Agent Based Computational Modelling

The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. Examples include population dynamics, evolution of social norms, communication structures, patterns in eco-systems and socio-biology, natural resource management, spread of diseases and development processes. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.

Agent-based Computational Economics using NetLogo explores how researchers can create, use and implement multi-agent computational models in Economics by using NetLogo software platform. Problems of economic science can be solved using multi-agent modelling (MAM). This technique uses a computer model to simulate the actions and interactions of autonomous entities in a network, in order to analyze the effects on the entire economic system. MAM combines elements of game theory, complex systems, emergence and evolutionary programming. The Monte Carlo method is also used in this e-book to introduce random elements. The 11 models presented in this text simulate the simultaneous operations of several agents in an attempt to recreate and predict complex economic phenomena. This e-book explains the topic in a systematic manner, starting with an introduction for readers followed subsequently by methodology and implementation using NetLogo. The volume ends with conclusions based on the results of the experiments presented. The e-book is intended as a concise and vital resource for economists, applied mathematicians, social sciences scientists, systems analysts, operations researchers and numerical analysts

Agent-Based Computational Demography (ABCD) aims at starting a new stream of research among social scientists whose interests lie in understanding demographic behaviour. The book takes a micro-demographic (agent-based) perspective and illustrates the potentialities of computer simulation as an aid in theory building. The chapters of the book, written by leading experts either in demography or in agent-based modelling, address several key questions. Why do we need agent-based computational demography? How can ABCD be applied to the study of migrations, family demography, and historical demography? What are the peculiarities of agent-based models as applied to the demography of human populations? ABCD is of interest to all scientists interested in studying demographic behaviour, as well as to computer scientists and modellers who are looking for a promising field of application.

Most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations. This book: Provides a comprehensive introduction to epistemological, theoretical and methodological features of agent-based modelling in sociology through various discussions and examples. Presents the pros and cons of using agent-based models in sociology. Explores agent-based models in combining quantitative and qualitative aspects, and micro- and macro levels of analysis. Looks at how to pose an agent-based research question, identifying the model building blocks, and how to validate simulation results. Features examples of agent-based models that look at crucial sociology issues. Supported by an accompanying website featuring data sets and code for the models included in the book. Agent-Based Computational Sociology is written in a common sociological language and features examples of models that look at all the traditional explanatory challenges of sociology. Researchers and graduate students involved in the field of agent-based modelling and computer simulation in areas such as social sciences, cognitive sciences and computer sciences will benefit from this book.

The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. Examples include population dynamics, evolution of social norms, communication structures, patterns in eco-systems and socio-biology, natural resource management, spread of diseases and development processes. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.

The first step-by-step introduction to the methodology of agent-based models in economics, their mathematical and statistical analysis, and real-world applications.

Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.

Although there are plenty of publications dealing with the theory of multi-agent systems and agent-based simulations, information about the practical development of such systems is scarce. The aim of this book is to fill this empty space and to provide knowledge about design and development of agent-based simulations in an easy and comprehensible way. The book begins with the fundamentals of multi-agent systems, agent principles and their interaction, and goes on to discuss the philosophy of agent-based programming. Agent-based models - like any other scientific method - have drawbacks and limitations, which are presented in the book as well. The main portion of the text is then devoted to a description of methodology and best practices for the design and development of agent-based simulation software. The methodology (called Agentology) guides the reader through the entire development process, from the formal definition of the problem, through conceptual modeling and the selection of the particular development platform, to the programming and debugging of the code itself and the final assessment of the model. The visual language as the means of representation of the conceptual model is included. The reader is also presented with a comparison of present multi-agent development environments and tools, which could be helpful for the selection of appropriate development instruments. Given that the theoretical foundation is presented in an accessible way and supported by many practical examples, figures, schemes and source codes, this publication is especially suitable as a textbook for introductory graduate-level courses on multi-agent systems and agent-based modeling. Besides appealing to students and the scientific community, the monograph can aid software architects and developers who are not familiar with agent principles, conveying valuable insights into this distinct computer paradigm.