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Introduction to Agent-Based Modelling (ABM) using Python
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Getting Started
What is this book about?
How to use this book
Contents
Chapter 1 - Modelling and simulation
1.1: What is modelling?
1.1.1: From chaos to clarity: why model?
1.1.2: The building blocks of a model
1.1.3: When do we use models?
1.2: The modelling cycle
1.2.1: Conceptual model
1.2.2: Formal model
1.2.3: Implementation
1.2.4: Verification and validation
1.2.5: Simulation experiments
1.2.6: Using the model
1.2.7: Applied example - the modelling cycle in a student report
1.3: Good practices of modelling
1.3.1: What makes a model "good"?
1.3.2: How much detail? KISS and KIDS
1.3.3: Common pitfalls
1.3.4: Reproducibility checklist
1.4: Selecting modelling and simulation tools
1.4.1: Agent-based modelling platforms
1.4.2: Tools for other paradigms
1.4.3: How to choose a tool
Chapter 2 - Fundamentals of agent-based modelling
2.1: Introduction to ABM
2.1.1: What is ABM?
2.1.2: Why is ABM important?
2.1.3: ABM vs other approaches
2.2: Main elements of an ABM
2.2.1: Agents
2.2.2: Environment and space
2.2.3: Time
2.2.4: Interactions
2.2.5: Model
2.3: Benefits and limitations of ABM
2.3.1: Strengths of ABM
2.3.2: Limitations and challenges
2.3.3: Decision framework
Chapter 3 - Your first Mesa model
3.1: Household washing coordination
Chapter 4 - Examples of ABMs built with Mesa
4.1: Simple economy
4.1.1: Exercises
4.2: Game of Life
4.2.1: Exercises
4.3: Traffic
4.3.1: Exercises
4.4: Virus on a network
4.4.1: Exercises
4.5: Event scheduling
4.5.1: Exercises
Chapter 5 - Towards empirical ABMs of complex systems
5.1: Introduction
5.2: Empirical agent-based models
5.3: Types of data for empirical ABM
5.4: Good practices for empirical ABM
About this Book
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