On 11 March 2019, 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”. To expand my thinking on topics from the second interview, here are four addenda:

• The Thinking Society
• Other references from the interview
• A brief Critique of AI (Artifical Intelligence)
• My addenda to interviews with Harold Channer to date

Read the rest of this essay »

It may be that the presumed dichotomy between determinism and randomness is superficial and illusory. Determinism is the world view that events result from an unalterable causal chain. It models the world as a clock whose behavior can be inferred by scientific investigation. Stocasticity or randomness is the world view that uncertainty pervades experience. It models the world as a dice game with unpredictable behavior.

Many thinkers including Einstein, Buckminster Fuller, and D’Arcy Wentworth Thompson have argued in support of the traditional deterministic world view[1]. However, Quantum mechanics, machine learning, and behavioral economics are three prominent areas which have helped realign modern thinking to apprehend that randomness and uncertainty may be fundamental and pervasive. Leonard Mlodinow in a 2008 book goes further and argues that randomness rules our lives.

In preparing for and discussing randomness at a recent meetup of the Ben Franklin Thinking Society, I started to gravitate to the hypothesis that uncertainty and determinism may be like inside and outside or concave and convex. They may be both real, both partially right and partially wrong, both revelatory and misleading. It may be that each perspective is a “tuning in” to only part of a reality that is both-neither[2].

The principle of functions states that a function can always and only coexist with another function as demonstrated experimentally in all systems as the outside-inside, convex-concave, clockwise-counterclockwise, tension-compression couples.

— R. Buckminster Fuller, Synergetics 226.01

Here are several ways to see the dual and co-occurant qualities of the stochastic and deterministic models or world views.

In a deterministic model of the world, the fixed set of laws that govern everything apply to every quanta of energy or their constituents. So computing the state of the world requires applying these fixed laws to each such quanta from some initial state and iterating through each picosecond of time. Clearly, this is computationally infeasible except for the computer known as Universe itself. So any effective simulation or calculation will entail estimates and approximations, that is, randomness. Unwittingly, randomness imposes itself into the system!

Conversely, in a stochastic model the relationships between data are given by frequencies with respect to their sample space, the set of possible outcomes. What could be more deterministic than the elementary counting of frequencies? Indeed probability is basically a form of advanced counting in ratios. Deterministic indeed!

Now consider measurement. The basis of a scientific model involves measurable parameters. Data are measurements. Science has determined that all measurements involve uncertainty. MIT physicist Walter Lewin puts it emphatically: “any measurement that you make without any knowledge of the uncertainty is meaningless!” Measurement theory is built upon the law of error which is a principle of the science of randomness. Hard data acquires its validity and persuasiveness from the science of chance!

The key to understanding measurement is understanding the nature of the variation in data caused by random error.
— Leonard Mlodinow

On the other hand, the law of error is a central principle in statistics, the science of inferring probabilities from observed data. Such inference is the gold standard of scientific truth. The techniques of scientific inference are based on the mathematics of randomness. Like all mathematics, the theory is definite, rigorous, and repeatably verified by logic, proof and experiment. The sciences of probability and statistics are rigorous and deterministic like all mathematics!

Even in a fundamentally deterministic world, our understanding, decision-making, strategies, predictions, measurements, and designs are predicated upon uncertainty and randomness. To be effective we must be cognizant of these lingering unavoidable uncertainties.

Conversely, even in a fundamentally uncertain world ruled by randomness, pattern and order emerge and can be identified. To be effective we can and should seek the design and structure permeating through the apparent randomness.

From these considerations, I conclude that randomness and determinism always and only coexist. They are inseparable. Each provides a spectacular, incisive perspective on reality. The careful thinker or practitioner should be facile in using both types of models to get a more wholistic, more complete picture of the world in which we find ourselves. This is evidence that both-neither should be our guiding principle in seeking truth!

Do you find the argument compelling? Is it sound? Can you help me improve it? Do you see other ways in which these two models interpenetrate and interaccommodate? How do you see the interrelationship between determinism and randomness?

To better develop my understanding of a more complete set of models (beyond superficial determinism vs. stochasticity), I am excited about Scott E. Page‘s new and just started on-line video course on Model Thinking. I think we need many diverse models to sharpen our thinking and uncover subtleties in the complex systems and theories upon which our civilization is built. I am looking forward to wrapping my head around the 21 or so models in this course. You can register for the Model Thinking course by filling out the form at http://www.modelthinker-class.org/.

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

Finally, here are three good audio-visual resources that explore issues of randomness further:

• Radiolab’s exquisite podcast on Stocasticity.
• World Science Festival: The Illusion of Certainty: Risk, Probability, and Chance.
• Persi Diaconis’ talk “On Coincidences” (scroll down near the bottom where the 1998 lectures live or search for Diaconis).

[1] Click here to read my previous essay on randomness where arguments for determinism are discussed.
[2] Credit to Tom Miller for the wonderful expression both-neither.

The interrelationships between society and technology run deep. We all partake and participate in the unfolding technology evolution “discussion” 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:

Read the rest of this essay »

For the past couple of years, Jeannie and I have been engaged as students using so-called “open educational resources” (OER). We’ve “taken” a number of courses at MIT’s OpenCourseWare (OCW), OpenYaleCourses as well as dabbling in material from numerous other schools around the Internet.

I first read Buckminster Fuller’s short book Education Automation many years ago. I was amazed at Fuller’s foresight in advocating so much of what has now become the OER movement. Then a week ago I led a Ben Franklin Thinking Society discussion on Buckminster Fuller and the Open Educational Resources Movement. Here are my reflections on what I learned from preparing and participating in that discussion.

The Open Educational Resources (OER) Movement

The OER movement is simply an Internet-powered expansion of the time-honored practice of students and teachers sharing materials and ideas. On the Internet this sharing can include video and guided tutorials as well as traditional media such as lecture notes, homework assignments, textbooks, and exams. All of these materials were more difficult and more expensive to share before high-bandwidth Internet and modern computer systems became widespread. A group of educators has tried to define the OER movement in the 2007 CapeTown Open Education Declaration. Here is a short excerpt which gives the gist:

Unlocking the promise of open educational resources

We are on the cusp of a global revolution in teaching and learning. Educators worldwide are developing a vast pool of educational resources on the Internet, open and free for all to use. These educators are creating a world where each and every person on earth can access and contribute to the sum of all human knowledge. They are also planting the seeds of a new pedagogy where educators and learners create, shape and evolve knowledge together, deepening their skills and understanding as they go.

During the discussion, I asked participants if they had used any OER materials. Many of them had not. But I was excited to learn that one of the participants studied Linear Algebra with video lectures by Gilbert Strang. Jeannie and I put more time into that excellent course (even doing all of the homework, quizzes, and two and a half final exams) than any other OER course we’ve worked through.

As a self-learner, one of the most important elements of OER courses to me is that I can choose how to use the materials (unlike in school where one is more or less told what to do). For example, there are some courses where I just want an overview or a feeling for the subject, but I may not need to master the material. Like when we studied Introductory Biology at MIT’s OCW, we watched the videos and only briefly looked at the lecture notes. We skipped the homework and the tests. We quickly ignored the parts that were not, at that moment, of interest. I think this is a big improvement over school where I frequently suffered from wanting to go into more depth than the course in some parts and less depth in others. Using OER I can get the education I want, when I want it!

It should be noted that the OER movement has been partly inspired (according to this good review article on open educational resources in Communications of the ACM) by the FOSS (Free and Open Source Software) movement. I find this fascinating since I have long been involved in the FOSS / Linux world (I’ve written about that extensively in the managing FOSS blog). Fuller’s global vision has foreseen elements of both movements.

To find out more about OER, the wikipedia entry http://en.wikipedia.org/wiki/Open_educational_resources can get you started.

Buckminster Fuller on Education: Prescient Harbinger of the OER Movement

To prepare for the Ben Franklin Thinking Society discussion, I re-read Education Automation twice. That led to these five quotes on Bucky’s thinking on education including how he foresaw elements of the OER Movement. The quotes and my commentary expand the discussion to address some broader issues in education as well. The quotes are all from Education Automation which was published way back in 1962.

Read the rest of this essay »