Agent-Based Modeling and Network Dynamics

Agent-Based Modeling and Network Dynamics

Author: Akira Namatame

Publisher: Oxford University Press

Published: 2016-01-28

Total Pages: 294

ISBN-13: 0191074993

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While the significance of networks in various human behavior and activities has a history as long as human's existence, network awareness is a recent scientific phenomenon. The neologism network science is just one or two decades old. Nevertheless, with this limited time, network thinking has substantially reshaped the recent development in economics, and almost all solutions to real-world problems involve the network element. This book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The text also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. It reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, such as diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. At the end, the book considers two challenging issues using agent-based models of networks: network risks and economic growth.


Agent-Based Modeling and Network Dynamics

Agent-Based Modeling and Network Dynamics

Author: Akira Namatame

Publisher: Oxford University Press

Published: 2016-01-28

Total Pages: 341

ISBN-13: 0191017981

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While the significance of networks in various human behavior and activities has a history as long as human's existence, network awareness is a recent scientific phenomenon. The neologism network science is just one or two decades old. Nevertheless, with this limited time, network thinking has substantially reshaped the recent development in economics, and almost all solutions to real-world problems involve the network element. This book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The text also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. It reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, such as diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. At the end, the book considers two challenging issues using agent-based models of networks: network risks and economic growth.


Network Theory and Agent-Based Modeling in Economics and Finance

Network Theory and Agent-Based Modeling in Economics and Finance

Author: Anindya S. Chakrabarti

Publisher: Springer Nature

Published: 2019-10-23

Total Pages: 454

ISBN-13: 9811383197

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This book presents the latest findings on network theory and agent-based modeling of economic and financial phenomena. In this context, the economy is depicted as a complex system consisting of heterogeneous agents that interact through evolving networks; the aggregate behavior of the economy arises out of billions of small-scale interactions that take place via countless economic agents. The book focuses on analytical modeling, and on the econometric and statistical analysis of the properties emerging from microscopic interactions. In particular, it highlights the latest empirical and theoretical advances, helping readers understand economic and financial networks, as well as new work on modeling behavior using rich, agent-based frameworks. Innovatively, the book combines observational and theoretical insights in the form of networks and agent-based models, both of which have proved to be extremely valuable in understanding non-linear and evolving complex systems. Given its scope, the book will capture the interest of graduate students and researchers from various disciplines (e.g. economics, computer science, physics, and applied mathematics) whose work involves the domain of complexity theory.


An Introduction to Agent-Based Modeling

An Introduction to Agent-Based Modeling

Author: Uri Wilensky

Publisher: MIT Press

Published: 2015-04-03

Total Pages: 505

ISBN-13: 0262731894

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A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline. The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.


Agent-based Modeling and Network Dynamics

Agent-based Modeling and Network Dynamics

Author: Akira Namatame

Publisher: Oxford University Press

Published: 2016

Total Pages: 341

ISBN-13: 0198708289

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The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The text shows that the modern network science mainly driven by game-theorists andsociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks.


Agent-Based Modelling and Geographical Information Systems

Agent-Based Modelling and Geographical Information Systems

Author: Andrew Crooks

Publisher: SAGE Publications Limited

Published: 2019-01-16

Total Pages: 0

ISBN-13: 9781473958654

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This is the era of Big Data and computational social science. It is an era that requires tools which can do more than visualise data but also model the complex relation between data and human action, and interaction. Agent-Based Models (ABM) - computational models which simulate human action and interaction – do just that. This textbook explains how to design and build ABM and how to link the models to Geographical Information Systems. It guides you from the basics through to constructing more complex models which work with data and human behaviour in a spatial context. All of the fundamental concepts are explained and related to practical examples to facilitate learning (with models developed in NetLogo with all code examples available on the accompanying website). You will be able to use these models to develop your own applications and link, where appropriate, to Geographical Information Systems. All of the key ideas and methods are explained in detail: geographical modelling; an introduction to ABM; the fundamentals of Geographical Information Science; why ABM and GIS; using QGIS; designing and building an ABM; calibration and validation; modelling human behavior. An applied primer, that provides fundamental knowledge and practical skills, it will provide you with the skills to build and run your own models, and to begin your own research projects.


Evolutionary Game Dynamics

Evolutionary Game Dynamics

Author: American Mathematical Society. Short Course

Publisher: American Mathematical Soc.

Published: 2011-10-27

Total Pages: 186

ISBN-13: 0821853260

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This volume is based on lectures delivered at the 2011 AMS Short Course on Evolutionary Game Dynamics, held January 4-5, 2011 in New Orleans, Louisiana. Evolutionary game theory studies basic types of social interactions in populations of players. It combines the strategic viewpoint of classical game theory (independent rational players trying to outguess each other) with population dynamics (successful strategies increase their frequencies). A substantial part of the appeal of evolutionary game theory comes from its highly diverse applications such as social dilemmas, the evolution of language, or mating behaviour in animals. Moreover, its methods are becoming increasingly popular in computer science, engineering, and control theory. They help to design and control multi-agent systems, often with a large number of agents (for instance, when routing drivers over highway networks or data packets over the Internet). While these fields have traditionally used a top down approach by directly controlling the behaviour of each agent in the system, attention has recently turned to an indirect approach allowing the agents to function independently while providing incentives that lead them to behave in the desired way. Instead of the traditional assumption of equilibrium behaviour, researchers opt increasingly for the evolutionary paradigm and consider the dynamics of behaviour in populations of agents employing simple, myopic decision rules.


Dynamic Social Networks in Agent-based Modelling

Dynamic Social Networks in Agent-based Modelling

Author: Holzhauer, Sascha

Publisher: kassel university press GmbH

Published: 2017

Total Pages: 416

ISBN-13: 373760262X

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Agent-based modelling enables the explicit representation of entities and their interaction with each other and the environment, and so it became an important method to study complex systems. Social networks form an important part of agent-based social simulation, as they define the topology of agent interaction. This dissertation initially identifies important properties of social networks and their dynamics and reviews their representation in agent-based models of relevant domains. A classification of levels of detail for the network modelling components initialisation, dynamics of networks, and dynamics on networks is proposed and guides the identification of deficits. A formal, iterative evaluation framework is developed to quantitatively assess network modelling approaches under a set of weighted criteria (representativity, adjustability, validity, and efficiency). The framework is applied to an abstract model of opinion dynamics and to an empirically grounded model of social influence. A lifestyle-specific network survey is designed, conducted, and analysed and helps to ground the evaluation of the network modelling’s representativity on empirical data. The study finds significant differences of degree and distance distributions as well as in the composition of ego networks between lifestyles. New network modelling approaches are developed to account for requirements in agent-based models such as agent-type specific link preferences, degree and distance distributions, community structures, and interaction dynamics. The comparison of simple to elaborated network modelling for the application models shows a significant impact on simulation results, highlighting the need for informed decisions about suitable approaches.


Assessing the Use of Agent-Based Models for Tobacco Regulation

Assessing the Use of Agent-Based Models for Tobacco Regulation

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2015-07-17

Total Pages: 269

ISBN-13: 0309317258

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Tobacco consumption continues to be the leading cause of preventable disease and death in the United States. The Food and Drug Administration (FDA) regulates the manufacture, distribution, and marketing of tobacco products - specifically cigarettes, cigarette tobacco, roll-your-own tobacco, and smokeless tobacco - to protect public health and reduce tobacco use in the United States. Given the strong social component inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the potential to be an essential tool in assessing the effects of policies to control tobacco. Assessing the Use of Agent-Based Models for Tobacco Regulation describes the complex tobacco environment; discusses the usefulness of agent-based models to inform tobacco policy and regulation; presents an evaluation framework for policy-relevant agent-based models; examines the role and type of data needed to develop agent-based models for tobacco regulation; provides an assessment of the agent-based model developed for FDA; and offers strategies for using agent-based models to inform decision making in the future.