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A large part of the volume presents articles which illustrate the history of the field until the late 1990s. During the early days researchers at the Santa-Fe Institute focused on discovery and description: can we find realistic patterns in the economy using a computational approach from a complexity perspective? By referring to the famous El Farol Bar example, Arthur shows clearly and rigorously that, first, this requires an analysis by applying agent-based models where agents are able to select their individual decision-making strategies based on the way they perform and therefore on how they become heterogeneous. Second, this implies that decision models both can become relevant and can turn obsolete according to the quality of their performance. Third, this can result in an economy with stable periods alternating with volatile periods. The examples are well described, convincing, and undoubtedly belong to the gems in the complexity literature.
The other chapters study several single elements of complexity theory related to the science of economy by looking explicitly at how technology changes, at evolutionary aspects, and at uncertainty in decision making. Arthur reiterates the relevance and importance of complexity economics by highlighting, for example, the fact that inherent uncertainties do matter and that technological change implies the absence of an equilibrium state. They draw together a wide variety of research fields and by doing so they provide a subtle reflection on economy, technology, and people.
Arthur has an inviting and elaborating style, taking the reader along his line of reasoning. An insightful introduction to the topic of the book by the author himself can be found on YouTube: https://www.youtube.com/watch?v=W0dGLEreBrM which illustrates content and style of the book. He is inspirational in using a mix of simple and complicated examples, ranging from a wide variety of domains. Arthur shows properly that the two core examples of El Farol Bar and the Artificial Stock Market are the fundaments of his work. It is promising that today many of the findings in this volume are, at least for this audience, intuitive. It shows how complexity in general and agent-based modelling in particular evolved over the past decades. But Arthur’s story is as urgent today as ever before: if we want to be confident in fixing ‘the economy’ we need to take complexity seriously into consideration.
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