hat tip: Human Current episode 25
In this episode, Haley interviews TK Coleman to discuss how humans allow their conflicting mental models to influence the way they handle controversial topics like racism. TK also shares how understanding context and patterns within human systems ultimately empowers us to actively contribute to human progress.
I generally prefer the harder sciences among Human Current’s episodes, but even episodes on the applications in other areas are really solid. I’m glad to hear about TK Coleman’s overarching philosophy and the idea of “human beings” versus “human doings.”
Also glad to have the recommendation of General Systems Theory: Beginning With Wholes by Barbara G. Hanson as a more accessible text in comparison to Ludwig von Bertalanffy’s text. The gang at Human Current should set up an Amazon Affiliate link so that when I buy books they recommend (which happens frequently), it helps to support and underwrite their work.
Highlights, Quotes, Annotations, & Marginalia
Reality is objective, but meaning is contextual.
—Barbara Hanson, General Systems Theory: Beginning with Wholes quoted within the episode
In this episode, Haley talks with physicist, complexity scientist, and MIT professor, Cesar Hidalgo. Hidalgo discusses his interest in the physics of networks and complex system science and shares why he believes these fields are so important. He talks about his book, Why Information Grows: The Evolution of Order, from Atoms to Economies, which takes a scientific look at global economic complexity. Hidalgo also shares how economic development is linked to making networks more knowledgeable.
Quotes from this episode:
“Thinking about complexity is important because people have a tendency to jump into micro explanations for macro phenomenon.” — Cesar Hidalgo
“I think complex systems give you not only some practical tools to think about the world, but also some sort of humbleness because you have to understand that your knowledge and understanding of how the systems work is always very limited and that humbleness gives you a different attitude and perspective and gives you some peace.” — Cesar Hidalgo
“The way that we think about entropy in physics and information theory come from different traditions and sometimes that causes a little bit of confusion, but at the end of the day it’s the number of different ways in which you can arrange something.” — Cesar Hidalgo
“To learn more complex activities you need more social reinforcement.” — Cesar Hidalgo
“When we lead groups we have to be clear about the goals and the main goal to keep in mind is that of learning.” — Cesar Hidalgo
“Everybody fails, but not everyone learns from their failures.” — Cesar Hidalgo
“Learning is not just something that is interesting to study, it is actually a goal.” — Cesar Hidalgo
A solid interview here with Cesar Hidalgo. His book has been incredibly influential on my thoughts for the past two years, so I obviously highly recommend it. He’s got a great description of entropy here. I was most surprised by his conversation about loneliness, but I have a gut feeling that’s he’s really caught onto something with his thesis.
I also appreciated about some of how he expanded on learning in the last portion of the interview. Definitely worth revisiting.
What does a JPEG have to do with economics and quantum gravity? All of them are about what happens when you simplify world-descriptions. A JPEG compresses an image by throwing out fine structure in ways a casual glance won't detect. Economists produce theories of human behavior that gloss over the details of individual psychology. Meanwhile, even our most sophisticated physics experiments can't show us the most fundamental building-blocks of matter, and so our theories have to make do with descriptions that blur out the smallest scales. The study of how theories change as we move to more or less detailed descriptions is known as renormalization.
This tutorial provides a modern introduction to renormalization from a complex systems point of view. Simon DeDeo will take students from basic concepts in information theory and image processing to some of the most important concepts in complexity, including emergence, coarse-graining, and effective theories. Only basic comfort with the use of probabilities is required for the majority of the material; some more advanced modules rely on more sophisticated algebra and basic calculus, but can be skipped. Solution sets include Python and Mathematica code to give more advanced learners hands-on experience with both mathematics and applications to data.
We'll introduce, in an elementary fashion, explicit examples of model-building including Markov Chains and Cellular Automata. We'll cover some new ideas for the description of complex systems including the Krohn-Rhodes theorem and State-Space Compression. And we'll show the connections between classic problems in physics, including the Ising model and plasma physics, and cutting-edge questions in machine learning and artificial intelligence.
Mark Newman is a British physicist and Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, as well as an external faculty member of the Santa Fe Institute. He is known for his fundamental contributions to the fields of complex networks and complex systems, for which he was awarded the 2014 Lagrange Prize.
No recent scientific enterprise has been so alluring, so terrifying, and so filled with extravagant promise and frustrating setbacks as artificial intelligence. But how intelligent—really—are the best of today’s AI programs? How do these programs work? What can they actually do, and what kinds of things do they fail at? How human-like do we expect them to become, and how soon do we need to worry about them surpassing us in most, if not all, human endeavors?
From Melanie Mitchell, a leading professor and computer scientist, comes an in-depth and careful study of modern day artificial intelligence. Exploring the cutting edge of current AI and the prospect of 'intelligent' mechanical creations - who many fear may become our successors - Artificial Intelligence looks closely at the allure, the roller-coaster history, and the recent surge of seeming successes, grand hopes, and emerging fears surrounding AI. Flavoured with personal stories and a twist of humour, this ultimately accessible account of modern AI gives a clear sense of what the field has actually accomplished so far and how much further it has to go.
Melanie Mitchell, has Farrar, Straus & Giroux assigned your book a publicist yet? I’d love to get an advance review copy when they’re available.
What is artificial intelligence? Could unintended consequences arise from increased use of this technology? How will the role of humans change with AI? How will AI evolve in the next 10 years?
In this episode, Haley interviews leading Complex Systems Scientist, Professor of Computer Science at Portland State University, and external professor at the Santa Fe Institute, Melanie Mitchell. Professor Mitchell answers many profound questions about the field of artificial intelligence and gives specific examples of how this technology is being used today. She also provides some insights to help us navigate our relationship with AI as it becomes more popular in the coming years.
Definitely worth a second listen.
This racial dot map is an American snapshot; it provides an accessible visualization of geographic distribution, population density, and racial diversity of the American people in every neighborhood in the entire country. The map displays 308,745,538 dots, one for each person residing in the United States at the location they were counted during the 2010 Census. Each dot is color-coded by the individual’s race and ethnicity. The map is presented in both black and white and full color versions. In the color version, each dot is color-coded by race.
Maps & spatial analysis: One-dot one-person map for the entire United States: Introduction to geo-scripting in R & Python: Awesome blog with cool maps and the codes behind them by James C…
This video presents the basic idea of the so-called Coleman Boat.
In this episode, Haley interviews research professor and leader of the Self-Organizing Systems Labat UNAM, Carlos Gershenson. Gershenson discusses findings from his book, Complexity: 5 Questions, which is comprised of “interview style contributions by leading figures in the field of complexity”. He also shares his own perspectives on the past, present and future of complexity science, as well as how philosophy plays a role in the emergence of science.
In this episode, Haley interviews Stephen Wolfram at the Ninth International Conference on Complex Systems. Wolfram is the creator of Mathematica, Wolfram|Alpha and the Wolfram Language; the author of A New Kind of Science; and the founder and CEO of Wolfram Research. Wolfram talks with Haley about his professional journey and reflects on almost four decades of history, from his first introduction to the field of complexity science to the 30 year anniversary of Mathematica. He shares his hopes for the evolution of complexity science as a foundational field of study. He also gives advice for complexity researchers, recommending they focus on asking simple, foundational questions.
In this episode, Angie talks with systems educator and award-winning author, Linda Booth Sweeney. Booth Sweeney describes her work as a systems educator and explains why understanding systems is so important. She shares many wonderful examples and stories of patterns (and feedback loops) that show up in everyday life and explains how seeing a pattern is the very first step toward influencing change. Booth Sweeney also talks about her books and why storytelling is such an instrumental tool in her work.
Some awesome ideas hiding in here. Definitely worth a second listen as well as bookmarking some of Sweeney’s books to read in the future. I particularly like the idea of systems thinking for children via storytelling. Some of the ideas here have some overlap with ideas in Big History.