X-Machines for Agent-Based Modeling FLAME Perspectives
From the Foreword: "This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was designed to make the building of large scale complex systems models straightforward and the simulation code that it generates is highly e...
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| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Milton
CRC Press
2017
Knowledge Unlatched GmbH Taylor & Francis CRC Press LLC Chapman & Hall |
| Edition | 1 |
| Series | Chapman & Hall/CRC Computer and Information Science Series |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1498723853 9781498723855 0367573156 9780367573157 9781315334523 131535358X 9781315370729 9781498723879 1315334526 149872387X 9781315353586 1315370727 |
| DOI | 10.1201/9781315370729 |
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| Abstract | From the Foreword: "This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was designed to make the building of large scale complex systems models straightforward and the simulation code that it generates is highly efficient and can be run on any modern technology. FLAME was the first such platform that ran efficiently on high performance parallel computers and a version for GPU technology is also available. At its heart, and the reason why it is so efficient and robust, is the use of a powerful computational model ‘Communicating X-machines’ which is general enough to cope with most types of modelling problems. As well as being increasingly important in academic research, FLAME is now being applied in industry in many different application areas. This book describes the basics of FLAME and is illustrated with numerous examples." —Professor Mike Holcombe, University of Sheffield, UK Agent-based models have shown applications in various fields such as biology, economics, and social science. Over the years, multiple agent-based modeling frameworks have been produced, allowing experts with non-computing background to easily write and simulate their models. However, most of these models are limited by the capability of the framework, the time it takes for a simulation to finish, or how to handle the massive amounts of data produced. FLAME (Flexible Large-scale Agent-based Modeling Environment) was produced and developed through the years to address these issues. This book contains a comprehensive summary of the field, covers the basics of FLAME, and shows how concepts of X-machines, can be stretched across multiple fields to produce agent models. It has been written with several audiences in mind. First, it is organized as a collection of models, with detailed descriptions of how models can be designed, especially for beginners. A number of theoretical aspects of software engineering and how they relate to agent-based models are discussed for students interested in software engineering and parallel computing. Finally, it is intended as a guide to developers from biology, economics, and social science, who want to explore how to write agent-based models for their research area. By working through the model examples provided, anyone should be able to design and build agent-based models and deploy them. With FLAME, they can easily increase the agent number and run models on parallel computers, in order to save on simulation complexity and waiting time for results. Because the field is so large and active, the book does not aim to cover all aspects of agent-based modeling and its research challenges. The models are presented to show researchers how they can build complex agent functions for their models. The book demonstrates the advantage of using agent-based models in simulation experiments, providing a case to move away from differential equations and build more reliable, close to real, models. The Open Access version of this book, available at https://doi.org/10.1201/9781315370729, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. |
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| AbstractList | From the Foreword:
"This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was designed to make the building of large scale complex systems models straightforward and the simulation code that it generates is highly efficient and can be run on any modern technology. FLAME was the first such platform that ran efficiently on high performance parallel computers and a version for GPU technology is also available. At its heart, and the reason why it is so efficient and robust, is the use of a powerful computational model ‘Communicating X-machines’ which is general enough to cope with most types of modelling problems. As well as being increasingly important in academic research, FLAME is now being applied in industry in many different application areas. This book describes the basics of FLAME and is illustrated with numerous examples." —Professor Mike Holcombe, University of Sheffield, UK
Agent-based models have shown applications in various fields such as biology, economics, and social science. Over the years, multiple agent-based modeling frameworks have been produced, allowing experts with non-computing background to easily write and simulate their models. However, most of these models are limited by the capability of the framework, the time it takes for a simulation to finish, or how to handle the massive amounts of data produced. FLAME (Flexible Large-scale Agent-based Modeling Environment) was produced and developed through the years to address these issues.
This book contains a comprehensive summary of the field, covers the basics of FLAME, and shows how concepts of X-machines, can be stretched across multiple fields to produce agent models. It has been written with several audiences in mind. First, it is organized as a collection of models, with detailed descriptions of how models can be designed, especially for beginners. A number of theoretical aspects of software engineering and how they relate to agent-based models are discussed for students interested in software engineering and parallel computing. Finally, it is intended as a guide to developers from biology, economics, and social science, who want to explore how to write agent-based models for their research area. By working through the model examples provided, anyone should be able to design and build agent-based models and deploy them. With FLAME, they can easily increase the agent number and run models on parallel computers, in order to save on simulation complexity and waiting time for results.
Because the field is so large and active, the book does not aim to cover all aspects of agent-based modeling and its research challenges. The models are presented to show researchers how they can build complex agent functions for their models. The book demonstrates the advantage of using agent-based models in simulation experiments, providing a case to move away from differential equations and build more reliable, close to real, models.
The Open Access version of this book, available at https://doi.org/10.1201/9781315370729, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Setting the Stage: Complex Systems, Emergence and Evolution Complex and Adaptive systems What is Chaos? Constructing Artificial Systems Importance of Emergence Dynamic Systems Is there Evolution at Work? Distributing Intelligence? Modeling and Simulation
Artificial Agents Intelligent Agents Engineering Self-Organising Systems Agent-Based Modeling Frameworks Adaptive Agent Design Mathematical Foundations Objects or Agents? Influence of other Research Areas on ABM
Designing X-Agents using FLAME FLAME and its X-machine methodology Using Agile Methods to Design Agents Overview: FLAME version 1.0 Libmboard (FLAME message board library) FLAME’s Missing Functionality
Getting started with FLAME Setting up FLAME Messaging Library: Libmboard How to run a model? Implementation Details Using Grids Integrating with more Libraries Writing a Model - Fox and Rabbit Predator Model 8 Enhancing the Environment
Agents in Social Science Sugarscape Model Modeling Social Networks Modeling Pedestrians in Crowds
Agents in Economic Markets and Games Perfect Rationality vs Bounded Rationality Learning Firms in a Cournot model A Virtual Mall Model: Labor and Goods Market Combined Programming Games Learning in an Iterated Prisonner’s Dilemma Game Multi-Agent Systems and Games
Agents in Biology Example Models Modeling Epithelial Tissue Modeling the Drosophila Embryo Development Output a Particular File for Analysis Modeling Pharaoh’s Ants (Monomorium pharaonis Modeling Drugs Delivery for Cancer Treatment
Testing Agent Behavior Unit and System Testing Statistical Testing of Data Statistics Testing on Code Testing Simulation Durations
FLAME’s Future FLAME to FLAME GPU Commercial applications of FLAME
Dr. Mariam Kiran is a well-recognized researcher in agent-based modeling, high performance simulations and cloud computing. She has published numerous papers in these fields, both, in theory and practical implementations, exploiting grid and cloud ecosystems for improving computational performance for multi-domain research. She has an extensive record of research collaborations across the world, serving as a board member for Complex Systems research in CoMSES, and several joint projects funded by European Research and UK Engineering Council. She is also active in education research of software engineering in team building and writing software for simulations.
Mariam Kiran received her PhD in Computer Science from University of Sheffield, Sheffield UK in 2010. She is currently involved in many projects at Lawrence Berkeley National Labs, California, optimizing high performance computing problems across various disciplines. Prior to this, she was working as an Associate Professor at University of Bradford, leading the Cloud Computing research in the School.
Open access – no commercial reuse From the Foreword: "This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was designed to make the building of large scale complex systems models straightforward and the simulation code that it generates is highly efficient and can be run on any modern technology. FLAME was the first such platform that ran efficiently on high performance parallel computers and a version for GPU technology is also available. At its heart, and the reason why it is so efficient and robust, is the use of a powerful computational model ‘Communicating X-machines’ which is general enough to cope with most types of modelling problems. As well as being increasingly important in academic research, FLAME is now being applied in industry in many different application areas. This book describes the basics of FLAME and is illustrated with numerous examples." —Professor Mike Holcombe, University of Sheffield, UK Agent-based models have shown applications in various fields such as biology, economics, and social science. Over the years, multiple agent-based modeling frameworks have been produced, allowing experts with non-computing background to easily write and simulate their models. However, most of these models are limited by the capability of the framework, the time it takes for a simulation to finish, or how to handle the massive amounts of data produced. FLAME (Flexible Large-scale Agent-based Modeling Environment) was produced and developed through the years to address these issues. This book contains a comprehensive summary of the field, covers the basics of FLAME, and shows how concepts of X-machines, can be stretched across multiple fields to produce agent models. It has been written with several audiences in mind. First, it is organized as a collection of models, with detailed descriptions of how models can be designed, especially for beginners. A number of theoretical aspects of software engineering and how they relate to agent-based models are discussed for students interested in software engineering and parallel computing. Finally, it is intended as a guide to developers from biology, economics, and social science, who want to explore how to write agent-based models for their research area. By working through the model examples provided, anyone should be able to design and build agent-based models and deploy them. With FLAME, they can easily increase the agent number and run models on parallel computers, in order to save on simulation complexity and waiting time for results. Because the field is so large and active, the book does not aim to cover all aspects of agent-based modeling and its research challenges. The models are presented to show researchers how they can build complex agent functions for their models. The book demonstrates the advantage of using agent-based models in simulation experiments, providing a case to move away from differential equations and build more reliable, close to real, models. The Open Access version of this book, available at https://doi.org/10.1201/9781315370729, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment). Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains. Discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular agent-based modeling environment that enables automatic parallelization of models. Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains including biology, economics and social networks. The book also includes examples to guide readers on how to write their own models. This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular agent-based modeling environment that enables automatic parallelization of models. Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains including biology, economics and social networks. The book also includes examples to guide readers on how to write their own models. From the Foreword:"This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was designed to make the building of large scale complex systems models straightforward and the simulation code that it generates is highly efficient and can be run on any modern technology. FLAME was the first such platform that ran efficiently on high performance parallel computers and a version for GPU technology is also available. At its heart, and the reason why it is so efficient and robust, is the use of a powerful computational model 'Communicating X-machines' which is general enough to cope with most types of modelling problems. As well as being increasingly important in academic research, FLAME is now being applied in industry in many different application areas. This book describes the basics of FLAME and is illustrated with numerous examples."-Professor Mike Holcombe, University of Sheffield, UKAgent-based models have shown applications in various fields such as biology, economics, and social science. Over the years, multiple agent-based modeling frameworks have been produced, allowing experts with non-computing background to easily write and simulate their models. However, most of these models are limited by the capability of the framework, the time it takes for a simulation to finish, or how to handle the massive amounts of data produced. FLAME (Flexible Large-scale Agent-based Modeling Environment) was produced and developed through the years to address these issues.This book contains a comprehensive summary of the field, covers the basics of FLAME, and shows how concepts of X-machines, can be stretched across multiple fields to produce agent models. It has been written with several audiences in mind. First, it is organized as a collection of models, with detailed descriptions of how models can be designed, especially for beginners. A number of theoretical aspects of software engineering and how they relate to agent-based models are discussed for students interested in software engineering and parallel computing. Finally, it is intended as a guide to developers from biology, economics, and social science, who want to explore how to write agent-based models for their research area. By working through the model examples provided, anyone should be able to design and build agent-based models and deploy them. With FLAME, they can easily increase the agent number and run models on parallel computers, in order to save on simulation complexity and waiting time for results.Because the field is so large and active, the book does not aim to cover all aspects of agent-based modeling and its research challenges. The models are presented to show researchers how they can build complex agent functions for their models. The book demonstrates the advantage of using agent-based models in simulation experiments, providing a case to move away from differential equations and build more reliable, close to real, models. The Open Access version of this book, available at https://doi.org/10.1201/9781315370729, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. |
| Author | Kiran, Mariam |
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| Keywords | CRTH2 Receptor Nash Equilibrium Agent Based Modeling MPI Library MPI Cournot Model GPU Version Simulation Program Generator Iterated Prisoner’s Dilemma Game Software Engineering Perspective GAs Monomorium Pharaonis Sugarscape Model Geometric Partitioning Closing Channels Agent Based Modeling Framework Active Management Applications Feed Forward Network Structure Artificial Society Cellular Automata Grid Version Memetic Algorithms Xml File GPU Kernel Function Execution |
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| Snippet | From the Foreword:
"This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was... From the Foreword: "This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was... This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment). Along with a... From the Foreword:"This book exemplifies one of the most successful approaches to modeling and simulating [the] new generation of complex systems. FLAME was... Discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular... This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a... |
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| Subtitle | FLAME Perspectives |
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| TableOfContents | 4.8.1 Constant Variables -- 4.8.2 Time Rules -- Chapter 5 Agents in Social Science -- 5.1 Sugarscape Model -- 5.1.1 Evolution from Bottom-Up -- 5.1.2 Distribution of Wealth -- 5.1.3 Location Is Important -- 5.1.4 Find Agents around Me -- 5.1.5 Handle Multiple 'Eaten' Requests -- 5.1.6 Change Starting Conditions -- 5.2 Modeling Social Networks -- 5.2.1 Set Up a Recurring Function -- 5.2.2 Assigning Conditions with Functions -- 5.2.3 Using Dynamic Arrays and Data Structures -- 5.2.4 Creating Local Dynamic Arrays -- 5.3 Modeling Pedestrians in Crowds -- 5.3.1 Calculate Movement toward Other Agents -- 5.3.2 Entering and Exiting Agents -- Chapter 6 Agents in Economic Markets and Games -- 6.1 Perfect Rationality versus Bounded Rationality -- 6.2 Modeling Multiple Shopper Behaviors -- 6.3 Learning Firms in a Cournot Model -- 6.3.1 Genetic Programming with Agents -- 6.3.2 Filtering Messages in Advance -- 6.3.3 Comparing Two Data Structures -- 6.4 A Virtual Mall Model: Labor and Goods Market Combined -- 6.5 Programming Games -- 6.5.1 Nash Equilibrium -- 6.5.2 Evolutionary Game Theory -- 6.5.3 Evolutionary Stable State -- 6.5.4 Game Theory versus Evolutionary Game Theory -- 6.5.5 Continuous Strategies -- 6.5.6 Red Queen and Equilibrium -- 6.6 Learning in an Iterated Prisoner's Dilemma Game -- 6.7 Multi-Agent Systems and Games -- Chapter 7 Agents in Biology -- 7.1 Example Models -- 7.1.1 Molecular Systems Models -- 7.1.2 Tissue and Organ Models -- 7.1.3 Ecological Models -- 7.1.4 Industrial Applications of Agent-Based Modeling with FLAME -- 7.2 Modeling Epithelial Tissue -- 7.2.1 Merging with Other Toolkits -- 7.3 Modeling Drosophila Embryo Development -- 7.3.1 Stochastic Modeling -- 7.3.2 Converting to an Agent-Based Model -- 7.3.3 Find Optimum Model Settings -- 7.4 Output Files for Analysis -- 7.5 Modeling Pharaoh's Ants (Monomorium pharaonis Cover -- Half Title -- PUBLISHED TITLES -- Title -- Copyright -- Dedication -- Contents -- Foreword -- Preface -- List of Figures -- List of Tables -- FLAME Contributors -- Chapter 1 Setting the Stage: Complex Systems, Emergence and Evolution -- 1.1 Complex and Adaptive Systems -- 1.2 What Is Chaos -- 1.3 Constructing Artificial Systems -- 1.4 Importance of Emergence -- 1.5 Dynamic Systems -- 1.6 Is There Evolution at Work -- 1.6.1 Adaptation -- 1.7 Distributing Intelligence -- 1.8 Modeling and Simulation -- 1.8.1 Research Examples -- Chapter 2 Artificial Agents -- 2.1 Intelligent Agents -- 2.1.1 "Can Machines Think -- 2.2 Engineering Self-Organizing Systems -- 2.2.1 Bring in the Agents -- 2.2.2 Characteristics of Agent-Based Models -- 2.3 Agent-Based Modeling Frameworks -- 2.4 Adaptive Agent Design -- 2.5 Mathematical Foundations -- 2.6 Objects or Agents -- 2.7 Influence of Other Research Areas on ABM -- Chapter 3 Designing X-Agents Using FLAME -- 3.1 FLAME and Its X-Machine Methodology -- 3.1.1 Transition Functions -- 3.1.2 Memory and States -- 3.2 Using Agile Methods to Design Agents -- 3.2.1 Extension to Extreme Programming -- 3.3 Overview: FLAME Version 1.0 -- 3.4 Libmboard (FLAME message board library -- 3.4.1 Compiling and Installing Libmboard -- 3.4.2 FLAME's Synchronization Points -- 3.5 FLAME's Missing Functionality -- Chapter 4 Getting Started with FLAME -- 4.1 Setting Up FLAME -- 4.1.1 MinGW -- 4.1.2 GDB GNU Debugger -- 4.1.3 Dotty as an Extra Installation -- 4.2 Messaging Library: Libmboard -- 4.3 How to Run a Model -- 4.4 Implementation Details -- 4.5 Using Grids -- 4.6 Integrating with More Libraries -- 4.7 Writing a Model - Fox and Rabbit Predator Model -- 4.7.1 Adding Complexity to Models -- 4.7.2 XML Model Description File -- 4.7.3 C Function -- 4.7.4 Additional Files -- 4.7.5 0.xml File -- 4.8 Enhancing the Environment 7.6 Model Drug Delivery for Cancer Treatment -- 7.6.1 Using Multiple Outputs -- Chapter 8 Testing Agent Behavior -- 8.1 Unit and System Testing -- 8.2 Statistical Testing of Data -- 8.3 Statistics Testing on Code -- 8.4 Testing Simulation Durations -- Chapter 9 FLAME's Future -- 9.1 FLAME to FLAME GPU -- 9.1.1 Visualizing Is Easy in FLAME GPU -- 9.1.2 Utilizing Vector Calculations -- 9.2 Commercial Applications of FLAME -- Bibliography -- Index |
| Title | X-Machines for Agent-Based Modeling |
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