AnyLogic:In the beginning of 1990s there was a big interest in the mathematical approach to modeling and simulation of parallel processes. This approach may be applied to the analysis of correctness of parallel and distributed programs...
in 2000, as well as some custom-designed code, 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. Bonabeau (2002) is a good survey of the potential of agent-based modeling as of the time that its modelling software became widely available.
Kathleen M. Carley
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 International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the...
developed an early ABM, Construct , to explore the co-evolution of social networks and culture.
Joshua M. Epstein
Joshua M. Epstein
Joshua M. Epstein is Professor of Emergency Medicine at Johns Hopkins University, and a member of the External Faculty of the Santa Fe Institute.- Early life and Education:Epstein was born in New York City and grew up in Amherst....
and Robert Axtell
Robert Axtell
- References :...
developed a large-scale ABM, the Sugarscape
Sugarscape
Sugarscape is a model artificially intelligent agent-based social simulation following some or all rules presented by Joshua M. Epstein & Robert Axtell in their book Growing Artificial Societies.-Origin:...
, to simulate and explore the role of social phenomenon such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.
Nigel Gilbert
Nigel Gilbert
Nigel Gilbert 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 , author of several books on computational social sciences, social simulation and social research and editor...
published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established its most relevant journal: the Journal of Artificial Societies and Social Simulation
Journal of Artificial Societies and Social Simulation
The Journal of Artificial Societies and Social Simulation is a quarterly peer-reviewed academic journal created and edited by Nigel Gilbert . The journal publishes articles in computational sociology, social simulation, complexity science, and artificial societies. Its approach is...
.
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
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 International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the...
, of Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
, was a major contributor, especially to models of social networks, obtaining National Science Foundation
National Science Foundation
The National Science Foundation is a United States government agency that supports fundamental research and education in all the non-medical fields of science and engineering. Its medical counterpart is the National Institutes of Health...
funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the University of Chicago
University of Chicago
The University of Chicago is a private research university in Chicago, Illinois, USA. It was founded by the American Baptist Education Society with a donation from oil magnate and philanthropist John D. Rockefeller and incorporated in 1890...
and Argonne National Laboratory
Argonne National Laboratory
Argonne National Laboratory is the first science and engineering research national laboratory in the United States, receiving this designation on July 1, 1946. It is the largest national laboratory by size and scope in the Midwest...
, and then by Michael Prietula of Emory University
Emory University
Emory University is a private research university in metropolitan Atlanta, located in the Druid Hills section of unincorporated DeKalb County, Georgia, United States. The university was founded as Emory College in 1836 in Oxford, Georgia by a small group of Methodists and was named in honor 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. Nowadays, 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
George Mason University
George Mason University is a public university based in unincorporated Fairfax County, Virginia, United States, south of and adjacent to the city of Fairfax. Additional campuses are located nearby in Arlington County, Prince William County, and Loudoun County...
taking the lead role in local arrangements.
More recently, Ron Sun
Ron Sun
Ron Sun is a cognitive scientist and currently Professor of Cognitive Science at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri...
developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation (see Sun 2006). 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 equilibriumSteady state
A system in a steady state has numerous properties that are unchanging in time. This implies that for any property p of the system, the partial derivative with respect to time is zero:...
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
Emergence
In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems....
, and complexity
Complexity
In general usage, complexity tends to be used to characterize something with many parts in intricate arrangement. The study of these complex linkages is the main goal of complex systems theory. In science there are at this time a number of approaches to characterizing complexity, many of which are...
.
Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity.
These agents are:
- Intelligent and purposeful.
- SituatedSituatedIn 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 can also be situated if:...
in space and time. They reside in networks and in lattice-like neighborhoods. The location of the agents and their responsive and purposeful behavior are encoded in algorithmAlgorithmIn mathematics and computer science, an algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Algorithms are used for calculation, data processing, and automated reasoning...
ic form in computer programs. The modeling process is best described as inductiveInductive reasoningInductive reasoning, also known as induction or inductive logic, is a kind of reasoning that constructs or evaluates propositions that are abstractions of observations. It is commonly construed as a form of reasoning that makes generalizations based on individual instances...
. 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
Internet
The Internet is a global system of interconnected computer networks that use the standard Internet protocol suite to serve billions of users worldwide...
, 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, the 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. describes 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 protocol.
Applications
Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include supply chain optimizationSupply chain optimization
Supply chain optimization is the application of processes and tools to ensure the optimal operation of a manufacturing and distribution supply chain. This includes the optimal placement of inventory within the supply chain, minimizing operating costs...
and logistics
Logistics
Logistics is the management of the flow of goods between the point of origin and the point of destination in order to meet the requirements of customers or corporations. Logistics involves the integration of information, transportation, inventory, warehousing, material handling, and packaging, and...
, modeling of consumer behavior, including word of mouth
Word of mouth
Word of mouth, or viva voce, is the passing of information from person to person by oral communication. Storytelling is the oldest form of word-of-mouth communication where one person tells others of something, whether a real event or something made up. Oral tradition is cultural material and...
, social network
Social network
A social network is a social structure made up of individuals called "nodes", which are tied by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.Social...
effects, distributed computing
Distributed computing
Distributed computing is a field of computer science that studies distributed systems. A distributed system consists of multiple autonomous computers that communicate through a computer network. The computers interact with each other in order to achieve a common goal...
, workforce management
Workforce management
Workforce management encompasses all the activities needed to maintain a productive workforce. Sometimes referred to as HRMS systems, or even part of ERP systems...
, and portfolio management
Investment management
Investment management is the professional management of various securities and assets in order to meet specified investment goals for the benefit of the investors...
. They have also been used to analyze traffic congestion
Traffic congestion
Traffic congestion is a condition on road networks that occurs as use increases, and is characterized by slower speeds, longer trip times, and increased vehicular queueing. The most common example is the physical use of roads by vehicles. When traffic demand is great enough that the interaction...
. In these and other applications, the system of interest is simulated by capturing the behavior of individual agents and their interconnections. Agent-based modeling tools can be used to test how changes in individual behaviors will affect the system's emerging overall behavior.
Other models have analyzed the spread of epidemics, the threat of biowarfare, biological applications
Agent-based model in biology
Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system...
including population dynamics , the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, and biomedical applications including inflammation and the human immune system
Immune system
An immune system is a system of biological structures and processes within an organism that protects against disease by identifying and killing pathogens and tumor cells. It detects a wide variety of agents, from viruses to parasitic worms, and needs to distinguish them from the organism's own...
. Agent-based models have also been used for developing decision support systems such as for breast cancer.
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 has been used to simulate information delivery in ambient assisted environments. 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 Computer Science based Formal Specification framework coupled with Wireless sensor networks and an Agent-based simulation has recently been demonstrated in.
Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems. Further details on the topic can be found in R. Sarker and T. Ray (2010) Agent based Evolutionary Approach: An Introduction, Agent Based Evolutionary Search, Springer series in Evolutionary Learning and Optimization, Springer, pp. 1–12.
Economics
Prior to and in the wake of the financial crisis interest has grown in ABMs as possible tools for economic analysis. ABMs do not assume the economy can achieve equilibrium and "representative agentRepresentative agent
Economists use the term representative agent to refer to the typical decision-maker of a certain type ....
s" are replaced by agents with diverse, dynamic, and interdependent behavior including herding
Herding
Herding is the act of bringing individual animals together into a group , maintaining the group and moving the group from place to place—or any combination of those. While the layperson uses the term "herding", most individuals involved in the process term it mustering, "working stock" or...
. 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
Linear
In mathematics, a linear map or function f is a function which satisfies the following two properties:* Additivity : f = f + f...
(disproportionate) responses to proportionally small changes. A July 2010 article in The Economist
The Economist
The Economist is an English-language weekly news and international affairs publication owned by The Economist Newspaper Ltd. and edited in offices in the City of Westminster, London, England. Continuous publication began under founder James Wilson in September 1843...
looked at ABMs as alternatives to the DGSE models. The journal Nature
Nature (journal)
Nature, first published on 4 November 1869, is ranked the world's most cited interdisciplinary scientific journal by the Science Edition of the 2010 Journal Citation Reports...
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
J. Doyne Farmer is an American physicist and entrepreneur, with interest in chaos theory and complexity. He is a professor at the Santa Fe Institute. He was also a member of Eudaemonic Enterprises.-Biography:...
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.
Agent-directed simulation
The agent-directed simulation (ADS) http://www.eng.auburn.edu/~yilmaz/ADS.html 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).
Hardware
The Software described above is designed for serial von-Neumann computer architectures. This limits the speed and scalability of these systems. A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation http://rudomin.cem.itesm.mx/~rudomin/shader-agents.htm, http://www.me.mtu.edu/~rmdsouza/ABM_GPU.html and http://www.dcs.shef.ac.uk/~paul/abgpu.html. 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.Verification and validation of agent-based models
Verification and validation (V&V) of simulation models is extremely important. Verification involves debugging the model to ensure it works correctly; whereas Validation ensures that you have built the right model. Verification and validation in the social sciences domain can be seen in. In Computational Economics, validation can be examined in. In, the author proposes face validation, sensitivity analysis, calibration and statistical validation. Discrete-Event Simulation Framework approach for the validation of Agent-Based systems has been proposed in. A comprehensive resource on empirical validation of agent-based models isA formal approach for V&V of all agent-based models is based on building a 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. The agents in the Multi-Agent System are able to gather data by generation of logs as well as provide run-time validation and verification support by watch agents and also agents to check any violation of invariants at run-time. These are set by the Simulation Specialist with help from the SME (Subject Matter Expert). An example of using VOMAS for Verification and Validation of a Forest Fire simulation model is given in
VOMAS provides a formal way of Validation and Verification. If you want to develop a VOMAS, you need to start by designing VOMAS agents along with the agents in the actual simulation preferably from the start. So, in essence, by the time your simulation model is complete, you essentially can consider to have one model which contains two models:
- An Agent Based Model of the intended system
- An Agent Based Model of the VOMAS
Unlike all previous work on Verification and Validation, VOMAS agents ensure that the simulations are validated in-simulation i.e. even during execution. In case of any exceptional situations, which are programmed on the directive of the Simulation Specialist (SS), the VOMAS agents can report them. In addition, the VOMAS agents can be used to log key events for the sake of debugging and subsequent analysis of simulations. In other words, VOMAS allows for a flexible use of any given technique for the sake of Verification and Validation of an Agent-based Model in any domain.
Details of Validated agent-based modeling using VOMAS along with several case studies are given in . This thesis also gives details of "Exploratory Agent-based Modeling", "Descriptive Agent-based Modeling" in addition to "Validated Agent-based Modeling" using several worked case study examples.
See also
- Agent-Based Computational EconomicsAgent-Based Computational EconomicsAgent-based computational economics is the major aspect of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in paradigm of complex adaptive systems...
- Agent-based model in biologyAgent-based model in biologyAgent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system...
- agent-based social simulationAgent-based social simulationAgent-based social simulation consists in social simulations that are based on Agent-based modeling, and implemented using artificial agent technologies....
(ABSS) - ACEGESACEGESThe ACEGES is a decision-support tool for energy policy by means of controlled computational experiments. The ACEGES tool is designed to be the foundation for large custom-purpose simulations of the global energy system...
- artificial lifeArtificial lifeArtificial life is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986...
- artificial societyArtificial societyArtificial Society is the specific agent based computational model for computer simulation in social analysis. It is mostly connected to the theme in complex system, emergence, Monte Carlo Method, computational sociology, multi-agent system, and evolutionary programming. The concept itself is...
- boidsBoidsBoids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference...
- Comparison of agent-based modeling software
- complex systemComplex systemA complex system is a system composed of interconnected parts that as a whole exhibit one or more properties not obvious from the properties of the individual parts....
- complex adaptive systemComplex adaptive systemComplex adaptive systems are special cases of complex systems. They are complex in that they are dynamic networks of interactions and relationships not aggregations of static entities...
- computational sociologyComputational sociologyComputational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and new analytic approaches like social network analysis, computational sociology...
- Conway's Game of LifeConway's Game of LifeThe Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970....
- dynamic network analysisDynamic Network AnalysisDynamic network analysis is an emergent scientific field that brings together traditional social network analysis , link analysis and multi-agent systems within network science and network theory. There are two aspects of this field. The first is the statistical analysis of DNA data. The second...
- emergenceEmergenceIn philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems....
- evolutionary algorithmEvolutionary algorithmIn artificial intelligence, an evolutionary algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection...
- flockingFlocking (behavior)Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, the swarming behavior of insects, and herd behavior of land animals....
- Generative sciencesGenerative sciencesThe generative science is a interdisciplinary and multidisciplinary science that explores the natural world and its complex behaviours as a generative process...
- human fit
- Kinetic exchange models of marketsKinetic exchange models of marketsKinetic exchange models are multi-agent dynamic models inspired by the statistical physics of energy distribution, which try to explain the robust and universal features of income/wealth distributions....
- Multi-agent systemMulti-agent systemA multi-agent system is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve...
- simulated realitySimulated realitySimulated reality is the proposition that reality could be simulated—perhaps by computer simulation—to a degree indistinguishable from "true" reality. It could contain conscious minds which may or may not be fully aware that they are living inside a simulation....
- social complexitySocial complexityIn the discipline of sociology, social complexity is a theoretical construct useful in the analysis of society.- Overview :Contemporary definitions of complexity in the sciences are found in relation to systems theory, where a phenomenon under study has many parts and many possible arrangements of...
- social simulationSocial simulationSocial simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in sociology, political science, economics, anthropology, geography, archaeology and linguistics ....
- software agentSoftware agentIn computer science, a software agent is a piece of software that acts for a user or other program in a relationship of agency, which derives from the Latin agere : an agreement to act on one's behalf...
- Swarming behaviour
- Swarm Development GroupSwarm Development GroupThe Swarm Development Group is an American non-profit organization to advance the development of complex adaptive system-oriented agent-based modeling tools initiated at the Santa Fe Institute in Santa Fe, New Mexico, USA. It was formed in 1999 by a group of multidisciplinary scientists,...
- Web-based simulation
- Janus (multi-agent and holonic platform)Janus (multi-agent and holonic platform)Janus is the name of a multiagent simulation platform, useful for simulating the interaction of agents and their emergent collective behavior...
General
}}- Bonabeau, Eric, Agent-based modeling: methods and techniques for simulating human systems. Proc. National Academy of Sciences 99(3): 7280–7287, 2002.
- Carley, Kathleen M.Kathleen CarleyKathleen 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 International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the...
, Smart Agents and Organizations of the Future. In Handbook of New Media, edited by Leah Lievrouw & Sonia Livingstone, Ch. 12 pp. 206–220, Thousand Oaks, CA, Sage. http://www.casos.cs.cmu.edu/publications/Abstracts_All/SmartAgents_abstract.html
- Epstein, Joshua M. and Robert Axtell, Growing Artificial Societies: Social Science From the Bottom Up. MIT Press/Brookings Institution, 1996.http://www.amazon.com/dp/0262050536
- Gilbert, Nigel, and Klaus Troitzsch, Simulation for the Social Scientist, Open University Press, 1999; second edition, 2005.
- Grimm, Volker, and Steven F. Railsback, Individual-based Modeling and Ecology, Princeton University Press, 2005.
- Holland, John H., "Genetic Algorithms," Scientific American, 267:66–72, 1992.
- Holland, John H., Hidden Order: How Adaptation Builds Complexity, Addison-Wesley:Reading, Mass., 1995.
- Miller, John H. and Page, Scott E., Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press:Princeton, NJ, 2007.
- V.K.Murthy and E.V.Krishnamurthy (2009)," Multiset of Agents in a Network for Simulation of Complex Systems", in "Recent advances in Nonlinear Dynamics and synchronization, (NDS-1) – Theory and applications, Springer Verlag, New York,2009. Eds. K.Kyamakya et al.
- O'Sullivan,D. and Haklay, M., Agent-based models and individualism: Is the world agent-based? Environment and Planning A, 32:1409–25, 2000.
- Naldi G., Pareschi L., Toscani G., "Mathematical modeling of collective behavior in socio-economic and life sciences", Birkhauser, 2010. http://www.springer.com/birkhauser/mathematics/book/978-0-8176-4945-6
- Preis, Tobias et al., "Multi-agent-based Order Book Model of financial markets", Europhysics Letters 75:510-516, 2006. http://dx.doi.org/10.1209/epl/i2006-10139-0
- Rudomin, B. Hernandez, E. Millan, Fragment shaders for agent animation using finite state machines, In Simulation Modelling Practice and Theory Journal, Volume 13, Issue 8 , Programmable Graphics Hardware November 2005, Pages 741-751 Elsevier,
- Sallach, David, and Charles Macal, The simulation of social agents: an introduction, Special Issue of Social Science Computer Review 19(3):245–248, 2001.
- Samuelson, Douglas A., “Designing Organizations,” OR/MS Today, December 2000.
- Samuelson, Douglas A., “Agents of Change,” OR/MS Today, February 2005.
- Samuelson, Douglas A. and Charles M. Macal, “Agent-Based Modeling Comes of Age,” OR/MS Today, August 2006.
- Shoham, Yoav, and Kevin Leyton-Brown, "Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations". Cambridge University Press, 2009.
- Sun, Ron, Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006. http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=0521839645
External links
Articles/General Information- From System Dynamics and Discrete Event to Practical Agent-Based Modeling: Reasons, Techniques, Tools Compares the three major methods in simulation modeling
- Agent-based models of social networks, java applets.
- On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences
- Introduction to Agent-based Modeling and Simulation. Argonne National LaboratoryArgonne National LaboratoryArgonne National Laboratory is the first science and engineering research national laboratory in the United States, receiving this designation on July 1, 1946. It is the largest national laboratory by size and scope in the Midwest...
, November 29, 2006. - Agent-based models in Ecology - Using computer models as theoretical tools to analyze complex ecological systems
- Open Agent-Based Modeling Consortium's Agent Based Modeling FAQ
- Multiagent Information Systems - 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