Tutorial on agent-based modelling and simulation
TL;DR: A brief introduction to ABMS is provided, the main concepts and foundations are illustrated, some recent applications across a variety of disciplines are discussed, and methods and toolkits for developing agent models are identified.
Abstract: Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.
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TL;DR: Development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library, are described.
Abstract: This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years. Includes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration. Include a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system. We discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.
506 citations
Cites methods from "Tutorial on agent-based modelling a..."
...Agent-based modeling provides a mechanism for modeling CASs (Bonabeau 2002; Macal and North 2010)....
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TL;DR: In this paper, the authors present the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently.
473 citations
TL;DR: A significant synthesis of Agent Based Modelling and Simulation (ABMS) resources has been performed in this review that stimulates further investigation into this topic.
445 citations
Cites methods from "Tutorial on agent-based modelling a..."
...The modellers create simulation models by making a series of calls, thereby invoking various built-in functions within the modelling toolkit [51]....
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...The IDE approach also narrates inherentmechanism to interpret, compile and run the simulation models [51]....
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TL;DR: This series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state–transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently.
Abstract: Models-mathematical frameworks that facilitate estimation of the consequences of health care decisions-have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR modeling task force reported in 2003 has led to a new task force, jointly convened with the Society for Medical Decision Making, and this series of seven papers presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; dealing with uncertainty; and validating and reporting models transparently. This overview introduces the work of the task force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these papers includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.
352 citations
Cites methods from "Tutorial on agent-based modelling a..."
...This report defined a model and its purpose, laid out the approach to evaluating a model, and described the task force’s consensus regarding the attributes that characterize a good model, in terms of structure, data, and validation.14 In the intervening years, the range of modeling techniques for…...
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TL;DR: The issue of ABMS represents as a new development is revisited, considering the extremes of being an overhyped fad, doomed to disappear, or a revolutionary development, shifting fundamental paradigms of how research is conducted.
Abstract: This paper addresses the background and current state of the field of agent-based modelling and simulation (ABMS). It revisits the issue of ABMS represents as a new development, considering the ext...
309 citations
Cites background from "Tutorial on agent-based modelling a..."
...There is no universal agreement on the precise definition of the term agent, or, by extension, on the term agentbased model (Macal and North, 2010)....
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...Applications range across virtually all disciplines in the natural, social, and physical sciences as well as engineered systems and well beyond the usual ones for simulation in engineering, business, operations management, and similar fields (Macal and North, 2010, 2014)....
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References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.
33,034 citations
Book•
17 Aug 2006TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
22,840 citations
TL;DR: This work characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links that connect them.
Abstract: Social Network Analysis Methods And Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network ...
12,634 citations
"Tutorial on agent-based modelling a..." refers background in this paper
...Social Network Analysis (SNA) is a field with a long history that studies the characterization and analysis of social structure and interaction through network representations (Wasserman and Faust 1994)....
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