Introduction to Complexity

This course moved the needle on my understanding of this fascinating subject space, and introduced some rigour into what was earlier mostly just doe-eyed interest: fractals, the information-energy relationship, complex adaptive systems, universal computation, autopoiesis and network theory.

Together with Scott Page's Model Thinking, this course brought alive for me new patterns and truths that the 21st century is asking us to appreciate and embrace, as part of the process of defining a new narrative for humanity. Centralization, control, simplistic programmed thinking, ossification and stasis of viewpoints, the linearity bias etc are out of sync with the evolutionary reality of our times; individuals and institutions who are not smelling the coffee will be left cold in the stone age. Complex thought has to enter educational and human development processes and curricula sooner rather than later. The Santa Fe Institute is a pillar of this much needed shift.

The course provided me with a solid foundation for delving deeper into the philosophical, organizational and trans-disciplinary aspects of complexity and complex systems - including but not limited to the work of Paul Cilliers, Alfonso Montuori and Edgar Morin.

The Guest lectures were very nice and added diversity to the curriculum - I particularly enjoyed the talks on Complexity Economics.

And finally, Melanie is such a sweetheart with her infectious energy, patient enumeration and rich erudition - that makes the course every moment's worth!

This course provides a nice introduction into many exciting topics in complex systems science. Ideas from nonlinear dynamics, information theory and network science are reviewed in an engaging manner by the course instructor Melanie Mitchell in a series of video lectures. Short quizzes and simple simulation excercises (using the NetLogo environment) help to grasp the material. The course is light on mathematical sophistication, which probably makes it accessible for a wide audience.

For me personally the strong point of this course was what I would call "scientific tourism"! In many interviews with practioners in the field of complex systems science a myriad of interesting applications of the ideas discussed in the course were exhibited. Through these interviews one gets acquainted with some of the open questions and can appreciate the interconnections within this highly interdisciplinary field.

It is a nice course but I think it is highly overrated. The thing is, I think it is quite shallow and while it is a great introduction to the field of complex systems for amateurs and new comers, it also downgrades the field and is presented in a rather shallow manner and fashion leaving aside the main questions that any area of science should be trying to answer instead of trying to come up with yet another silly definition of complexity or cherry picked measure of complexity. Missed completely the are of algorithmic complexity that another course int he Complexity Explorer does cover, fortunately, that of Algorithmic Information Dynamics. But this one, despite being at the core of complexity science, leaves aside things like time and space complexity, computability hierarchies and algorithmic information theory. It should at least send students to the other courses relevant on those topics.

An Exceptional Resource — Accessible, Practical, and Open to All

This course is a rare gem: a truly accessible introduction to complexity science. The material is presented step-by-step, assuming no prior knowledge beyond basic algebra — yet it engages learners at all levels, from absolute beginners to those with prior programming or modeling experience. Its flexible structure allows students to choose the depth and pace that work best for them, ensuring comprehensive grasp of the material.

The guest lectures add valuable interdisciplinary breadth, demonstrating real-world applications across diverse fields. As a Ph.D. student, I was able to immediately apply several of the concepts to my own academic research in studying consciousness — including an important open question that I had been trying to solve for quite some time!

The fact that this course is offered for free is remarkable. I’m deeply grateful to the Santa Fe Institute for making this resource available to all learners.

I was also thrilled to find that the course is largely compatible with an iPad; nearly all of the material was accessible through this device, and the few components that were not did not impact course completion.

Highly recommended for anyone interested in exploring complex systems, interdisciplinary thinking, or scientific inquiry more broadly.

Intro to complexity delivers just what it promises: an overview of Complexity Science, covering all the branches of this vital and fascinating new discipline. It walks a fine line on mathematical rigor to be accessible to those without a strong math background while still being useful to quants. It's hard (impossible?) to please everyone, but Melanie Mitchell strikes a good balance.

The class covers Chaos Theory, fractals, entropy and Information Theory, genetic algorithms and cellular automata, emergence and Network Theory, evolution and self-organizing systems. It's a large amount of material but it's possible to finish the course in two weeks., though the nominal pacing more like ten. (One week per chapter.)

The class makes extensive use of netlogo computer models to illustrate concepts. The models are extremely useful, because they allow the student to run virtual experiment. Even if you have zero programming experience the models are easy to run, and the netlogo programming language is easy to learn. Depending on your interest in programming, you can dive as deep as you want.

The 21st is going to be a very complex century. This class should be given to every high school student, indeed everyone who wants to understand how our interconnected world functions (or doesn't).

I am a little confused. I am reading some authors of the supplementary materials and I dont understand why they are celebreting new discoveries. Let me explain it, If you evaluate the knowledge about complexity you can see that they arent enough predictive models, scientists here are talking about earthquakes but they cant predict when and where they will occur and nor the magnitude. Another talked about investing in stock market but he didt quantify his earnings on the time line(there are many succsess history about it in world and many of them arent serious), others talked about eclectic point of view on nature and they do analogies, they dont consider many theories about the field in which they are giving their opinion. I have to tell the following words:

The field of complexity is growing with weak sustent.

Thanks

P.D I'll try to read Dr Mitchell's book.

Hiving listened and watched the video on an introduction to complexity, I find that without realizing it, I have "emerged" my academic topic of macroeconomics (of our sociasl system) from what most prior economists have regarded it as being impossible to properly follow. It has previously been steeped in doubt and vague ideas, which (mostly for political, commercial and religious doctrine reasons) it has confused its students and followers.

They claim that this due to its complexity. However, my resulting methodology in using agent-based operations has solved this difficulty of resolving the subject, into a form that is much more logical and scientific, so that it becomes much easier to understand. In fact it seems to me that this process of emergence is a logical engineered way for avoiding all the confusing kinds of complexity and that this concept applies so strongly that we don't really need to become involved in the complex possibilities at all!

All we need is the desire to generailze in the same way as mathematicians do when are developing a theorem. Consequently in my case I claim that macroeconomics is actually a true science which should replace the pseudo-science which the experts have been saying over the past 250 years or so of its existance (of Adam Smith and David Ricardo) and nearly 100 years of its recognition (of J.M. Keynes).