Also, what's the best not-necessarily-open textbook on the subject? I've been meaning to dig into the topic in depth for a while, I could use a good textbook.
Nonlinear Dynamics and Chaos by Steven Strogatz is pretty good. Nonlinear dynamics is one of the backbones of complex systems, so it's worth delving into. Graph theory/topology is a close second. Complex Networks by Latora, Nicosia, and Russo is a nice introduction to the graph-theoretical perspective. Complexity: A Guided Tour by Melanie Mitchell is a nice and brief introduction. I can also recommend Spin Glasses and Complexity by Stein and Newman. Spin glasses are basically the "model organism" of complexity science, so it wouldn't hurt to get acquainted with them.
The book is designed to support an introductory class on systems - eg a 100 level or first year class. It's a little more technical than you might find in a typical gen-ed quantitative reasoning class, but the material is presented in a way that I think would be approachable from a STEM or non-STEM career emphasis. I trained as an engineer, so I could be overstating that... There is a pedagogical library, pyCX [0]. Overall, I like the design of the book, but would say the intended audience is someone relatively early on their analytic journey and may not serve the interests of the HN reader well.
It has a much broader perspective than Allen Downey's "Think Complexity" [1], which is discrete systems (computational systems) focussed. Maybe you could think about Downey's book is a non mathematical approach to algorithms.
Scott E Page's book "The Model Thinker" is more mathematical, at an undergrad engineering level, and is more of a survey of models than a taxonomic overview of systems modeling, which is what Sayama's [2] book, above, offers. A thesis of Page's book is that there can be many ways to model a problem, and multiple models help.
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[ 2.6 ms ] story [ 23.4 ms ] threadAlso, what's the best not-necessarily-open textbook on the subject? I've been meaning to dig into the topic in depth for a while, I could use a good textbook.
It has a much broader perspective than Allen Downey's "Think Complexity" [1], which is discrete systems (computational systems) focussed. Maybe you could think about Downey's book is a non mathematical approach to algorithms.
Scott E Page's book "The Model Thinker" is more mathematical, at an undergrad engineering level, and is more of a survey of models than a taxonomic overview of systems modeling, which is what Sayama's [2] book, above, offers. A thesis of Page's book is that there can be many ways to model a problem, and multiple models help.
[0] https://github.com/hsayama/PyCX [1] http://www.allendowney.com/wp/books/ [2] http://bingweb.binghamton.edu/~sayama/