This book is extremely well written, very readable, beautifully illustrated, and eminently accessible with alternating clear explanations and clearly explained code examples. By the end of the book, the reader should be able to construct and explore his or her own agent-based model. Although the text and code are intertwined, the book is written in such a way that the reader can read through it and work out the examples along the way, or just read it and skip over the code. As the book progresses, the models get increasingly more complicated and robust as the NetLogo code gets more sophisticated. The NetLogo code used to create the model is very straightforward as well. The initial models are fairly simple, such as a model showing the spread of forest fires as a function of tree density. This book begins with some fundamentals of complex systems and then leads the reader into some concrete examples implemented in NetLogo. Codd published his groundbreaking paper on the relational data model, he published a book on cellular automata that is a precursor to agent-based modeling. For trivia buffs or those interested in the history of computers, here is an interesting tidbit. The first author of the book is also the author of the software.
NETLOGO MODELING COMMONS SOFTWARE
The software used to implement the models in the text is known as NetLogo. This bottom-up modeling approach is known as agent-based modeling. Why is this_?_ There are two primary reasons: the frequency with which we encounter complex systems, and the availability of software used to construct and execute models of these complex systems. But lately, the bottom-up view has been gaining more traction. Although the task of gathering data and developing differential equations to describe the data is no small chore, it pales in comparison to the task of modeling the behaviors, interactions, and resulting emergent properties of thousands, perhaps millions, of agents producing these emergent phenomena.
Since the days of Isaac Newton, scientists have largely adopted the top-down view to describe physical and social phenomena. This view can be thought of as the bottom-up view. Through their behaviors and interactions, they produce emergent properties that we refer to as the economy.
These people (referred to as agents in this view) may be consumers, producers, decision makers, or anyone whose behavior affects and is affected by the other agents. An alternative view would see the economy as emergent properties that arise from the interactions of the people who make up the economy. If you want to know what will happen if interest rates rise, just plug it into the equations and see how employment or growth is affected.
This view, which we can think of as the top-down view, would model the economy using system-level mathematical functions, usually differential equations. One view would assert that the economy is governed by invisible mathematical laws, much the way that, say, planetary motion is governed by the laws of gravity. Complex systems, such as the economy, can be conceptualized in two very different ways. So, I will address the beginning of this review to those who are not. Readers who are already familiar with agent-based modeling are probably already aware of this book and would certainly recognize the name of the first author. There is also an accompanying website with all the models and code. Features in each chapter include step-by-step guides to developing models in the main text text boxes with additional information and concepts end-of-chapter explorations and references and lists of relevant reading.
NETLOGO MODELING COMMONS HOW TO
The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Its hands-on approach - with hundreds of examples and exercises using NetLogo - enables readers to begin constructing models immediately, regardless of experience or discipline. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models.
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