Agent Based and Individual Based Modeling A Practical Introduction 2nd Edition by Steven Railsback, Volker Grimm – Ebook PDF Instant Download/Delivery: 9780691190044 ,0691190046
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ISBN 10: 0691190046
ISBN 13: 9780691190044
Author: Steven Railsback, Volker Grimm
The essential textbook on agent-based modeling—now fully updated and expanded Agent-Based and Individual-Based Modeling has become the standard textbook on the subject for classroom use and self-instruction. Drawing on the latest version of NetLogo and fully updated with new examples, exercises, and an enhanced text for easier comprehension, this is the essential resource for anyone seeking to understand how the dynamics of biological, social, and other complex systems arise from the characteristics of the agents that make up these systems. Steven Railsback and Volker Grimm lead students stepwise through the processes of designing, programming, documenting, and doing scientific research with agent-based models, focusing on the adaptive behaviors that make these models necessary. They cover the fundamentals of modeling and model analysis, introduce key modeling concepts, and demonstrate how to implement them using NetLogo. They also address pattern-oriented modeling, an invaluable strategy for modeling real-world problems and developing theory. This accessible and authoritative book focuses on modeling as a tool for understanding real complex systems. It explains how to pose a specific question, use observations from actual systems to design models, write and test software, and more. A hands-on introduction that guides students from conceptual design to computer implementation to analysis Filled with new examples and exercises and compatible with the latest version of NetLogo Ideal for students and researchers across the natural and social sciences Written by two leading practitioners Supported by extensive instructional materials at www.railsback-grimm-abm-book.com
Agent Based and Individual Based Modeling A Practical Introduction 2nd Edition Table of contents:
Part I Agent-Based Modeling and NetLogo Basics
1 Models, Agent-Based Models, and the Modeling Cycle
1.1 Introduction, Motivation, and Objectives
1.2 What Is a Model?
1.3 What Does the Modeling Cycle Involve?
1.4 What Is Agent-Based Modeling? How Is It Different?
1.5 Summary and Conclusions
1.6 Exercises
2 Getting Started with NetLogo
2.1 Introduction and Objectives
2.2 A Quick Tour of NetLogo
2.3 A Demonstration Program: Mushroom Hunt
2.4 Summary and Conclusions
2.5 Exercises
3 Describing and Formulating ABMs: The ODD Protocol
3.1 Introduction and Objectives
3.2 What Is ODD and Why Use It?
3.3 The ODD Protocol
3.4 Our First Example: Virtual Corridors of Butterflies
3.5 Summary and Conclusions
3.6 Exercises
4 Implementing a First Agent-Based Model
4.1 Introduction and Objectives
4.2 ODD and NetLogo
4.3 Butterfly Hilltopping: From ODD to NetLogo
4.4 Comments and the Full Program
4.5 Summary and Conclusions
4.6 Exercises
5 From Animations to Science
5.1 Introduction and Objectives
5.2 Observation of Corridors
5.3 Analyzing the Model
5.4 Time-Series Results: Adding Plots and File Output
5.5 A Real Landscape
5.6 Summary and Conclusions
5.7 Exercises
6 Testing Your Program
6.1 Introduction and Objectives
6.2 Common Kinds of Errors
6.3 Techniques for Debugging and Testing NetLogo Programs
6.4 Documentation of Tests
6.5 An Example and Exercise: The Culture Dissemination Model
6.6 Summary and Conclusions
6.7 Exercises
Part II Model Design Concepts
7 Introduction to Part II
7.1 Objectives of Part II
7.2 Overview of Part II
8 Emergence
8.1 Introduction and Objectives
8.2 A Model with Less Emergent Dynamics
8.3 Simulation Experiments and BehaviorSpace
8.4 A Model with Complex Emergent Dynamics
8.5 Summary and Conclusions
8.6 Exercises
9 Observation
9.1 Introduction and Objectives
9.2 Observing the Model via NetLogo’s View
9.3 Other Interface Displays
9.4 File Output
9.5 BehaviorSpace as an Output Writer
9.6 Export Primitives and Menu Commands
9.7 Summary and Conclusions
9.8 Exercises
10 Sensing
10.1 Introduction and Objectives
10.2 Who Knows What: The Scope of Variables
10.3 Using Variables of Other Objects
10.4 Putting Sensing to Work: The Business Investor Model
10.5 Summary and Conclusions
10.6 Exercises
11 Adaptive Behavior and Objectives
11.1 Introduction and Objectives
11.2 Identifying and Optimizing Alternatives in NetLogo
11.3 Adaptive Behavior in the Business Investor Model
11.4 Nonoptimizing Adaptive Behavior: A Satisficing Example
11.5 The Objective Function
11.6 Summary and Conclusions
11.7 Exercises
12 Prediction
12.1 Introduction and Objectives
12.2 Example Effects of Prediction: The Business Investor Model’s Time Horizon
12.3 Implementing and Analyzing Submodels
12.4 Analyzing the Investor Utility Function
12.5 Modeling Prediction Explicitly
12.6 Summary and Conclusions
12.7 Exercises
13 Interaction
13.1 Introduction and Objectives
13.2 Programming Interaction in NetLogo
13.3 The Telemarketer Model
13.4 The March of Progress: Global Interaction
13.5 Direct Interaction: Mergers in the Telemarketer Model
13.6 The Customers Fight Back: Remembering Who Called
13.7 Summary and Conclusions
13.8 Exercises
14 Scheduling
14.1 Introduction and Objectives
14.2 Modeling Time in NetLogo
14.3 Summary and Conclusions
14.4 Exercises
15 Stochasticity
15.1 Introduction and Objectives
15.2 Stochasticity in ABMs
15.3 Pseudorandom Number Generation in NetLogo
15.4 An Example Stochastic Process: Empirical Model of Behavior
15.5 Summary and Conclusions
15.6 Exercises
16 Collectives
16.1 Introduction and Objectives
16.2 What Are Collectives?
16.3 Modeling Collectives in NetLogo
16.4 Example: A Wild Dog Model with Packs
16.5 Summary and Conclusions
16.6 Exercises
Part III Pattern-Oriented Modeling
17 Introduction to Part III
17.1 Toward Structurally Realistic Models
17.2 Single and Multiple, Strong and Weak Patterns
17.3 Overview of Part III
18 Patterns for Model Structure
18.1 Introduction and Objectives
18.2 Steps in POM to Design Model Structure
18.3 Example: Modeling European Beech Forests
18.4 Example: Management Accounting and Collusion
18.5 Summary and Conclusions
18.6 Exercises
19 Theory Development
19.1 Introduction and Objectives
19.2 Theory Development and Strong Inference in the Virtual Laboratory
19.3 Examples of Theory Development for ABMs
19.4 Exercise Example: Stay or Leave?
19.5 Summary and Conclusions
19.6 Exercises
20 Parameterization and Calibration
20.1 Introduction and Objectives
20.2 Parameterization of ABMs Is Different
20.3 Parameterizing Submodels
20.4 Calibration Concepts and Strategies
20.5 Example: Calibration of the Woodhoopoe Model
20.6 Summary and Conclusions
20.7 Exercises
Part IV Model Analysis
21 Introduction to Part IV
21.1 Objectives of Part IV
21.2 Overview of Part IV
22 Analyzing and Understanding ABMs
22.1 Introduction and Objectives
22.2 Example Analysis: The Segregation Model
22.3 Additional Heuristics for Understanding ABMs
22.4 Statistics for Understanding
22.5 Summary and Conclusions
22.6 Exercises
23 Sensitivity, Uncertainty, and Robustness Analysis
23.1 Introduction and Objectives
23.2 Sensitivity Analysis
23.3 Uncertainty Analysis
23.4 Robustness Analysis
23.5 Summary and Conclusions
23.6 Exercises
24 Where to Go from Here
24.1 Introduction and Objectives
24.2 Keeping Your Momentum: Reimplementation
24.3 Your First Model from Scratch
24.4 Modeling Agent Behavior
24.5 ABM Gadgets
24.6 NetLogo as a Platform for Large Models
24.7 An Odd Farewell
References
Index
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