THE METHOD: Pirsig, Scientific Relativism, and Rational Knowledge

 

 

 

 

 

Robert Pirsig in his world famous classic Zen and The Art of Motorcycle Maintenance shares the experience of his disillusionment with science when he was an undergraduate university student.

Pirsig’s Law

Pirsig noted a curious fact about doing research in his biochemistry lab. He found that the easiest part of experimentation is the thinking up of plausible hypotheses that may explain the phenomenon in question. Moreover, as he attempted to take up these hypotheses one by one and test them, their number did not decrease. Rather, it kept increasing!

Initially, Pirsig took a humorous view of the situation and even stated a law to express it:

“The number of rational hypotheses that can explain any given phenomenon is infinite.” (p. 139)

While this law is certainly not rigorously defended, the main point is that at any given point of time more hypotheses can be generated to explain a given phenomenon than can be tested. But if all hypotheses cannot be tested (because their number keeps increasing as experimentation continues) than the result of any single experiment at a given point of time is actually inconclusive: Which is to say that all scientific truth is relative, a function of time.

The nature of scientific method

When we closely inspect the nature of scientific method, it is easy to see why any scientific truth must be relative. Scientific method rests on a form of reasoning called as the hypothetico-deductive model. The model entails the following steps in any scientific inquiry:

1. You look at a problem (an unexplainable phenomenon) and first look for any previous explanations.

2. If no previous explanation is available, you construct and state an explanation yourself (the theory).

3. Next, you derive one or more very specific consequence(s)  (the hypotheses) that follows from the explanation in the step #2.

4. Finally, you test the hypothesis by collecting data and seeing whether they fit the opposite of the hypothesis in question.

This last point may well be unclear to those not familiar with scientific research, so here I go again:

The hypothesis from step 3 is called as the research hypothesis (also termed as the alternative hypothesis), the one that the experimenter is really interested in. For every research hypothesis, its corresponding opposite, the null hypothesis, is actually what is tested.

For example if the research hypothesis is “Tomato plants exhibit a higher rate of growth when planted in compost rather than in soil” then the null hypothesis would be “tomato plants do not exhibit a higher rate of growth when planted in compost rather than in soil”.

The experimenter really tests this null hypothesis. If the data do not fit the null hypothesis, it is rejected and the alternative hypothesis is then accepted.

No absolute proof for the research hypothesis

It is clear from the above explication that the scientific method can never really prove any given consequence (step #3) drawn from a given explanation (step #2). All it can do is to prove that the literal opposite of the research hypothesis does not conform to the observed variation in data.

This is what Christiaan Huygens meant when he said: “I believe that we do not know anything for certain, but everything probably.” Albert Einstein expressed the same idea more precisely: “No amount of experimentation can ever prove me right; a single experiment can prove me wrong.” [Source for both quotes is the wikipedia page on the Scientific Method.]

Since the evidence is really not in favor of any single explanation, a multitude of explanations can be surmised. It will be a matter of time before the currently favored explanation for any given phenomenon is ultimately disproven, with another alternative hypothesis taking its place.

Scientific truth as a function of time

This leads us directly to scientific relativism. Throughout the history of science, new and changing explanations have emerged for old facts. Explanations remained “true” for a certain period of time and were finally replaced by a newer truth. As Pirsig observes: Some scientific truths seemed to last for centuries, others for less than a year.

Authors, J. A. Fodor and Z. W. Pylyshyn made a similar observation with reference to a theory of perception: “Even in the respectable sciences, empirical knowledge is forever going reformulation, and any generation’s pet theories are likely to look naive when viewed from the perspective of thirty or forty years on.”

Pirsig additionally observes: “the scientific truths of the twentieth century seem to have a much shorter life-span than those of the last century because scientific activity is now much greater”. Thus the greater the number of hypotheses, the greater the amount of activity to test them, with ever increasing stimulation of more and more hypotheses. Instead of selecting one truth from a multitude you are increasing the multitude.

Is the Explainer of Chaos it’s Producer?

The ultimate conclusions Pirsig reaches about science are rather interesting, definitely harsh and, in my personal opinion, certainly true.

Through multiplication upon multiplication of facts, information, theories and hypotheses it is science itself that is leading mankind from single absolute truths to multiple, indeterminate, relative ones. The major producer of the social chaos, the indeterminacy of thoughts and values that rational knowledge is supposed to eliminate, is none other than science itself.

Pirsig’s final verdict on rationality:

It begins to be seen for what it really is–emotionally hollow, esthetically meaningless, and spiritually empty.

SCIENCE|RELIGION: Observations of a Scientist upon Science and Reality

Bernard d’Espagnat is a French theoretical physicist and a philosopher of science. He received the Templeton Prize in March this year upon work that shows how science cannot fully explain reality. The Templeton is the largest prize in the world in terms of monetary value and is annually awarded by the Templeton Foundation to acknowledge work that finds a common ground between science and religion and to individuals who reaffirm the spiritual dimension of life.

Bernard d’Espagnat’s major contribution in science is his work on several aspects of quantum mechanics. It was this work which lead him to explore the nature of reality and to question the disregarding attitude many scientists have towards the philosophical questions thrown up by quantum physics.

d’Espagnat’s ideas on the doomed division between science and ‘ultimate reality’

From The Guardian:

“What quantum mechanics tells us, I believe, is surprising to say the least. It tells us that the basic components of objects – the particles, electrons, quarks etc. – cannot be thought of as “self-existent”. The reality that they, and hence all objects, are components of is merely “empirical reality”.

This reality is something that, while not a purely mind-made construct as radical idealism would have it, can be but the picture our mind forces us to form of … Of what ? The only answer I am able to provide is that underlying this empirical reality is a mysterious, non-conceptualisable “ultimate reality”, not embedded in space and (presumably) not in time either.”

From Princeton University Press (In a review of his book On Physics and Philosophy):

His overall conclusion is that while the physical implications of quantum theory suggest that scientific knowledge will never truly describe mind-independent reality, the notion of such an ultimate reality–one we can never access directly or rationally and which he calls “veiled reality”–remains conceptually necessary nonetheless.

From his Templeton page:

“the things we observe may be tentatively interpreted as signs providing us with some perhaps not entirely misleading glimpses of a higher reality and, therefore, that higher forms of spirituality are fully compatible with what seems to emerge from contemporary physics.”

In a statement prepared for the news conference, d’Espagnat pointed out that since science cannot tell us anything certain about the nature of being, clearly it cannot tell us with certainty what it is not.

From the BBC report on the news:

His concept of an ultimate reality – as he terms it, “the ground of things” – is only glimpsed, not explicitly described, by science.

Science, he said, “is aimed not at describing ‘reality as it really is’ but at predicting what will be observed in such-and-such circumstances”.

From the statement delivered by d’Espagnat on the prize ceremony:

At this point I’d like to draw your attention on the fact that, if true, this conception of mine has two significant consequences.

One of them is that if indeed it is our mind that, due to its own structure, carves all objects out of the “ground of things,” obviously we cannot any more picture mind to ourselves as being itself an emanation of (some class of) objects. If the notion “emanation” is here to be kept, we may only claim that mind emanates “from the ground of things.” As we shall immediately see, the difference is far from being a negligible one.

For indeed – and this is nothing else than the second consequence I just mentioned – this “ground of things,” this Real, quite obviously is not a thing. Clearly it is not imbedded in space, and presumably not in time either. Let us call it “Being” if you like. Or “the One,” following
Plotinus.

BOOKS: What I learned from “Godel, Escher, Bach” – Part II

Continued from Part I.

In this post we will look more closely at “symbols”, “levels of meaning” and “isomorphism”.

Symbols – the carriers of meaning

Strange loops arise in systems that are powerful enough to capture meaning. Meaning is achieved when networks of signals stand for one particular concept in the world.  Thus each letter of the alphabet signals a particular sound. These signals in turn get arranged into a pattern – a word – that refers to one particular object or idea from the external world.

In a similar vein, the firing of a single neuron in response to a specific stimulus is a signal. For instance, studies on the visual cortex have shown that different specific neurons are stimulated upon presentation of and variation in very specific features of the visual stimuli such as length and orientation.

When we sense (see, hear, or touch) a new object, all the neurons responding to its various features are activated in the pertinent area of our brain are activated and form a neural circuit. Circuits in different regions of the brain are themselves interconnected: As you talk about an apple you are using i) the muscles of your respiratory system to create the right sounds, ii) the corresponding visual circuit to visualize the apple, and iii) circuits in the language areas representing the word apple as well as the corresponding phonetic pattern needed to pronounce the word correctly.

The above described network caters to one meaningful concept in the world. It is to such networks that Hofstadter applies the term symbol. The world is full of such information-preserving  symbols and below are some examples:

Meaning is not inherent in the symbol

By now it is clear that the power of a symbol does not reside in the signals that it is made up of; rather it is the correspondence with a specific concept from the outer world. Every word will be a meaningless pattern of sound if it was not associated with something we are familiar with. Words such as ‘mother’, ‘money’ and ‘love’ evoke strong personal reactions in most of us, not because there is something in the special arrangement of those particular sounds, but because of what each of these words refers to.

Thus the fact that each constituent of the symbol stands for a particular sound is explicit (i.e. apparent). On the other hand, the fact that the pattern as a whole stands for something else altogether is implicit (i.e. hidden) –  the meaning is not readily apparent to anybody who’s not  well-practiced in the use of these symbols (e.g. a child, a person not familiar with the English language).

Meaning is thus independent of any rules for combining signals to produce patterns. That is how, even though the formal system in Principia Mathematica was especially designed to shun explicit self-reference, it is by association with a different (a higher, and less readily apparent) level of meaning that self-reference is achieved.

Isomorphism – reading meaning into patterns of signals

The key is that the transition from the explicit to the implicit level is  information-preserving. In math, such a case wherein elements of two sets correspond with each other in an information-preserving fashion (math theorems and Godel numbers in the above example) is called an isomorphism.

Thus the word “table” is isomorphic to that piece of furniture on which my PC sits. So is the neural circuit that gets activated in my mind when I think of a table. The symbol ‘=’ is isomorphic to the concept of “is equal to”.  Genes are isomorphic to the protein synthesized from them. A code is isomorphic to the text of the message it hides. The camera film is isomorphic to the color photograph printed from it.

In short, human thinking and culture is fraught with isomorphisms of various kinds.

As far as we cannot detect and read the isomorphism, the structural similarity between two different sets of elements, we will be oblivious to the fact that one is a message encoded by the other. In Hofstadter’s terms, meaning is induced in the explicit lower level matter (or components of a pattern) by identifying its isomorphism with a real world concept at a more abstract level.

We recently have had an eerie reminder of this fact, when a scientist Craig Hogan realized that he may have hit upon some observations supporting the holographic principle. It seems that the totality of information on all the particles in our 3D universe may be contained on the 2D cosmic horizon…

Isomorphisms and mental life

The swirls of neuoronal activity back and forth across the brain are isomorphic to mental activity. In other words: consciousness of our inner life is attained because we can read off the explicit neurological processes at a much more higher and implicit level.

When you are looking at a TV screen, the data you are receiving is nothing but a fluctuating pattern of pixels. But you are not conscious of this ‘lower level’ of the message. You can simply read off the higher level meanings coded for by those pixels – feat we call as perception.

This shows how its totally unnecessary to be conscious of the lower level in order to read the implicit. Fluent readers are rarely conscious of the exact letter sequences making up the words they are reading. A practiced reader in a book describing highly visual scenes will simply see the scene by scene depiction of the story on the pages. The feel of reading is completely replaced by the sense of watching in such instances…

In sum, both intelligence and consciousness may be redefined as our capacity to perceive the meaningful isomorphisms in the world and within ourselves.

Tangled hierarchies and strange loops

Many a times a clear differentiation of ‘lower’ and ‘higher’ levels is possible when dealing with two isomorphic sets. A majority of the examples of such sets given above illustrate this differentiation. There are times however when such a clear differentiation is not possible, since levels keep leading back to each other.

It is the entangledness of our concepts that leads to recursion in human thinking. All our concepts are interrelated, are constantly activated by each other, and this constant exchange among themselves and with information from the outside leads either to modification or reinforcement of every concept.

We define our preferences and loves in relation to our own selves. We reflect upon the outer world and interacting with it obtain further information from ourselves thus re-affirming, enhancing or adjusting our self-concepts. All this modification is indeed not just at the abstract, conceptual changes. The changes are reflected in the underlying patterns of connections across networks of neurons. This is what happens in any level-crossing feedback loop. The system does not just mirrors meaning, it has the capacity to  change in response to changing information.

That is also why human intelligence is definitely superior to machine intelligence. Where a computer will get hanged, the human will leave the level on which it was working (for e.g. some office task) and work on other levels to solve the problem (for e.g. confronting the supervisor who didn’t explain the task fully, confronting and modifying one’s own level of knowledge and skill required to achieve the task, etc.).

The beauty of Hofstadter’s ideas is that they apply equally well to human intelligence and human consciousness. Fluid Concepts and Creative Analogies is the result of his research into the intricacies of human cognition, whereas I am a Strange Loop presents more fully Hofstadter’s ideas of the emergence of the human ‘I’.

An example of the all-tangled up semantic network underlying GEB (Click on the image to see in full size)

After I have completed reading I am a Strange Loop, I’ll find some excuse to post about it as well, InshaAllah!