Archive for ‘Science’ Category
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!
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.
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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Posted on 21 February 2012 by cjf
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. 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.
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!
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/.
Finally, here are three good audio-visual resources that explore issues of randomness further:
 Click here to read my previous essay on randomness where arguments for determinism are discussed.
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 fora.tv) 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?
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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Posted on 23 August 2011 by cjf
Each of us is connected through our parents and their parents and so forth to a first life form which has no parent and is composed and derived from non-biological forms. So each of us is intimately connected through our genealogical history to the non-biological Universe! Indeed, each of us is connected in this way to the whole Universe!!! It is this type of conceptual, big picture thinking that pervades Stephen Stearns’ free video course EEB 122: Principles of Evolution, Ecology, and Behavior at Open Yale Courses (OYC). Even though I do not think the separation between life and non-life is as clearcut as Stearns suggests, Jeannie and I thoroughly enjoyed our excursion into Biology with Stephen Stearns as our guide at OYC!
The Importance of Biology, Evolution, Ecology, and Behavior
Understanding biology is essential as civilization is inseparable from the great ecosystems upon which it is built and in which it is housed. Our future is inextricably linked to the always changing nature of the Earth-Biosphere system which provides our food and shelter. In EEB 122, we learned that the entire Earth has been sculpted by the biological technology we call “life“. At a more day-to-day human scale, medicine and health care are vital subjects in biology and in our economy (caring for the health of the ecosystem of cells, organs and their microscopic cohabitators known as “human” engages 9% of the economies in most OECD countries and nearly twice that in the USA). EEB 122 has a whole lecture devoted to medicine and more comments throughout the course.
Evolution theory has had a profound influence on modern thinking. From its nascent formulation by Charles Darwin, the theory of evolution has itself changed (evolved!) substantially over its first 150 years. I found it particularly interesting to learn that the modern theory is quite different from what my culture-imbued intuitions misled me to think. This course is eye-opening! Even if you disagree with some of the ideas of evolution (and who doesn’t have some questions and concerns about this subtle subject which is itself changing), this course corrects some of our endemic misperceptions. For example, Stearns asserts in lecture 3 that the notion of “survival of the fittest” is wrong! I had no idea that that iconic “sound bite” is but a dead-end on the road to the modern theory of evolution!
With current concerns about global warming and other stresses on our environment, ecology is a vitally important subject. The point is reinforced by the work of three of the recent winners of the Buckminster Fuller Challenge which deeply engages the subject of ecology. The 2011 winner, Blue Ventures, conserves threatened marine environments. The 2010 winner, Project Hope, restores savannas and grasslands lost to desertification with a comprehensive program featuring cattle management. The 2008 winner, John Todd’s Comprehensive Design for a Carbon Neutral World, restores the ecological devastation of the impact of mining in Appalachia. The principles of biology and ecology are essential to better understand and contribute to these and similar initiatives to improve our management of Earth’s ecosystems while raising the standard of living of every human being. EEB 122 explains some of the vital principles that will underpin any such solution.
In summary, OYC’s EEB 122 is an excellent introduction to the basic principles of biology needed to better understand medicine, health care, evolution theory, ecology, the behavior of organisms, and biological technologies such as the enterprising work of the Buckminster Fuller Challenge winners. Finally, and perhaps most importantly, EEB 122 gives a conceptually broad, biologically detailed introduction to one of the most enchanting visions of change ever developed: the theory of the evolution of species.
How We Used the EEB 122 Course Materials
Jeannie and I started watching EEB 122 around New Year’s. We watch courses like this for edutainment. That is, we do not plan to become professional biologists, instead we watch video courses as a form cultural enrichment: How do biologists think? What do they know? What is the current understanding of evolution? I am deeply curious about how the world works. Video courses like EEB 122 are deeply enriching in this regard.
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After some 30 years of waning, my interest in the assertion that the effects of sugar are poisonous was rekindled in April by reading a piece entitled “Is Sugar Toxic?” by Gary Taubes in the New York Times. When Taubes wrote admiringly of John Yudkin (1910–1995) (whose short 1972 book “Sweet and Dangerous” profoundly influenced me when I read it in the early 80s), the effect was electrifying: maybe he was right … vindicated after all these years! To sober myself up, I began researching our current understanding of the biochemistry and physiology of sugar metabolism. My research supported an interesting three hour discussion at the 10 July 2011 meetup of the Ben Franklin Thinking Society in Philadelphia.
Although the case against sugar is stronger now than it was in the 1970s, there is still no ironclad proof of its toxicity (more on that below). Sentiment against sweeteners with fructose in them (table sugar is one-half fructose) is growing because it has been implicated in several biochemical pathways associated with the so called metabolic syndrome. Metabolic syndrome is a complex of several (usually at least three) of the following factors: abnormal blood fat, high blood pressure, fatty liver, insulin resistance, and new fat deposition. Metabolic syndrome is very important because physicians now realize it is a more accurate predictor for diabetes, heart disease and cancer than any of the symptoms considered separately. The impact of this new perspective and the research that has ensued is starting to challenge conventional wisdom about the dietary factors involved in these diseases.
Taubes’ New York Times article starts by referring to the powerful, “viral”, 90-minute, YouTube video presentation by Dr. Robert Lustig indicting sugar. Lustig and Taubes convincingly lay out the case that fructose is a chronic toxin that causes metabolic syndrome. Therefore, it should be implicated as a causal factor in the obesity, diabetes, and cardiovascular disease pandemics. Taubes ends his article ominously by quoting prominent oncologists (cancer physicians) worrying that cancer could be caused in part by fructose & sugar as well (a claim Lustig did not make).
The Robert Lustig Video: Sugar: The Bitter Truth
Dr. Robert Lustig argues that the “fat is bad craze” has failed us: we reduced our fat intake, but the obesity and diabetes epidemics grew much more intense in the 1990s and 2000s. He observes that the fastest growing epidemic in obesity is among six month old babies! So the disparaging view that gluttony and sloth are the key factors in obesity appears absurd: babies don’t choose gluttony nor sloth — indeed, no one does! Could excess fat be a physiological problem and not a simple issue of “won’t power” (a phrase my grandfather used)? Perhaps our conventional wisdom is wrong? Lustig argues convincingly that a calorie is NOT just a calorie: some have worse physiological effects than others.
Lustig’s main thesis is that fructose acts in the body as a chronic toxin of the liver very much like alcohol. He observes that fructose is implicated in eight of the 12 chronic symptoms attributed to ethanol: high blood pressure, heart attack, abnormal blood fat, inflamation of the pancreas, obesity, liver dysfunction, insulin resistance, and habituation. In his not too difficult (but also not too easy) biochemistry lesson, Lustig points out that fructose and alcohol metabolism are nearly identical. Both hit the liver hard and both share many similar metabolic pathways. He concludes that “hepatic [liver-based] fructose metabolism leads to all of the manifestations of metabolic syndrome”.
John Yudkin’s Book “Sweet and Dangerous”
John Yudkin was a distinguished nutritionist and MD. He performed experimental studies and analyses of epidemiological data. As early as the 1960s, he concluded that sugar has no nutritional value beyond its calories and that if its effects were present in any other substance, it would be banned. This led to a heated debate with another distinguished nutritionist Ancel Keys who first proposed the link between dietary fat and heart disease. In his famous Seven Countries Study, Keys concluded that increased cholesterol and the western diet with its heavy load of saturated fats led to increases in heart disease and stroke. Keys effectively started the fat is bad craze that led to the US government’s recommendation that we reduce fat consumption from 40% to 30% of calories. Lustig argues that Keys may have done his statistical analysis incorrectly. Could the last 30 years of nutritional guidance be based on a statistics mistake?
On re-reading Yudkin’s book, I was impressed by his penetrating discussion of the techniques to prove causes of disease (a subject known as etiology). Yudkin observes that “absolute” proof requires pairing subjects into two groups who are as alike as possible with the exception of an experimental intervention. This “gold standard” in medical research is known as a clinical trial. Yudkin explains that the ethical and practical complications of such studies are enormous. Therefore, most nutritional and medical data comes from less reliable and more circumstantial evidence. Yudkin explains in basic terms the nature of epidemiological and experimental evidence and its limitations. Yudkin’s book is a great non-technical introduction to medical research.
I wish that more non-technical nutrition and health writing would advise us of the complications in applying insights from new medical research given the inherent limitations especially since so few of us understand reasoning with uncertainty. Health sciences writers would do well to “take a page” from Yudkin’s book.
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Posted on 24 February 2011 by cjf
As citizens aboard SpaceShip Earth, we need to understand the principles of science and technology that shape our world. We need this knowledge to become effective co-designers of the world that Humanity is collectively building for today and tomorrow. We need to conceptually apprehend and comprehend how the Universe actually operates so we can better contribute to steering the forces that continually reshape our worlds. What are the most important concepts needed to proficiently build, use, steward, and re-generate the infrastructure of civilization on an on-going basis? Where can we get the information needed in terms that is easy to understand, easy to relate to, easy to use, and relevant to the problems we all face today and into the future?
Although Berkeley‘s free video course Physics C10/LS C70V: Physics for future Presidents AKA Descriptive Introduction to Physics is not the answer to all of these questions, it will explain the basic physics that is necessary to critically evaluate much of the information that inundates us each day. This course will significantly increase your ability to think more confidently about the heady questions above. It is a first step.
Is there an on-line video course that does a better job than Physics C10/LS C70V of explaining the broadly relevant principles needed to understand the big issues of the day?
In 2000, when Richard Muller started teaching a new course, Physics C10/LS C70V: Physics for future Presidents AKA Descriptive Introduction to Physics, at Berkelely he asked himself what are the principles and facts from physics that a student should understand to be able to make effective decisions on the Big issues of the day should they become President of the United States? From this ambitious question, a course was designed that is eminently useful. Even though it is oriented to the non-scientist, Physics majors at Berkeley can take the course for credit toward their degree, meaning it is even useful for scientists!
In an introductory post on the OER (Open Educational Resources) Movement, I explained that the Internet now offers illions of educational resources many with free video lectures. I’ve spent several years searching for and enjoying on-line video courses and Physics C10 is the most broadly relevant course with the most critical information for understanding how the world works that I have found. Everyone should watch, enjoy and think about this most enriching class of some 35 hours of free on-line video lectures!
What resources do you use to get comprehensively educated about the principles of science and technology and how they are changing our civilization so rapidly? What is the most important or broadly useful OER course that you have found on the Internet?
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