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Review of “The Model Thinker” by Scott Page
Complex Adaptive Systems Modeling volume 8, Article number: 1 (2020)
Scott E. Page
The Model Thinker: What You Need to Know to Make Data Work for You.
Hatchett Book Group.
448 pages; ISBN-10: 0465094627; ISBN-13: 978-0465094622. 18.56 USD.
Modeling is a means of creating abstractions—of items and systems—either from the real-world, a perceived one, or even one’s imagination. Often, modeling helps develop representative simplified versions of a complex system with the aim of communication or education of a given audience (Epstein 2008).
While modeling has previously been applied to a wide range of domains and systems, of interest is the fact that models are extremely useful in the context of Complex Adaptive Systems (CAS) (Holland 1992). Models can range from Agent-based Models (Chaudhry 2016; Iantovics 2012) and Intelligent agents (Chira et al. 2012) to mathematical modeling, (Chaudhry et al. 2014) and complex social networks (Batool and Niazi 2014; Jin et al. 2013). Often times, advanced artificial intelligence techniques are also used for understanding CAS such as the use of swarms (Inbarani et al. 2014), ant methods (Pintea et al. 2011), and even fuzzy logic (Ramadan 2017) among other methods (Crişan et al. 2017).
“Model Thinker” by Page (2018) represents a single volume encompassing many facets of Complexity, Complex Adaptive Systems, and more. While we were hoping to present a review much earlier during the year, due to various reasons and also perhaps due to the wealth of information presented in the book, it has taken us a while to develop this review. Here, we hope to be able to present a review worthy of the book itself.
The book represents the culmination of an effort by Prof. Page to solidify numerous ideas about modeling and simulation of complex systems—all in a single volume. While existing techniques have previously been presented such as in Gilbert (2019), Newman (2018), Mitchell (2009), Holland (2014), Holland (2000), Niazi and Hussain (2012), somehow the depth and writing style used in this this particular book was certainly missing. The origin of the book is based on a course taught by the Professor first at Michigan, and then later, online at Coursera. The course has been a success. It has a massive following with up to a million students enrollments in recent offerings.
To build upon the words of the author, the book gives a vision of numerous angles, lenses, and perspectives to view the world. The focus is on 3 different classes of models. The first one being simplifications, followed by mathematical analogies, and exploratory, artificial constructs. In short, the book proposes the so-called “Many-model” approach.
The book starts with some background and history. And it then quickly moves on to justifications for employing modeling. The key emphasis is that by looking at other models, one gets to know techniques which can then be used to develop models of one’s very own. However, the author clarifies the paradox of modeling in terms of—models needing to be simplified, stripped down versions of reality to be useful and then not being able to cover every aspect in their entirety—just on their own—hence the many model approach.
In terms of book structure, the second and third chapters motivate the many-model approach further. This is followed by a set of challenges about modeling humans. The next twenty-something chapters simply outline different models and associated classes. The book concludes by applying the many-model approach to two seemingly very different problems—namely the “Opioid Epidemic” and “income inequality”.
The primary audience of this book is multidisciplinary researchers which are new to the theory of modeling and simulation of complex systems. In this respect, the book serves its purpose well.
What new information is presented?
The book presents a wealth of information gradually introducing the reader to the theory and moving on to the practice of modeling and simulation of complex systems.
Is there anything missing?
The book presents a lot of interesting ideas and concepts. However, it is not and does not aim to be exhaustive. That, however, would actually be a plus point. Consider the fact that already 20 or so modeling classes have been introduced in this book. So, interested reader can simply delve into related work moving on from here.
Any further suggestions
Ideally, it would have really helped if the many-model approach could have been flattened in the form of a Unified framework for modeling. In some respects, it appears to Russell and Norvig’s book “Artificial Intelligence, a Modern Approach” (Russell and Norvig 2016) except that the focus of the Norvig book is on agents. So, it would have been nice if the approach would have been simplified in a Unified framework.
Why read it?
This book is a must-read for anyone interested in modeling, simulation, or Complexity in general. The audience ranges from Social scientists to Engineers and even Medical professionals. The reason for that is the book offers insights on how to think. And that too, not just in a single way.
Availability of data and materials
Batool K, Niazi MA (2014) Towards a methodology for validation of centrality measures in complex networks. PLoS ONE 9(4):90283
Chaudhry QA (2016) An introduction to agent-based modeling modeling natural, social, and engineered complex systems with NetLogo: a review. Springer, New York
Chaudhry QA, Hanke M, Morgenstern R, Dreij K (2014) Surface reactions on the cytoplasmatic membranes-mathematical modeling of reaction and diffusion systems in a cell. J Comput Appl Math 262:244–260
Chira C, Dumitrescu D, Pintea CM (2012) Learning sensitive stigmergic agents for solving complex problems. Comput Inf 29(3):337–356
Crişan GC, Pintea C-M, Palade V (2017) Emergency management using geographic information systems: application to the first romanian traveling salesman problem instance. Knowl Inform Syst 50(1):265–285
Epstein JM (2008) Why model? J Artif Soc Soc Simul 11(4):12
Gilbert N (2019) Agent-based models, vol 153. Sage Publications, Incorporated, New York
Holland JH (1992) Complex adaptive systems. Daedalus 121(1):17–30
Holland JH (2000) Emergence: from chaos to order. OUP Oxford, Oxford
Holland JH (2014) Complexity: a very short introduction. OUP Oxford, Oxford
Iantovics BL (2012) Agent-based medical diagnosis systems. Comput Inf 27(4):593–625
Inbarani HH, Banu PN, Azar AT (2014) Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 25(3–4):793–806
Jin L, Chen Y, Wang T, Hui P, Vasilakos AV (2013) Understanding user behavior in online social networks: a survey. IEEE Commun Mag 51(9):144–150
Mitchell M (2009) Complexity: a guided tour. Oxford University Press, Oxford
Newman M (2018) Networks. Oxford University Press, Oxford
Niazi MA, Hussain A (2012) Cognitive agent-based computing-i: a unified framework for modeling complex adaptive systems using agent-based & complex network-based methods. Springer, New York
Page SE (2018) The model thinker: what you need to know to make data work for you. Hachette, New York
Pintea C-M, Chira C, Dumitrescu D (2011) Sensitive ants in solving the generalized vehicle routing problem. Int J Comput Commun Control 6(4):731–738
Ramadan RA (2017) Fuzzy brain storming optimisation algorithm. Int J Intell Eng Inform 5(1):67–79
Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, New York
The authors declare that they have no competing interests.
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Niazi, M.A., Iantovics, L.B. & Temkin, A. Review of “The Model Thinker” by Scott Page. Complex Adapt Syst Model 8, 1 (2020). https://doi.org/10.1186/s40294-019-0068-8
- Social networks
- Many-model approach