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Agent-based model

agent-based model, agent-based model human immune systems
An agent-based model ABM is one of 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 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 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 KISS "Keep it simple, stupid", 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 an environment ABMs are typically implemented as computer simulations, 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


  • 1 History
    • 11 Early developments
    • 12 1970s and 1980s: the first models
    • 13 1990s: expansion
    • 14 2000s and later
  • 2 Theory
    • 21 Framework
  • 3 Applications
    • 31 In biology
    • 32 In business, technology and network theory
    • 33 In economics and social sciences
    • 34 Organizational ABM: agent-directed simulation
  • 4 Implementation
  • 5 Verification and validation
  • 6 See also
  • 7 References
    • 71 Inline
    • 72 General
  • 8 External links
    • 81 Articles/general Information
    • 82 Simulation models


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

Early developments

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself The concept was then improved 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 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

1970s and 1980s: the first models

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971 Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome

In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics He tried to model the reality of lively biological agents, known as artificial life, a term coined by Christopher Langton

The first use of the word "agent" and a definition as it is currently used today is hard to track down One candidate appears to be John Holland and John H Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory CMOT This field grew as a special interest group of The Institute of Management Sciences TIMS and its sister society, the Operations Research Society of America ORSA

1990s: expansion

With the appearance of StarLogo in 1990, Swarm and NetLogo in the mid-1990s and RePast and AnyLogic in 2000, or GAMA in 2007 as well as some custom-designed code, modelling software became widely available and the range of domains that ABM was applied to, grew Bonabeau 2002 is a good survey of the potential of agent-based modeling as of the time

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 Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM, 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

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks CMOT—later renamed Computational Analysis of Social and Organizational Systems CASOS—incorporated more and more agent-based modeling Samuelson 2000 is a good brief overview of the early history, and Samuelson 2005 and Samuelson and Macal 2006 trace the more recent developments

In the late 1990s, the merger of TIMS and ORSA to form INFORMS, 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 The Second World Congress was held in the northern Virginia suburbs of Washington, DC, in July 2008, with George Mason University taking the lead role in local arrangements

2000s and later

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation 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 In 2014, Sadegh Asgari from Columbia University and his colleagues developed an agent-based model of the construction competitive bidding While his model were used to analyze the low-bid lump-sum construction bids, it could be applied to other bidding methods with little modifications to the model


Most computational modeling research describes systems in equilibrium or as moving between equilibria Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior The three ideas central to agent-based models are agents as objects, emergence, and complexity

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 algorithmic 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 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

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


Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

  1. Complex Network Modeling Level for developing models using interaction data of various system components
  2. Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research This can eg be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers
  3. 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
  4. Validated agent-based modeling using Virtual Overlay Multiagent system VOMAS for the development of verified and validated models in a formal manner

Other methods of describing agent-based models include code templates and text-based methods such as the ODD Overview, Design concepts, and Design Details protocol

The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work Simple environment affords simple agents, but complex environments generates diversity of behaviour


In biology

Main article: Agent-based model in biology

Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics, and the threat of biowarfare, biological applications including population dynamics, vegetation ecology, landscape diversity, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation, and the human immune system Agent-based models have also been used for developing decision support systems such as for breast cancer Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori Military applications have also been evaluated Moreover, agent-based models have been recently employed to study molecular-level biological systems

In business, technology and network theory

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems Examples of applications include the modeling of organizational behaviour and cognition, team working, supply chain optimization 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

Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain journals versus conferences In addition, ABMs have been used to simulate information delivery in ambient assisted environments A November 2016 article in arXiv analyzed an agent based simulation of posts spread in the Facebook online social network In the domain of peer-to-peer, ad-hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems

In economics and social sciences

Main articles: Agent-based computational economics and Agent-based social simulation Graphic user interface for an agent-based modeling tool

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 agents" 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 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 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

Since the beginning of the 21st century ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network

Organizational ABM: agent-directed simulation

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


Many agent-based modeling software are 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 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

Verification and validation V&V of simulation models is extremely important Verification involves the model being debugged to ensure it works correctly, whereas validation ensures that the right model has been built Face validation, sensitivity analysis, calibration and statistical validation have also been demonstrated A discrete-event simulation framework approach for the validation of agent-based systems has been proposed A comprehensive resource on empirical validation of agent-based models can be found here

As an example of V&V technique, consider VOMAS virtual overlay multi-agent system, a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model 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 Muazi et al also provide an example of using VOMAS for verification and validation of a forest fire simulation model

VOMAS provides a formal way of validation and verification To develop a VOMAS, one must design VOMAS agents along with the agents in the actual simulation, preferably from the start In essence, by the time the simulation model is complete, one can essentially consider it to be one model containing two models:

  1. An agent-based model of the intended system
  2. 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 ie 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" and "validated agent-based modeling", using several worked case study examples

See also

  • Agent-based computational economics
  • Agent-based model in biology
  • Agent-based social simulation ABSS
  • Artificial society
  • Boids
  • Comparison of agent-based modeling software
  • Complex system
  • Complex adaptive system
  • Computational sociology
  • Conway's Game of Life
  • Dynamic network analysis
  • Emergence
  • Evolutionary algorithm
  • Flocking
  • Kinetic exchange models of markets
  • Multi-agent system
  • Simulated reality
  • Social complexity
  • Social simulation
  • Sociophysics
  • Software agent
  • Swarming behaviour
  • Web-based simulation



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External links

Articles/general Information

  • 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 Laboratory, 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
  • An Agent-Based Model of the Flash Crash of May 6, 2010, with Policy Implications, Tommi A Vuorenmaa Valo Research and Trading, Liang Wang University of Helsinki - Department of Computer Science, October, 2013

Simulation models

  • Collection of Agent-Based Models at RunTheModelcom
  • Multi-agent Meeting Scheduling System Model by Qasim Siddique
  • Multi-firm market simulation by Valentino Piana

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