Agent Based Modeling And Simulation

Operational Research (OR) deals with the use of advanced analytical methods to support better decision-making. It is multidisciplinary with strong links to management science, decision science, computer science and many application areas such as engineering, manufacturing, commerce and healthcare. In the study of emergent behaviour in complex adaptive systems, Agent-based Modelling & Simulation (ABMS) is being used in many different domains such as healthcare, energy, evacuation, commerce, manufacturing and defense. This collection of articles presents a convenient introduction to ABMS with papers ranging from contemporary views to representative case studies. The OR Essentials series presents a unique cross-section of high quality research work fundamental to understanding contemporary issues and research across a range of Operational Research (OR) topics. It brings together some of the best research papers from the esteemed Operational Research Society and its associated journals, also published by Palgrave Macmillan.

An Agent Based Model (ABM) allows simulating the actions and interactions of many agents or entities in order to evaluate their impact on the system as a whole. These models are used in areas such as industry, business, biology, ecology, and the social sciences. CONTRIBUTIONS - IMMEDIATE RESULTS. From the first pages the reader is already able to create a model. - FREE SOFTWARE. The use of specific and free software for personal and educational use. - WITHOUT PRIOR TRAINING. Knowing how to program in Java, C ++, Pyton, Anylogic, etc. is not required. - GUIDE. A neat guide that explains each step in detail, for quick learning. - MODELS. The explanation of 40 didactic models, created to learn progressively. - FIGURES. The support of more than 1000 figures to advance clearly in each stage. - VIDEOS. The models described, together with various help videos, can be downloaded. - PRACTICAL. A practical approach allows the reader to see the possible applications to their environment. - EXPERIENCE. The teaching experience of the author and the reviewers has allowed the text to be refined to the maximum. AUTHOR AND REVIEWERS Juan Martín García is a Doctor of Industrial Engineering in Business Organization from the UPC (Spain) and a Diploma from the Sloan School of Management at MIT (USA). He has more than 30 years of experience as a consultant for companies and public organizations using simulation models based on System Dynamics. Professor at several Spanish and Latin American universities, he teaches online courses at Vensim https://vensim.com/vensim-online-courses/ (in English) and System Dynamics at ATC-Innova http://atc-innova. com/ (Spanish). He is the author of books and lectures on business, social and environmental applications of simulation models. - Dr. Francisco Campuzano Bolarín, Professor of Business Organization at the Polytechnic University of Cartagena (UPCT). - Lening Mora, M.S Environmental & Occupational Health (San Diego, California) and Postgraduate Diploma in Healthcare Modeling and Simulation at Naval Postgraduate School (Monterey, California USA). - Professor Gavin Melles, PhD, MSc Swinburne University (Victoria, Australia). INDEX Presentation software Installation Working screen A model in 1 minute Concepts Functions and tables Variables Model: Traffic light Model: Paris Rome Attributes Model: Rio Bravo 2 Model: Truck Fleet Collections and aggregates Model: Dragons and Castles Model: Parents and children Model: The Four Pirates References Model: White and Black Model: White and Black 2 Model: White and Black 3 Comments Tools Entities initial parameters Model: Horse Racing Temporal parameters Model: Satellite Launch External data entities Import initial data Import time series data Model: My three rabbits Exercises Model: Rabbit Population Model: Rabbit Population 2 Model: Rabbit Population 3 Model: Rabbit population 4 Model: Rabbit population 5 Model: Sweet candies Model: Cheese shop Model: Cheese Shop 2 Model: Formula 1 drivers Model: Patients and hospitals Model: Horse breeding Model: Horse breeding 2 Model: Horse breeding 3 Model: Horse breeding 4 Model: Horse breeding 5 Model: Horse breeding 6 Model: Horse breeding 7 Model: Fighter aircraft Model: Fighter Aircraft 2 Model: Fishing in three seas Model: Fishing in three seas 2 Model: Fishing in three seas 3 Model: Fishing in three seas 4 Model: Fishing in three seas 5 Model: Fishing in three seas 6 Model: Gold Market Model: Gold Market 2 Model: Gold Market 3 Model: Gold Market 4 Model: Eco Restaurant Model: Beer Game

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the “General Reference Model for Agent-based Modeling and Simulation” (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models –from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.

Discrete event simulation and agent-based modeling are increasingly recognized as critical for diagnosing and solving process issues in complex systems. Introduction to Discrete Event Simulation and Agent-based Modeling covers the techniques needed for success in all phases of simulation projects. These include: • Definition – The reader will learn how to plan a project and communicate using a charter. • Input analysis – The reader will discover how to determine defensible sample sizes for all needed data collections. They will also learn how to fit distributions to that data. • Simulation – The reader will understand how simulation controllers work, the Monte Carlo (MC) theory behind them, modern verification and validation, and ways to speed up simulation using variation reduction techniques and other methods. • Output analysis – The reader will be able to establish simultaneous intervals on key responses and apply selection and ranking, design of experiments (DOE), and black box optimization to develop defensible improvement recommendations. • Decision support – Methods to inspire creative alternatives are presented, including lean production. Also, over one hundred solved problems are provided and two full case studies, including one on voting machines that received international attention. Introduction to Discrete Event Simulation and Agent-based Modeling demonstrates how simulation can facilitate improvements on the job and in local communities. It allows readers to competently apply technology considered key in many industries and branches of government. It is suitable for undergraduate and graduate students, as well as researchers and other professionals.

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.

Archaeology has been historically reluctant to embrace the subject of agent-based simulation, since it was seen as being used to "re-enact" and "visualize" possible scenarios for a wider (generally non-scientific) audience, based on scarce and fuzzy data. Furthermore, modeling "in exact terms" and programming as a means for producing agent-based simulations were simply beyond the field of the social sciences. This situation has changed quite drastically with the advent of the internet age: Data, it seems, is now ubiquitous. Researchers have switched from simply collecting data to filtering, selecting and deriving insights in a cybernetic manner. Agent-based simulation is one of the tools used to glean information from highly complex excavation sites according to formalized models, capturing essential properties in a highly abstract and yet spatial manner. As such, the goal of this book is to present an overview of techniques used and work conducted in that field, drawing on the experience of practitioners.

To fully understand not only the past, but also the trajectories, of human societies, we need a more dynamic view of human social systems. Agent-based modeling (ABM), which can create fine-scale models of behavior over time and space, may reveal important, general patterns of human activity. Agent-Based Modeling for Archaeology is the first ABM textbook designed for researchers studying the human past. Appropriate for scholars from archaeology, the digital humanities, and other social sciences, this book offers novices and more experienced ABM researchers a modular approach to learning ABM and using it effectively. Readers will find the necessary background, discussion of modeling techniques and traps, references, and algorithms to use ABM in their own work. They will also find engaging examples of how other scholars have applied ABM, ranging from the study of the intercontinental migration pathways of early hominins, to the weather–crop–population cycles of the American Southwest, to the trade networks of Ancient Rome. This textbook provides the foundations needed to simulate the complexity of past human societies, offering researchers a richer understanding of the past—and likely future—of our species.

Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author’s website. A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.