Tag Archives: general system theory

the Phillips machine

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Here’s a 2009 New York Times column about a hydraulic model of the economy.

In the front right corner, in a structure that resembles a large cupboard with a transparent front, stands a Rube Goldberg collection of tubes, tanks, valves, pumps and sluices. You could think of it as a hydraulic computer. Water flows through a series of clear pipes, mimicking the way that money flows through the economy. It lets you see (literally) what would happen if you lower tax rates or increase the money supply or whatever; just open a valve here or pull a lever there and the machine sloshes away, showing in real time how the water levels rise and fall in various tanks representing the growth in personal savings, tax revenue, and so on. This device was state of the art in the 1950s, but it looks hilarious now, with all its plumbing and noisy pumps.

When it debuted back in November 1949, the leading thinkers at the London School of Economics crammed into the seminar room, some having come just to laugh, others gaping in amazement at the thing in the middle of the room, which had been cobbled together in a garage, with a pump cannibalized from an old Lancaster bomber.

Maybe it shouldn’t be quite so surprising. Before there were digital computers, there were “analog computers”, essentially circuits that could simulate various types of systems at equilibrium. Different types of systems have analogous building blocks and processes, like storages, flows, and resistances. As Howard T. Odum showed us, you can use these basic building blocks to model all types of systems, from physical to biological to socioeconomic.

designing genetic circuits

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This article in Science describes design of circuits that can be translated directly to DNA sequences, which when built will perform the function of the circuit.
Programming circuitry for synthetic biology

As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering.

children and patterns

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Here’s an interesting article in The Chronicle of Higher Education about Laszlo Polgár, a Hungarian who set out to turn his daughters into chess prodigies, and succeeded. A few interesting quotes:

There are three Polgár sisters, Zsuzsa (Susan), Zsofia (Sofia), and Judit: all chess prodigies, raised by Laszlo and Klara in Budapest during the Cold War. Rearing them in modest conditions, where a walk to the stationery store was a great event, the Polgárs homeschooled their girls, defying a skeptical and chauvinist Communist system. They lived chess, often practicing for eight hours a day. By the end of the 1980s, the family had become a phenomenon: wealthy, stars in Hungary and, when they visited the United States, headline news…

Laszlo believed that physical fitness was vital to intellectual success, so the girls played table tennis several hours a day, on top of their full day of chess and schooling. The parents were tireless in their devotion, buying every chess book they could, cutting out pages with past games, gluing them to cards, and storing it all in an old card catalog. They assembled more than 100,000 games; at the time, only the Soviet Union’s restricted chess archive could match it…

By the late 1980s, researchers had established that, contrary to what you might imagine, chess masters don’t tend to anticipate more moves as they gain skill. Rather, they gain expertise in recognizing patterns of the board, and patterns built out of those patterns. A question remained, however: How do they gain those skills? …

The focus of the article is on “nature vs. nurture” and the “10,000 hour rule” or “practice makes perfect”. What caught my attention though is the idea that children have a natural aptitude for pattern recognition. And systems are about patterns. I am thinking about H.T. Odum’s beautiful system diagrams, which are essentially circuits depicting the energy flows through any type of system. The building blocks are simple but they can be combined to describe very complex behavior in systems of any physical type. (Odum would have said they describe all the important aspects of social and economic systems too, but I haven’t decided if I agree with that yet.) So if young children of roughly average mental aptitude can memorize patterns in chess, could they learn to memorize Odum’s system patterns through repetition, perhaps through games? And if all children learned general systems theory in this way, could they be prodigies in solving the world’s complex problems later on? Are we focusing on entirely the wrong things in school?

Peter Checkland

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Peter Checkland is another system thinker that I have just discovered. Apparently he is well-known, but I find that systems thinkers are buried in a variety of disciplines, in this case management, and I wasn’t looking there.

This is from a 2000 journal article, Soft Systems Methodology: A Thirty Year Retrospective:

Although the history of thought reveals a number of holistic thinkers — Aristotle, Marx, Husserl among them — it was only in the 1950s that any version of holistic thinking became institutionalized. The kind of holistic thinking which then came to the fore, and was the concern of a newly created organization, was that which makes explicit use of the concept of ‘system’, and today it is ‘systems thinking’ in its various forms which would be taken to be the very paradigm of thinking holistically. In 1954, as recounted in Chapter 3 of Systems Thinking, Systems Practice, only one kind of systems thinking was on the table: the development of a mathematically expressed general theory of systems. It was supposed that this would provide a meta-level language and theory in which the problems of many different disciplines could be expressed and solved; and it was hoped that doing this would help to promote the unity of science.

These were the aspirations of the pioneers, but looking back from 1999 we can see that the project has not succeeded. The literature contains very little of the kind of outcomes anticipated by the founders of the Society for General Systems Research; and scholars in the many subject areas to which a holistic approach is relevant have been understandably reluctant to see their pet subject as simply one more example of some broader ‘general system’!

But the fact that general systems theory (GST) has failed in its application does not mean that systems thinking itself has failed. It has in fact flourished in several different ways which were not anticipated in 1954. There has been development of systems ideas as such, development of the use of systems ideas in particular subject areas, and combinations of the two. The development in the 1970s by Maturana and Varela (1980) of the concept of a system whose elements generate the system itself provided a way of capturing the essence of an autonomous living system without resorting to use of an observer’s notions of ‘purpose’, ‘goal’, ‘information processing’ or ‘function’. (This contrasts with the theory in Miller’s Living Systems (1978), which provides a general model of a living entity expressed in the language of an observer, so that what makes the entity autonomous is not central to the theory.) This provides a good example of the further development of systems ideas as such. The rethinking, by Chorley and Kennedy (1971), of physical geography as the study of the dynamics of systems of four kinds, is an example of the use of systems thinking to illuminate a particular subject area.

It’s sad to me to see his contention that general systems theory has failed.  It should be a central, foundational body of knowledge that people are trained in before they apply their focus to narrower fields. I have said many times, this would give a wider variety of intelligent people a shared body of knowledge, vocabulary, and respect for each other’s pursuits, and might accelerate the pace of innovation.

Dyson, Feynman, Hawking… Carson?

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Thinking back to my recent post about Freeman Dyson – a brilliant physicist who has suggested solutions to problems in biology, which biologists refuse to take seriously.

Here is what Richard Feynman has to say about scientists trying to solve puzzles outside their fields:

I believe that a scientist looking at nonscientific problems is just as dumb as the next guy — and when he talks about a nonscientific matter, he will sound as naive as anyone untrained in the matter…

In this age of specialization men who thoroughly know one field are often incompetent to discuss another. The great problems of the relations between one and another aspect of human activity have for this reason been discussed less and less in public. When we look at the past great debates on these subjects we feel jealous of those times, for we should have liked the excitement of such argument. The old problems, such as the relation of science and religion, are still with us, and I believe present as difficult dilemmas as ever, but they are not often publicly discussed because of the limitations of specialization.

Maybe, but is the solution then for everyone to specialize, accept the blinders that specialization causes, and never look beyond them? That can’t be right. The solution has to be for everyone to be trained in a comprehensive, general theory of system science. Then some people remain generalists, while others go on to specialize in a particular type or locality within that larger system theory. Then we would all have a common language and framework for talking to each other.

Take the case of Ben Carson, the “neuroscientist who can’t think“:

When Trump, an alumnus of the University of Pennsylvania’s Wharton School, says that climate change is a hoax, I can believe it’s a cynical lie pandering to the Republican base, rather than an index of his ignorance.  But when Carson, a retired Johns Hopkins neurosurgeon, denies that climate change is man-made, or calls the Big Bang a fairy tale, or blames gun control for the extent of the Holocaust, I think he truly believes it.

It’s conceivable that the exceptional hand-eye coordination and 3D vision that enabled Carson to separate conjoined twins is a compartmentalized gift, wholly independent of his intellectual acuity. But he could not have risen to the top of his profession without learning the Second Law of Thermodynamics (pre-meds have to take physics), without knowing that life on earth began more than 6,000 years ago (pre-meds have to take biology), without understanding the scientific method (an author of more than 120 articles in peer-reviewed journals can’t make up his own rules of evidence).  Yet what does it mean to learn such things, if they don’t stop you from spouting scientific nonsense? …

What I don’t get is how his rigorous scientific education and professional training gave Carson’s blind spots a pass.

The Feynman quote is in a Forbes article trying to refute Stephen Hawking talking about technological unemployment. From the Forbes article:

…the rise of the robots cannot possibly make us any poorer than we are now. And that’s in the very worst case: the worst that can possibly happen is that some other people become richer and we get to jog along much as we do now. That’s also the result that is vanishingly unlikely to actually happen. What is far more likely to happen is that we all, jointly, become vastly wealthier…

We have some mixture of human labour and machinery, automation, which produces the things that we consume today. Further, the only useful definition of income is what we’re able to consume. We’re not really interested in whether people have jobs or not, we also don’t care very much about income as income. The root point that we do care about is that people are able to consume things. Shelter, clothing, food, health care, the real point is that people get to eat, sleep under a roof, not be naked (except, of course, when that’s more fun), get treated for what ails them (possibly the result of that fun) and so on. Or, as Adam Smith said, the sole purpose of any production is consumption. It is only consumption, the ability to consume, which is the issue of any importance.

Well, I have a big philosophical problem that the idea that the purpose of life is consumption. What about love, art, achievement, leisure? But let’s stick to science and economics. I don’t have to be Stephen Hawking or even Richard Feynman to give some easy counter-examples. First, if we “produce” more, as measured in dollars changing hands, we can easily be degrading things that aren’t easily measured in dollars, like the atmosphere, forests, and oceans, for example. And eventually, the loss of these ecosystems could bring our civilization to its knees, making us very poor indeed in material terms, no how many dollars we thought we had. That’s a little theoretical, but for recent and obvious cases of technological unemployment, look at the displacement of agricultural workers in the southern U.S. in the early to mid-20th century, and the continuing poverty, ill health, and social problems of their descendants today. Or if you think racism was a larger factor than economic factors there (I think the two are overlapping and intertwined in many ways), look at the factory workers in Appalachia, both black and white, who were displaced by lower cost labor overseas. Again, their descendants are beset by widespread poverty, health and social problems which show no sign of getting better any time soon. So clearly, technological unemployment causes real poverty and suffering for some people, some places, and some times. The difference between these past examples and the AI future might be that it affects most people, most places, all the time, unless we find political solutions to spread the wealth.

And here is Stephen Hawking on exactly that subject in his recent “ask me anything” session:

The outcome will depend on how things are distributed. Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be toward the second option, with technology driving ever-increasing inequality.

Now, I’m not a famous physicist or even a brain surgeon, but that sounds about right to me.