Posts Tagged ‘Synergetics’

Addenda to My Conversation With Harold Channer

datePosted on 17 December 2014 by cjf

Harold Channer invited me to the studios of MNN (Manhattan Neighborhood Network) in New York City to record two one-hour editions of the TV program “Conversations with Harold Hudson Channer” on Tuesday the 25th of November, 2014. Since few things I write or speak come out fully baked, I thought I’d add a few additional thoughts to clarify, improve, or correct some of my comments. Since I value discussion, I sprinkled my remarks with many questions which I hope will elicit your feedback in the comments.

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I participated in the ReVIEWING Black Mountain College 4: Looking Forward at Buckminster Fuller’s Legacy conference on September 28-30, 2012 in Asheville, NC, USA. I gave two talks (click on the links below to see the PDF presentations):

  • Education Automation Now and in the Future. In this talk I recognize Buckminster Fuller as one of the conceptual founding fathers of the Open Educational Resources (OER) movement, detail six of his educational ideas, and give a brief review of several OER courses I’ve taken to indicate the kind of comprehensive education now possible using freely available on-line courses.
  • Synergetics and Model Thinking. In this talk I synthesize Scott E. Page’s Model Thinking with Buckminster Fuller’s Synergetics. I introduce both subjects, then discuss the importance of model thinking. Then I sketch some ideas about how Model Thinking and Synergetics can inform a more incisive approach to science.

Please share any thoughts you might have about these presentations in the comments. I would value your feedback.

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The Importance of Model Thinking

datePosted on 14 June 2012 by cjf

Models can help us understand, predict, strategize, and re-design our worlds. This is the profound lesson from Scott E. Page’s engaging on-line Coursera offering on Model Thinking. I was particularly interested in this 10 week course because Buckminster Fuller instilled in me a deep appreciation for models. With this course, Scott Page reinforced and enhanced that appreciation in spades. Also, like Bucky, Page makes his penetrating approach accessible to a very broad audience. This is a great course for anyone with even rudimentary algebra skills.

In addition to reviewing the course, I will also suggest that model thinking is a new more incisive kind of science. This approach and its nascent toolkit for understanding, decision-making, prediction, strategy, and design is vitally important for practitioners of all types. Model thinking may be just the type of tool humanity needs to solve some of its thorniest problems. As such its arrival into broader consciousness is not a moment too soon!

So if you want to be out there helping to change the world in useful ways, it’s really really helpful to have some understanding of models.
— Scott E. Page

Why Model Thinking

There are many ways to model the world. One of the most popular is with proverbs or short pithy sayings (our modern media seem to particularly love this deeply flawed “sound bite” approach to knowledge). As Scott Page points out, there are opposite proverbs too. For instance, the opposite of “nothing ventured, nothing gained” is “better safe than sorry.” Proverbs and their more elaborate cousins, allegories, can model or represent the world with persuasive stories, but they provide little discerning power and little basis for deeper understanding. In contradistinction, model thinking with its greater concern for precision can help us more carefully distinguish a complex of important factors with their interrelationships and behaviors. Therein lies its power!

Is intuition sufficient? No! Philip Tetlock, Robyn Dawes and others have demonstrated that simple naive models outperform experts of all stripes. In 1979 Dawes wrote a seminal paper, The Robust Beauty of Improper Linear Models in Decision Making, which showed the effectiveness of even “improper” linear models in outperforming human prognostication. Tetlock has made the most ambitious and extensive study of experts to date and finds that crude extrapolation models outperform humans in every domain he has studied.

That is not to say that models are “right”. Page emphasizes that all models are “wrong” too! Which leads to his most profound insight in the course: you need many types of models to help think through the logic of any given situation. Each model can help check, validate, and build your understanding. This depth of understanding is essential to make better decisions or predictions or build more effective designs or develop more effective strategies to achieve your goals.

Is intuition important? Yes, absolutely! The many model thinker relies upon intuition to select and critically evaluate a battery of models or to construct new or modified models when appropriate. These models help test our intuition. Intuition helps tests the models! Writing out a model often identifies facets and elements of the situation which intuition misses. Intuition is essential to find the aspects of the models that are a bit off the mark — and all models are a bit off. Model thinking is not “flying on instruments” or turning control over to mathematical or computer models. Instead it is about evaluating and comparing diverse models to test, build, fortify, and correct our intuitions, decisions, predictions, designs, and strategies.

Fascinating Models

Page’s course is filled to the brim with fascinating models! One of the first models Page introduces is Thomas Schelling’s segregation model which represents people as agents on a checkerboard. We discover deep and unexpected insights about how people sort themselves into clusters where everyone looks alike, for example, the segregation of neighborhoods based on race, ethnicity, income, etc. It is the first of many agent-based models to be discussed.

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In a poignant lecture on The Logic of Science (412 MB QuickTime video download; 6800 word transcript), Stephen Stearns provides some of the most practical results from the philosophy of science possible in an introductory 45 minute lecture. Together with reading T. C. Chamberlin‘s essay The Method of Multiple Working Hypotheses, watching Kevin Kelly’s lecture The Next 100 Years of Science: Long-term Trends in the Scientific Method (video at and participating in a recent discussion on On the Nature, Being, and Logic of Science at The Ben Franklin Thinking Society, I’m inspired to formulate and share some of my thoughts about the ever-changing “ways of knowing” that we call science. Even more so because the 2011 Design Science Symposium that I am helping to organize will try to broach the subject of the science in Buckminster Fuller’s Synergetics.

Perhaps, the most intriguing thing I learned from the Stearns lecture was the importance of the method of multiple working hypotheses. I decided to read the source, a 1965 reprint of T. C. Chamberlin’s classic 1890 essay “The Method of Multiple Working Hypotheses” (here is a printable PDF). I was hit by the insidiousness of the bias inherent in the hypothetico-deductive model of science which unfortunately is still taught as dogma in many science classes today. I was shocked to realize how counterproductive it can be to focus on developing and testing a simple working hypothesis, a style of thinking that I have frequently used and must now grow beyond!

Chamberlin convinced me that in science and in life we must challenge ourselves to imagine a comprehensive array of possible explanations (hypotheses). Only in this way can we get sufficient perspective to clearly see the kind of questions, observations and experiments that might tease out Truth from the inherent complexity of Universe. Wow, isn’t that the essence of Buckminster Fuller’s Synergetics: comprehensive thinking?

[In practicing the method of multiple working hypotheses] the mind appears to become possessed of the power of simultaneous vision from different standpoints. Phenomena appear to become capable of being viewed analytically and synthetically at once. — T. C. Chamberlin, 1890

My next step on the ladder to understanding scientific knowledge was Kevin Kelly’s fascinating 02006 talk on Long-term Trends in the Scientific Method (video). Kelly suggests that science is driven by applying knowledge to itself recursively or self-similarly. Kelly included a timeline which I have modified to give a slice through some milestones in the history of scientific ways of knowing:

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Society and Our Technology Built World

datePosted on 2 June 2011 by cjf

The interrelationships between society and technology run deep. We all partake and participate in the unfolding technology evolution “discussion” Invention by Design by Henry Petroski that is our lives. The tools we use, try out, improvise, critique, and/or advocate are our minimal contributions to this discussion. The accidents of technological history set the context for the discussion. We are all technologists entangled in a technological world! Technology has been the main (perhaps the only?) means by which human progress has been achieved with tools like the pencil, slide fastener (or zipper), jet airplane, water systems, skyscrapers, bridges, and computers all dramatically changing society. Henry Petroski’s great short book “Invention by Design: How Engineers Get from Thought to Thing” explores the design and engineering arts in the full richness of their social context in nine intriguing case studies.

I first read Invention by Design in February 1999. Recently I was re-reading it when Michael Tweed of the The Ben Franklin Thinking Society invited me to lead the group’s Science & Technology meetup every month. That led to the Discussion: Engineering Failures & Society on 8 May 2011. Here are some thoughts reflecting on Petroski’s book, the 8 May meetup, and further cogitating about the big picture of society and technology. Hopefully these notes and your feedback will help us better understand the technological world at the core of our ever changing civilization.

What is Technology?

Technology is the catch-all term used to describe objects and the networks, systems, and infrastructures in which they are embedded, as well as the patterns of use that we impose upon them and they upon us. Technology is clearly context-dependent and ever evolving. — Henry Petroski

Petroski’s definition suggests that civilization itself may be technology. So it would seem that technology embraces culture, values, psychology, history, and the multidimensional elements of the environment (materials science, biology, anthropology, geophysics, chemistry, etc.). Buckminster Fuller goes further:

In its complexities of design integrity, the Universe is technology. The technology evolved by man is thus far amateurish compared to the elegance of nonhumanly contrived regeneration. Man does not spontaneously recognize technology other than his own, so he speaks of the rest as something he ignorantly calls nature. — Buckminster Fuller, Synergetics, 172.00-173.00

By taking Petroski’s “networks, systems, and infrastructures” to the next level of “design integrities” and identifying it as technology, Bucky leads us to the biggest of big pictures: Universe itself! As social creatures we often think of society as the big picture. I think his point is well made: technology is an inhernet component of Universe itself. Human society is our storied Earth-developed technology. It seems likely that Human society will become the “brain” managing the regenerative ecological functions of Gaia, the theory that Earth is “alive”. If that happens, the storied technology of Earth would probably become even more syntropic and powerful than what life has achieved thus far. Regardless, society and the technology with which it is built are inextricably intertwined!

Design and Engineering in Society

Design and engineering are the arts of consciously working to evolve and develop our technological infrastructure to improve our worlds. Petroski emphasizes the role of society in the engineering process and vice versa in these illuminating quotes:

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In Buckminster Fuller’s essay Guinnea Pig B, he lays out the hypothesis that the purpose of Humans in Universe is to support the integrity of cosmic evolution:

In our immediate need to discover more about ourselves we also note that what is common to all human beings in all history is their ceaseless confrontation by problems, problems, problems. We humans are manifestly here for problem-solving and, if we are any good at problem-solving, we don’t come to utopia, we come to more difficult problems to solve. That apparently is what we’re here for, so I therefore conclude that we humans are here for local information-gathering and local problem-solving with our minds having access to the design principles of the Universe and — I repeat — thereby finally discover that we are most probably here for local information-gathering and local-Universe problem-solving in support of the integrity of eternally regenerative Universe.
—R. Buckminster Fuller

This precept of the function of Humans in Universe is, to me, one of the most deeply motivating responsibilities that I have ever taken on as a working hypothesis. I love the way it engages me as a co-designer in Universe. And I love the way in which it inspires me to a higher purpose.

Recently I read a National Geographic news article that Time Will End in Five Billion Years, Physicists Predict and my mind went into a tizzy. The following fairy tale emerged:

A Cosmic Evolution Fantasy

Captain’s log of Brenda S______ dated 5,000,002,010 CE (that is, 5 three-illion, 2 one-illion and 10 years CE).

Galaxy Cluster (NASA)“I have just returned to Earth after a 7,042 year survey of our galaxy cluster testing the integrity of the fabric of space-time throughout the isotropic vector matrix. What a trip! Our team has verified that all the millennia of research and development by countless humans and other sentients throughout Universe has succeeded in holding time together: the Universe will continue for the foreseeable future! We have verified that all vital parameters for managing the entirety of the cosmos are within fail-safe tolerances!

“Of course, there are a few issues (there always are);

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Although I have always been interested in the mind and thinking, I have been suspicious of psychology and the cognitive sciences. Recently, I’ve been impressed by several TED Talks that address new ideas in the sciences of the mind. These subjects are starting to provide valuable insights into how the world really works. It is still wise to be skeptical, but we might have made enough mistakes in psychology that we now have some groundwork upon which to start figuring out what is really going on in our heads.

So I was delighted with the chance to go into more depth in the science of decision making by reading Jonah Lehrer’s 2009 book How We Decide and participating in a discussion with the Ben Franklin Thinking Society. First, some overall impressions of the book. I thought Lehrer gave a good account of how the emotional brain works and some strengths and weaknesses in our decision making. I really value how he presents so many examples of experiences and experiments to illustrate the subject. His conclusion, though adequate, did not bring it altogether for me (cognitive dissonanace is a good thing and it helped me write this post!). Jeannie was turned off by Lehrer’s bone-chilling accounts of airplane crashes and psychopaths. However, we both learned a lot about the neuroscience of decision making. For me it was a good read, if not a great book.

The nature of emotions

One major omission from the book was the lack of a diagram showing the relationships among the brain regions discussed. Jeannie drew a rough sketch entitled Brain Turmoil below to give some sense of how the pieces fit.Brain Turmoil by Jeannie Moberly

Apparently, the brain uses dopamine-mediated “prediction” neurons to recognize patterns (a dopamine “high” if the pattern fits and a “low” if the pattern is “off”). This effect delivers our “feelings” to a decision making center in the orbitofrontal cortex (OFC). In Lehrer’s synthesis the brain considers these often conflicting signals from its various parts until it forms a decision. Jeannie’s designation Brain Turmoil is apt: chapter 7 is entitled “The Brain Is an Argument”.

As I re-read Lehrer’s text trying to pinpoint what emotions are, I found his description too vague. Still I synthesized this working hypothesis: emotions are the self-communicated feelings, intuitions, or instincts formed by dopamine-mediated pattern detection centers in the brain. This gives a nice concrete notion of the nature of emotions that seems to fit well enough with the text and my experience. Does anyone know a better characterization of emotions?

Mistake Mystique

The message from the (sometimes excessively repetitive) middle part of the book is that both our “rational” and “emotional” brains can make serious mistakes. Lehrer recounts the emotional brain’s proclivity to find a pattern in any situation leading to grave errors whenever randomness is in play. For example, he explains the gamblers fallacy where one is rapturously deceived by occasional but completely random winnings leading to thoughts that “my turn has come” and the likelihood of bigger losses. He debunks the notion of streaks in sports citing the research of Gilovich, Vallone & Tversky that shows they are just random events that our brain misinterprets. There are more stories of this nature in the book. I had already encountered several from reading Fooled by Randomness by Nassim Nicholas Taleb which goes into great depth on this deficiency in the brain. Taleb details our weaknesses, but Lehrer also highlights some of our strengths and addresses how to make better decisions.

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