â€œThe goal of science is to make the wonderful and complex understandable and simpleâ€”but not less wonderful.â€â€”Herbert SimonModels are computational devices that interpret information we gather about the world. Joshua Epstein said that modelling is a form of inquiry, as â€œit enforces habits of mind essential to freedomâ€ (Epstein, 2008). Besides the usefulness of prediction, models allow you to explain, guide additional data collection, understand core dynamics and suggest extensions by analogy; they also helps formulate new questions and illuminate uncertainties, bound outcomes to feasible ranges, react to crisis, demonstrate trade-offs, test boundaries of prevailing theories, expose weaknesses in prevailing wisdom, discipline dialogue and educate, plus reveal the complex to be simple (and vice versa).Scott Page has already written great books and The Model Thinker is his most open and accessible yet. His book with John H. Miller "Complex Adaptive Systems" from 2009 is outstanding, and probably motivated Page's great MOOC on models in the social sciences from a while back. That course evolved into The Model Thinker  -a playlist of useful ways of thinking about social phenomena. The book describes mathematical representations that apply to a wide range of subjects: models with a broad reference class application and a good track record in doing some, if not all, of the things Epstein praised about models. The great majority come from economics and game theory, but others build from epidemiology, sociology, demographics, political science, computational science and other areas.Models ultimately accomplish one or more of seven objectives, which Page summarizes with the acronym REDCAPE: they help us reason, explain, design, communicate, act, predict and explore. Going through the COVID-19 epidemic in 2020, I can see each of those features play out or not at all, to the detriment of a frightened population. Thinking in models seems to be not only practical but a moral imperative, as incompetence in dealing with the epidemic by not understanding its dynamics will result in unnecessary suffering.The book surveys around 20 models, or parts of models, in succinct and well crafted chapters. The explanation of each model is brief, a handful of pages at most, minimal in mathematical notation and high on intuition and application. The most impressive aspect of the book is Pages' ability to describe complex and mathematically sophisticated ideas in a simple and engaging manner that also reflects on real world phenomena. His description of mechanism design, an advanced topic in microeconomic theory, should become an example of conceptual clarity that many economics textbooks, enamored with math scaffolding the topic, convey without enthusiasm. Something similar can be said about the chapters on entropy, markov chains and multi-armed bandit problems.The book's main message is echoed in the work Page has done on diversity: the need to expose diverse ideas with one another to avoid hubris, reveal something truthful and improve precision. He writes that "[â€¦] we achieve wisdom through a multiplicity of lenses" (p. 5) and the book succeeds at showing that models, as thinking tools, as arguments, are best used in combinations with one another other. In the last chapter Page ends with a nuanced applications of some of the many models explained in order to frame two complex processes: the opioid crisis in the US and the increase of economic inequality within nations. In both cases Page shows how each model presented has lots to contribute to the understanding (and mitigation) of each of these phenomena, but that ultimately understanding is always partial, incomplete, and that the model never resembles completely the world it aims to portray.Although I loved the book and learned something in each of its almost 30 chapters, I missed a practical computational companion that helps apply the insights captured in all the models directly. There are so many tools available for free that help illustrate the power of each of these models, from R to Netlogo to Gambit, and The Model Thinker is in dire need of a way for readers and students alike to put in practice the menagerie of ideas within. This was a surprise, given Page's emphasis on computation being a source of theoretical insight (Chapter 5 of Complex Adaptive Systems is devoted to that idea), so hopefully in the future we could find computational applications of the models in the book.Modelling is a fantastic epistemological technology. Combinatorial at heart, ensembles and mixtures of mathematical simplifications apply to new and old phenomena alike, and can yield insight when a single perspective reveals shortsightedness or inflexibility. "The practice of modelling can be a beautiful game" (p. 355). Wonderful book.