The Model Thinker: What You Need to Know to Make Data Work for You
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- Create Date：2021-04-28 10:51:54
- Update Date：2025-09-06
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- Author：Scott E. Page
- ISBN：1541675711
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Summary
Work with data like a pro using this guide that breaks down how to organize， apply， and most importantly， understand what you are analyzing in order to become a true data ninja。
From the stock market to genomics laboratories， census figures to marketing email blasts， we are awash with data。 But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows， numbers aren't enough: we need to know how to make those numbers talk。 In The Model Thinker， social scientist Scott E。 Page shows us the mathematical， statistical， and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius。 At the core of the book is Page's "many-model paradigm，" which shows the reader how to apply multiple models to organize the data， leading to wiser choices， more accurate predictions， and more robust designs。 The Model Thinker provides a toolkit for business people， students， scientists， pollsters， and bloggers to make them better， clearer thinkers， able to leverage data and information to their advantage。
From the stock market to genomics laboratories， census figures to marketing email blasts， we are awash with data。 But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows， numbers aren't enough: we need to know how to make those numbers talk。 In The Model Thinker， social scientist Scott E。 Page shows us the mathematical， statistical， and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius。 At the core of the book is Page's "many-model paradigm，" which shows the reader how to apply multiple models to organize the data， leading to wiser choices， more accurate predictions， and more robust designs。 The Model Thinker provides a toolkit for business people， students， scientists， pollsters， and bloggers to make them better， clearer thinkers， able to leverage data and information to their advantage。
Reviews
Max
，
After using Scott Page's previous work on path dependence models during my bachelor thesis， I was really excited when this book was suggested during my master。 A beautiful book！ It took me some time to go through all the introduced models but it was worth it！ After using Scott Page's previous work on path dependence models during my bachelor thesis， I was really excited when this book was suggested during my master。 A beautiful book！ It took me some time to go through all the introduced models but it was worth it！ 。。。more
David Kritz
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Dr。 Page is one of my academic heroes。 I have taken his courses online and watched his videos on the Great Courses Plus。 I cannot endorse his books highly enough。
Humam Fauzi
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Explain well about many model and its application。 Not too deep but not to shallow either so a layperson could understand without making the one who is familiar with the field bored。Maths does not too hard but on few model require to write things down to make it more clearer。 Very satisfying。 Recommended for someone who want to understand analysis， causality， data and statistics。
Robert
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A good addition to the subject matter if you have some experience with modeling。
Walter
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This book accomplishes what it sets out to do: to expose the reader to a wide range of mental models in a very accessible way， without the need for any specialized training in mathematics。 Of course， a little math goes a long way in extracting the most here。 For those in this category， some of the models will be familiar。I subtract one star because though it opened very strong and remained so for 75% of the book， it kinda lost steam in the later chapters。 Recommended。
Min
，
This books offers insight into so many disciplines by giving a primer of so many models and concepts， developed by different people for different problems encountered in the real world。 While it can only be an introductory reading due to the vast number of models presented， it is still very dense and at times very technical。 So be prepared to do some skimming if you want to get through it。 But your horizon will be broadened。
Jaume Sués Caula
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Walkthrough of the up-to-date mathematical models， with real-life applications。 I missed more in-depth description of neural networks， basis of artificial intelligence。 Great dense lecture， in any case。
Silvio
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Interesting and revealing， a guide to help think on today's complex problemsWhat's a model？ How to use a model？ Why to use a model？ Any qualitative approach to apply a model？ Only use 1 model or various？ These are the questions that these book look to help to answer。 It is fascinating as depending your area of expertise some model will be recognizable and others not。 Maybe someone can say that many other models are not included， or not clear the difference what a model is， is the Standard Atomic Interesting and revealing， a guide to help think on today's complex problemsWhat's a model？ How to use a model？ Why to use a model？ Any qualitative approach to apply a model？ Only use 1 model or various？ These are the questions that these book look to help to answer。 It is fascinating as depending your area of expertise some model will be recognizable and others not。 Maybe someone can say that many other models are not included， or not clear the difference what a model is， is the Standard Atomic model a "model"？ Not described here however it is。 Same with many others。 In any case it is very informative and overeating to understand the why: the more diverse approach to think about a problem， the better answers or guidelines you can get。 Some are most difficult to apply unless further study or practice like bandit models or NK models in my case。 What about machine learning？ Algorithms are also a model？ It is a super interesting book to start this thinking approach， and I recommend to do also his course in Coursera: Model Thinking。 Ideally to read paired with Algorithms to Live by: different style， different Algorithms （or model？） But completes this vision of learning to apply formal models to analyze a problem and think on different potential solutions。 。。。more
Rory Fox
，
An Interesting and thought provoking read， but certainly not an easy book。 Whilst this is not an academic text book， it does assume a familiarity with mathematics and statistics。 Readers who prefer not to engage with formulas and notation will find the book challenging。The earlier chapters explain the concepts of models and modelling， then the book works through 29 chapters which apply the models， chapter by chapter。 Around 25% of the book is footnotes。 Interspersed amongst the models are anecdo An Interesting and thought provoking read， but certainly not an easy book。 Whilst this is not an academic text book， it does assume a familiarity with mathematics and statistics。 Readers who prefer not to engage with formulas and notation will find the book challenging。The earlier chapters explain the concepts of models and modelling， then the book works through 29 chapters which apply the models， chapter by chapter。 Around 25% of the book is footnotes。 Interspersed amongst the models are anecdotes and examples which show the application and significance of the models。 This makes the book enjoyable with the authors wide knowledge leading to frequent surprises。 We see， for example， that a better understanding of Standard Deviation would have avoided the mistaken policy decisions which saw many countries chasing ‘small schools，’ as an educational panacea in the 1990s （Kindle location 15%）。 One of the author’s insistent points is that people need to be prepared to apply multiple models， and to apply models from different areas。 He illustrates this creatively。 For example， he uses a model of radioactive decay to illustrate how memories also ‘decay’ （Kindle 22%）。Whilst the models are thoughtfully presented， there were occasional questions of over-simplification。 For example， in Chapter 26 on Learning models， we hear about how rewards and incentivisation can improve learning （65%）。 This is certainly true， but there are also well-known sets of biases which can counteract some of the implications。 For example ‘incentivisation biases’ show how performance can actually get worse when it is rewarded， because it can replaces an inner， intrinsic incentive with an extrinisic motivation。 It would have been helpful if the book could have acknowledged some of these additional complexities。On a more fundamental level， I also wonder if the core insight is quite right。 The philosophy of the book is that multiple models is the answer to converging on truth。 Our thinking will be better proportionate to our ability to select and apply multiple models。But is this really true？ Our selection of models takes place inside an overarching single model of rationality。 Our ability to compare models is because we have a single， more fundamental model which can be used to judge all the other models。 For example， our over-arching model of rationality means that we do not accept contradictions， and that is a criterion which we apply to all models。 This suggests that although multiple models may indeed be helpful， they are helpful within a single model of rationality， not as a pluralistic alternative to rationality。 Ultimately， there are some important philosophical questions raised by this book， but they are left tantalizingly incomplete。 However， I enjoyed the opportunity to refresh my knowledge of statistics and I appreciated the creative way which the models are presented and related to each other。 。。。more
Jenny
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Maybe one of my favorite books for this topic， though it doesn’t make for a particularly enjoyable linear read。 Bookmark and come back to it。 Re-read things multiple times。 Revisit it annually， maybe。
Bill Rand
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Fantastic tour-de-force of a wide variety of models， with an emphasis on many model thinking。
Denis Vasilev
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Книга повторяющая отличный курс на Курсере по Модельному мышлению。 Непростое чтение， но для тех， кто хочет разбираться в мат моделях неизбежное。 Я проходил курс， поэтому многое уже было знакомо。 Сложнее с применением， хоть и очень хочется использовать
Jan Spörer
，
Here are my key takeaways from the book。 I’ve found that the earlier chapters were more interesting to me as there were more statistical concepts at the start。 There are many game theory models in the later chapters:-Many-model approach: The book relies on a many-model approach。 A given problem can usually be solved with several models。 Also， different problems require different models。 （pp。 5-6） Condorcet jury theorem: Many models are better than one model in a jury problem。 If a judge is right Here are my key takeaways from the book。 I’ve found that the earlier chapters were more interesting to me as there were more statistical concepts at the start。 There are many game theory models in the later chapters:-Many-model approach: The book relies on a many-model approach。 A given problem can usually be solved with several models。 Also， different problems require different models。 （pp。 5-6） Condorcet jury theorem: Many models are better than one model in a jury problem。 If a judge is right more often than not， a decision will be more accurate if there are multiple jurors。 （p。 28） Diversity prediction theorem: Many-model error = Average-model error - diversity of model predictions。 （pp。 29-30）-The wisdom hierarchy: Data， information， knowledge， wisdom。 Data is just raw data。 Information is categorical data。 For example， region-labeled temperature data is information。 Knowledge is “justified true belief” （Plato） and contains additional relational information such as correlations， causation， logic， and conditions for the models to hold。 Knowledge is organized information。 Models are knowledge。 Wisdom is the “ability to identify and apply relevant knowledge”。 （pp。 7-8）-Models can be divided into embodiment models （such as a discounted cash flow model）， analogous models （such as a valuation multiple model）， and alternative reality models （such as the game of life; they model hypothetical scenarios and not an actual process）。 （pp。 13-14） -The seven uses of models: REDCAPE （p。 15）: --Reason （identify conditions and deduce logical implications）; --explain; --design processes， institutions， policies， and rules; --communicate: Clear sets of inputs and outputs and corresponding definitions of all inputs and outputs that can be agreed upon。 （pp。 20-21）--act; --predict; --explore。-Bagging means “bootstrap aggregation” and constructs many models。 Many datasets are drawn and models are fitted。 The average of the models is the final model。 （p。 42）-Rule-based models work without loss functions。 （p。 53）-Models produce an equilibrium， cycles， randomness， or complexity。 （p。 56）-Rational-choice models and zero-intelligence models provide upper and lower bounds for potential outcomes。 （p。 58）-Multiplication of random variables leads to lognormal distributions。 Addition of random variables leads to normal distributions。 （p。 60） “Lognormal distributions lack symmetry because products of numbers larger than 1 grow faster than sums 。。。 and multiples of numbers less than 1 decrease faster than 。。 sums。 If we multiply sets of twenty random variables with values uniformly distributed between zero and 10， their product will consist of many outcomes near zero and some large outcomes， creating the skewed distribution shown in 5。2。” （p。 66） Long-tailed distributions require non-independence。 Those often come in the form of positive feedback。 Sales， fires， city populations。 （p。 69） Power-law distributions have many small events。 （p。 70） Power laws with an exponent of 2 or less lack a well-defined mean。 （p。 71）-Sampling random variables: \sigma_\mu = \frac{\sigma}{\sqrt{N}} and \sigma_{\sum} = \sigma\sqrt{N} （p。 63）-Zipf’s law: “The special case of power laws with exponents equal to 2 are known as Zipf distributions。 For power laws with exponents of two， an event’s rank times its probability will equal a constant， a regularity known as Zipf’s law。 Words satisfy Zipf’s law。 The most common English word， the， occurs 7% of the time。 The second most common word， of， occurs 3。5% of the time。 Notice that its rank， 2， times its frequency of 3。5% equals 7%。” Event Rank * Event Size = Constant （p。 72）-Self-organized criticality， forest fire model: Self-organized criticality leads to power-law distributions for the sizes of forest fires。 “The key assumptions for self-organization to critical states is that pressure increases smoothly， like water flowing into the lake， and that pressure decreases in bursts， including possibly large events。 （pp。 74-75） -Projects with large budgets over run out of control。 This is because the random variables， i。e。， the budgets for individual stages and parts of a project， are positively dependent on each other。 If one part encounters problems， the next step will also run into budgetary problems。 （pp。 78-79）-Models of value and power: LOTB and Shapley values。 LOTB is the last-on-the-bus value。 How much would a participant add to the system if he joined last？ Shapley values are the average value of a participant for each possible condition under which the participant could enter the system。 （p。 107）-In a network model， a “node’s betweenness score equals the percentage of minimal paths that go through a node。” （p。 119）-Friendship paradox: “One analysis of friendships on Facebook found that the average person has around two hundred friends and their friends， on average， have more than six hundred friends。” （p。 124）-Network robustness。 “Here， we consider the question of how the size of the largest connected component of the network， the giant component， changes as nodes randomly fail… In the random network， the size of the giant component falls linearly at first。 At a critical value where the probability of an edge equals 1 divided by the number of nodes， the size of the largest component falls to an arbitrarily small proportion of the original network size。 The small-world network shows no such abrupt change。 A majority of t connections exist within the geographic clusters。” （p。 127） -”Most consumer goods and information spread through both diffusion and broadcast。 Our next model， the bass model， combines the two processes in a single model。” （p。 136）-SIR model: The susceptible-infected-recovered model has a built-in saturation assumption。 （p。 137）-The minimum percentage of immunity in a population， the vaccination threshold， depends on the basic reproduction number （R_0）， which says how many people get infected by each person that is newly infected。 The vaccination threshold is: V \geq \frac{R_0 - 1}{R_0}。 Polio has a R_0 of 6， so the vaccine must cover ⅚ of the population。（p。 138-139）-Superspreaders are key to epidemics。 This is due to degree squaring。 Superspreaders have more contacts to others， which exposes them to more risk for getting a disease and also increases the chances to spread the disease in the same manner。 A superspreader with 3x more social contact will therefore be 3x more likely and 3x more dangerous when infected， making him 9x more dangerous overall。 （pp。 139-140）。-R_0 and the vaccination threshold are contextual tipping points。 Small changes in the context （environment） can change outcomes significantly。 The current focus in public discussions around R_0 and vaccinations show that there is awareness for the criticality of these measures。 （p。 141）-Grossman and Stiglitz paradox: If investors believe in the efficient market hypothesis， they stop analyzing， making markets inefficient。 （p。 160）-Agent-based models are computer programs that model each agent individually。 （p。 213）-Hedonic attributes: more is always better。 Spatial attributes: sweet spots that are different among agents。 （pp。 227-228）-Voronoi neighborhoods help to see which characteristics a product should have to optimally fit a certain customer group。 （p。 231）-”A one-dimensional model implies that candidates position at the median。 Higher-dimensional models imply that they should not。 Which type of model do we believe？ We should place complete faith in neither model， but instead gain insights from both… We should instead expect complexity， an endless dance of competition for votes through coalition building。” （p。 234）-Models of cooperation: In this example: a lone cooperator cannot spawn an additional cooperator， but two adjacent cooperators can。 It follows that a small cluster of cooperative nodes surrounded by empty cells could expand into open nodes。 Therefore， regions of cooperation can emerge from a handful of cooperators。 （p。 264） Group selection is another way to achieve efficient cooperation。 Let groups form。 The best group （the one with many cooperators） will emerge as a winner and make copies of itself or otherwise project its system on the rest of the population。 This can be easier than to just have one large population that has to be made cooperative。 （p。 265） 。。。more
Mike
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The models are explained at a level for the beginner but there are lots of references if you want to dive deeper。
Asiman
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A tough read - not one you can simply breeze through - a revelation nonetheless， once you have managed to trundle through the first 50 pages。 The Model Thinker spans across multiple disciplines， throwing light on the most critical models yet discovered or constructed for various contexts。 After laying down the basics of a model， Prof。 Page highlights the insights gleaned from the model's applications in the germane contexts。 Prof。 Page takes care not to delve too deep into each model， for then， A tough read - not one you can simply breeze through - a revelation nonetheless， once you have managed to trundle through the first 50 pages。 The Model Thinker spans across multiple disciplines， throwing light on the most critical models yet discovered or constructed for various contexts。 After laying down the basics of a model， Prof。 Page highlights the insights gleaned from the model's applications in the germane contexts。 Prof。 Page takes care not to delve too deep into each model， for then， the book would inevitably have run into multiple volumes， and also would have become inaccessible to a bigger audience。 Needless to say， most of the models might need of the reader a solid mathematical grounding or at least the intuition。 Those with the intuition might have to think deeper through each explanation to truly understand the implications。 Even if you could not really grasp all the maths， I would still recommend you to focus on the takeaways highlighted for each model， for those could really help guide you and your larger group's decisions in the contexts that you might experience in personal， public or professional lives。In the end though， the book stresses most on the importance of applying multiple （appropriate） models to a problem or situation to cater to any of the REDCAPE uses （Reason， Explain， Design， Communicate， Act， Predict， Explore）。 Also， since 'all models are wrong'， it follows that using ensembles leads to more accurate results than would have emerged from using the individual models （wisdom of the crowds）。 。。。more
Jeffrey
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Makes some good points about the usefulness of models and provides a concise overview of many useful types of models。 Works well as a general introduction to what modeling can do， or as a brief glimpse at other models for people who already do modeling and looking for inspiration， but its not particularly useful as any sort of guide to get started in actually doing it。Could also have used a more thorough editing。
Mustufa Kerawala
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A really dense mathematical book learnt which teaches some neat algorithmic tricks。 Although， not an easy read to handle
Daniel
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It's a testament to human interpretation that models that barely differ mathematically can explain so many diverse social phenomena。 Book is very well written too， even if the title about "data" is misleading。 This is not a book about data really。I started reading this a few months while skiing alone。 I'd hurtle down and read this on the way up。 Feels a long， long time ago now。 It's a testament to human interpretation that models that barely differ mathematically can explain so many diverse social phenomena。 Book is very well written too， even if the title about "data" is misleading。 This is not a book about data really。I started reading this a few months while skiing alone。 I'd hurtle down and read this on the way up。 Feels a long， long time ago now。 。。。more
Jeff Heuer
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A somewhat dry， but unique text， that concisely presents a worldview it took me years of piecemeal study to stitch together from bits of economics， behavioral science， computer science， biology， complex systems， and beyond。 You might think of it as “computational social science”， though it certainly extends further。It does a very nice job of pointing to real-world scenarios that can be usefully analyzed by each model， and highlights topics of equity， diversity， and other pro-social values， speci A somewhat dry， but unique text， that concisely presents a worldview it took me years of piecemeal study to stitch together from bits of economics， behavioral science， computer science， biology， complex systems， and beyond。 You might think of it as “computational social science”， though it certainly extends further。It does a very nice job of pointing to real-world scenarios that can be usefully analyzed by each model， and highlights topics of equity， diversity， and other pro-social values， specifically。 Recommended if you’re interested in a broad set of models with which to understand how the world works， and the levers at our disposal to improve it。 。。。more
Michael
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honestly far too verbose and abstract for a "how to think" book honestly far too verbose and abstract for a "how to think" book 。。。more
Fernando Rodriguez-Villa
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Fascinating read。 It managed to explain a wide range of intense math/statistical topics in an approachable way。 It is definitely more technical than a pop science book （Sapiens， Sixth Extinction） but more accessible than a textbook。。。 take from that what you will。 The true test will be if I retained any of it for use in daily/life。 I certainly hope so。
Zeqiong Huang
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super concise and useful intro to model everything
Kevin Dineen
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I preface my review with this: I'm a beginner in data and formulas。 This book quickly went over my head， although I was able to grasp a decent amount。 Written clearly， the logical and math behind it was more beyond me。 Definitely will help to have some background with logic， mathematical formulas， and data analysis。 I preface my review with this: I'm a beginner in data and formulas。 This book quickly went over my head， although I was able to grasp a decent amount。 Written clearly， the logical and math behind it was more beyond me。 Definitely will help to have some background with logic， mathematical formulas， and data analysis。 。。。more
Ben Rogers
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Good book on models。 Most I know， some I learned。 2。9/5
Arne
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Compelling
Daniel Christensen
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OK。 So， this is a beast which is neither fish nor fowl。 It’s somewhere between popular science and a coursebook。Many model thinking is so hot right now （see Munger， Parish etc etc。）。 This book kinda fits into that genre。 On the other hand， it is also a much more serious treatment of how to apply analytic models， and it’s almost a textbook for his course: https://www。coursera。org/learn/model-。。。That said， the book doesn’t get into the nitty gritty of how to use each approach。 That’s not necessari OK。 So， this is a beast which is neither fish nor fowl。 It’s somewhere between popular science and a coursebook。Many model thinking is so hot right now （see Munger， Parish etc etc。）。 This book kinda fits into that genre。 On the other hand， it is also a much more serious treatment of how to apply analytic models， and it’s almost a textbook for his course: https://www。coursera。org/learn/model-。。。That said， the book doesn’t get into the nitty gritty of how to use each approach。 That’s not necessarily a problem – learning how to use an analytic technique ‘for realz’ takes time。 What I liked: I’m a researcher/ data analyst。 My stats skills are pretty solid， but are very much grounded in a psychology background （almost everything I do belongs to the family of regression/ structural equation models）。 What this did is blow things wide open and offer a lot of other possible views。The book uses a variety of examples to make it’s point。He also makes the argument of multiple models as offering complementary views and tools （a sort of pluralist approach）。 My favourite parts: The illustrations of using multiple models to offer complementary explanations of the same phenomenon， e。g。 the Global Financial Crisis， the Cuban missile crisis， and rising social inequality。Minor objections: Page is clearly a strong advocate for combining models （many model thinking）。 The book could maybe do with a little more discussion on the practicalities of combining models， and also potential errors/ drawbacks。Given Page talks about causality a bit， I think it could have done with its own chapter。 Selfishly， I’d love to see his take on Judea Pearl’s work， and how he compares and contrasts it to other approaches。TBH， my biggest objection is that some of the images and equations came through with very poor image quality on the kindle。 That problem is not unique to this book， but it’s still a bit annoying。All that said， it was good enough for me to enrol in the Coursera version。 。。。more
Joel
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More of a reference manual than a book to be read straight through。 Had some good tidbits here and there and the last chapter was great。 But I mostly found the real world examples to be the only part of this book that were intelligible。
Jim Mehnet
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The case made by the author is that social systems are very complex and often have vast numbers of what could be causal forces at play。 In physics， chemistry and biology， sciences from which social sciences emerge we have often had few enough variables that relatively simple models have been constructed since the enlightenment that are so surefooted at predicting fairly granular aspects of the future that we call them laws。 The social sciences （economics， psychology， political science， sociology The case made by the author is that social systems are very complex and often have vast numbers of what could be causal forces at play。 In physics， chemistry and biology， sciences from which social sciences emerge we have often had few enough variables that relatively simple models have been constructed since the enlightenment that are so surefooted at predicting fairly granular aspects of the future that we call them laws。 The social sciences （economics， psychology， political science， sociology， etc。） that are emergent from the physical sciences and have complexity that makes creating model that consistently predict future outcomes correctly enough to be called laws difficult at best。 Such models would likely be too complex for our minds to use effectively。So what to do？ Should we examine with a single model that is more detailed and more complex。 The author makes a strong case that often this leads to less understanding and is impossible to calibrate meaningfully。 But there is a proven way to make more accurate predictions。 If we simplify a problem in the social sciences we can model it and still gain valuable insight。 And then we can simplify the the model in several other contexts and model it in those ways。 The prime insight that I obtained from the book is that if we simplify a complex system problem in the context of different forces we gain a much better understanding of a system and can gain a better understanding of its future behavior and make better decisions。 The Model Thinker then goes on to explain and give examples of many types of model types。 These include but are not limited to: linear models， power models， network models， broadcast models， diffusion models contagion models， path dependent models， local interaction models， Markov models， systems dynamics models， game theory models， cooperation models， collective action models， etc。 Examples of problems are analyzed with every model introduced。 Of course even though the math shows the power of this processes， most of us don’t have intuition related to more complex math。 The author finishes the book in a way that addresses this。 Two complex system problems， opioid addiction and wealth inequality are examined with multiple models to show the power of this approach。 。。。more
Spencer
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Read all but the 2nd-to-last and 3rd-to-last chapters， because I ran out of time before the book was due back at the library。 I did read the final chapter。 I found the book to be very dense， but interesting at times。 The first 100 pages were tedious and largely review for me。 There were some interesting nuggets from game theory， signaling models， random walks， and spatial vs。 hedonic models。 All in all， like an encyclopedia that would be best digested in 6+weeks， each chapter lulling you to slee Read all but the 2nd-to-last and 3rd-to-last chapters， because I ran out of time before the book was due back at the library。 I did read the final chapter。 I found the book to be very dense， but interesting at times。 The first 100 pages were tedious and largely review for me。 There were some interesting nuggets from game theory， signaling models， random walks， and spatial vs。 hedonic models。 All in all， like an encyclopedia that would be best digested in 6+weeks， each chapter lulling you to sleep。 Or a reference book to come back to and skim to find an idea。 。。。more
Ben Hughes
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