Determinism, Indeterminism, and the nature of the laws of physics…

The laws of physics are causal, but this fact does not imply that they can be used to determine each and everything that you feel should be determinable using them, in each and every context in which they apply. What matters is the nature of the laws themselves. The laws of physics are not literally boundless; nothing in the universe is. They are logically bounded by the kind of abstractions they are.


Let’s take a concrete example.

Take a bottle, pour a little water and detergent in it, shake well, and have fun watching the Technicolor wonder which results. Bubbles form; they show resplendent colors. Then, some of them shrink, others grow, one or two of them eventually collapse, and the rest of the network of the similar bubbles adjusts itself. The process continues.

Looking at it in an idle way can be fun: those colorful tendrils of water sliding over those thin little surfaces, those fascinating hues and geometric patterns… That dynamics which unfolds at such a leisurely pace. … Just watching it all can make for a neat time-sink—at least for a while.

But merely having fun watching bubbles collapse is not physics. Physics proper begins with a lawful description of the many different aspects of the visually evident spectacle—be it the explanation as to how those unreal-looking colors come about, or be it an explanation of the mechanisms involved in their shrinkage or growth, and eventual collapse, … Or, a prediction of exactly which bubble is going to collapse next.


For now, consider the problem of determining, given a configuration of some bubbles at a certain time t_0, predicting exactly which bubble is going to collapse next, and why… To solve this problem, we have to study many different processes involved in the bubbles dynamics…


Theories do exist to predict various aspects of the bubble collapse process taken individually. Further it should also be possible to combine them together. The explanation involves such theories as: the Navier-Stokes equations, which govern the flow of soap water in the thin films, and of the motion of the air entrapped within each bubble; the phenomenon of film-breakage, which can involves either the particles-based approaches to modeling of fluids, or, if you insist on a continuum theory, then theories of crack initiatiation and growth in thin lamella/shells; the propagation of a film-breakage, and the propagation of the stress-strain waves associated with the process; and also, theories concerning how the collapse process gets preferentially localized to only one (or at most few) bubbles, which involves again, nonlinear theories from mechanics of materials, and material science.

All these are causal theories. It should also be possible to “throw them together” in a multi-physics simulation.

But even then, they still are not very useful in predicting which bubble in your particular setup is going to collapse next, and when, because not the combination of these theories, but even each theory involved is too complex.

The fact of the matter is, we cannot in practice predict precisely which bubble is going to collapse next.


The reason for our inability to predict, in this context, does not have to do just with the precision of the initial conditions. It’s also their vastness.

And, the known, causal, physical laws which tell us how a sensitive dependence on the smallest changes in the initial conditions deterministically leads to such huge changes in the outcomes, that using these laws to actually make a prediction squarely lies outside of our capacity to calculate.

Even simple (first- or second-order) variations to the initial conditions specified over a very small part of the network can have repercussions for the entire evolution, which is ultimately responsible for predicting which bubble is going to collapse next.


I mention this situation because it is amply illustrative of a special kind of problems which we encounter in physics today. The laws governing the system evolution are known. Yet, in practice, they cannot be applied for performing calculations in every given situation which falls under their purview. The reason for this circumstance is that the very paradigm of formulating physical laws falls short. Let me explain what I mean very briefly here.


All physical laws are essentially quantitative in nature, and can be thought of as “functions,” i.e., as mappings from a specific set of inputs to a specific set of outputs. Since the universe is lawful, given a certain set of values for the inputs, and the specific function (the law) which does the mapping, the output isĀ  uniquely determined. Such a nature of the physical laws has come to be known as determinism. (At least that’s what the working physicist understands by the term “determinism.”) The initial conditions together with the governing equation completely determine the final outcome.

However, there are situations in which even if the laws themselves are deterministic, they still cannot practically be put to use in order to determine the outcomes. One such a situation is what we discussed above: the problem of predicting the next bubble which will collapse.

Where is the catch? It is in here:

When you say that a physical law performs a mapping from a set of input to the set of outputs, this description is actually vastly more general than what appears on the first sight.

Consider another example, the law of Newtonian gravity.

If you have only two bodies interacting gravitationally, i.e., if all other bodies in the universe can be ignored (because their influence on the two bodies is negligibly small in the problem as posed), then the set of the required input data is indeed very small. The system itself is simple because there is only one interaction going on—that between two bodies. The simplicity of the problem design lends a certain simplicity to the system behaviour: If you vary the set of input conditions slightly, then the output changes proportionately. In other words, the change in the output is proportionately small. The system configuration itself is simple enough to ensure that such a linear relation exists between the variations in the input, and the variations in the output. Therefore, in practice, even if you specify the input conditions somewhat loosely, your prediction does err, but not too much. Its error too remains bounded well enough that we can say that the description is deterministic. In other words, we can say that the system is deterministic, only because the input–output mapping is robust under minor changes to the input.

However, if you consider the N-body problem in all its generality, then the very size of the input set itself becomes big. Any two bodies from the N-bodies form a simple interacting pair. But the number of pairs is large, and worse, they all are coupled to each other through the positions of the bodies. Further, the nonlinearities involved in such a problem statement work to take away the robustness in the solution procedure. Not only is the size of the input set big, the end-solution too varies wildly with even a small variation in the input set. If you failed to specify even a single part of the input set to an adequate precision, then the predicted end-state can deterministically become very wildly different. The input–output mapping is deterministic—but it is not robust under minor changes to the input. A small change in the initial angle can lead to an object ending up either on this side of the Sun or that. Small changes produce big variations in predictions.

So, even if the mapping is known and is known to work (deterministically), you still cannot use this “knowledge” to actually perform the mapping from the input to the output, because the mapping is not robust to small variations in the input.

Ditto, for the soap bubbles collapse problem. If you change the initial configuration ever so slightly—e.g., if there was just a small air current in one setup and a more perfect stillness in another setup, it can lead to wildly different predictions as to which bubble will collapse next.

What holds for the N-body problem also holds for the bubble collapse process. The similarity is that these are complex systems. Their parts may be simple, and the physical laws governing such simple parts may be completely deterministic. Yet, there are a great many parts, and they all are coupled together such that a small change in one part—one interaction—gets multiplied and felt in all other parts, making the overall system fragile to small changes in the input specifications.

Let me add: What holds for the N-body problem or the bubble-collapse problems also holds for quantum-mechanical measurement processes. The latter too involves a large number of parts that are nonlinearly coupled to each other, and hence, forms a complex system. It is as futile to expect that you would be able to predict the exact time of the next atomic decay as it is to expect that you will be able to predict which bubble collapses next.

But all the above still does not mean that the laws themselves are indeterministic, or that, therefore, physical theories must be regarded as indeterministic. The complex systems may not be robust. But they still are composed from deterministically operating parts. It’s just that the configuration of these parts is far too complex.


It would be far too naive to think that it should be possible to make exact (non-probabilistic) predictions even in the context of systems that are nonlinear, and whose parts are coupled together in complex manner. It smacks of harboring irresponsible attitudes to take this naive expectation as the standard by which to judge physical theories, and since they don’t come up to your expectations, to jump to the conclusion that physical theories are indeterministic in nature. That’s what has happened to QM.

It should have been clear to the critic of the science that the truth-hood of an assertion (or a law, or a theory) is not subject to whether every complex manner in which it can be recombined with other theoretical elements leads to robust formulations or not. The truth-hood of an assertion is subject only to whether it by itself and in its own context corresponds to reality or not.

The error involved here is similar, in many ways, to expecting that if a substance is good for your health in a certain quantity, then it must be good in every quantity, or that if two medicines are without side-effects when taken individually, they must remain without any harmful effects even when taken in any combination—that there should be no interaction effects. It’s the same error, albeit couched in physicists’ and philosopher’s terms, that’s all.

… Too much emphasis on “math,” and too little an appreciation of the qualitative features, only helps in compounding the error.


A preliminary version of this post appeared as a comment on Roger Schlafly’s blog, here [^]. Schlafly has often wondered about the determinism vs. indeterminism issue on his blog, and often, seems to have taken positions similar to what I expressed here in this post.

The posting of this entry was motivated out of noticing certain remarks in Lee Smolin’s response to The Edge Question, 2013 edition [^], which I recently mentioned at my own blog, here [^].


A song I like:
(Marathi) “kaa re duraavaa, kaa re abolaa…”
Singer: Asha Bhosale
Music: Sudhir Phadke
Lyrics: Ga. Di. Madgulkar


[In the interests of providing better clarity, this post shall undergo further unannounced changes/updates over the due course of time.

Revision history:
2019.04.24 23:05: First published
2019.04.25 14:41: Posted a fully revised and enlarged version.
]

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Flames not so old…

The same picture, but two American interpretations, both partly misleading (to varying degrees):

NASA releases a photo [^] on the FaceBook, on 24 August at 14:24, with this note:

The visualization above highlights NASA Earth satellite data showing aerosols on August 23, 2018. On that day, huge plumes of smoke drifted over North America and Africa, three different tropical cyclones churned in the Pacific Ocean, and large clouds of dust blew over deserts in Africa and Asia. The storms are visible within giant swirls of sea salt aerosol (blue), which winds loft into the air as part of sea spray. Black carbon particles (red) are among the particles emitted by fires; vehicle and factory emissions are another common source. Particles the model classified as dust are shown in purple. The visualization includes a layer of night light data collected by the day-night band of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPP that shows the locations of towns and cities.

[Emphasis in bold added by me.]

For your convenience, I reproduce the picture here:

Aerosol data by NASA

Aerosol data by NASA. Red means: Carbon emissions. Blue means: Sea Salt. Purple means: Dust particles.

Nicole Sharp blogs [^] about it at her blog FYFD, on Aug 29, 2018 10:00 am, with this description:

Aerosols, micron-sized particles suspended in the atmosphere, impact our weather and air quality. This visualization shows several varieties of aerosol as measured August 23rd, 2018 by satellite. The blue streaks are sea salt suspended in the air; the brightest highlights show three tropical cyclones in the Pacific. Purple marks dust. Strong winds across the Sahara Desert send large plumes of dust wafting eastward. Finally, the red areas show black carbon emissions. Raging wildfires across western North America are releasing large amounts of carbon, but vehicle and factory emissions are also significant sources. (Image credit: NASA; via Katherine G.)

[Again, emphasis in bold is mine.]

As of today, Sharp’s post has collected some 281 notes, and almost all of them have “liked” it.

I liked it too—except for the last half of the last sentence, viz., the idea that vehicle and factory emissions are significant sources (cf. NASA’s characterization):


My comment:

NASA commits an error of omission. Dr. Sharp compounds it with an error of commission. Let’s see how.

NASA does find it important to mention that the man-made sources of carbon are “common.” However, the statement is ambiguous, perhaps deliberately so. It curiously omits to mention that the quantity of such “common” sources is so small that there is no choice but to regard it as “not critical.” We may not be in a position to call the “common” part an error of commission. But not explaining that the man-made sources play negligible (even vanishingly small) role in Global Warming, is sure an error of omission on NASA’s part.

Dr. Sharp compounds it with an error of commission. She calls man-made sources “significant.”

If I were to have an SE/TE student, I would assign a simple Python script to do a histogram and/or compute the densities of red pixels and have them juxtaposed with areas of high urban population/factory density.


This post may change in future:

BTW, I am only too well aware of the ugly political wars being waged by a lot of people in this area (of Global Warming). Since I do appreciate Dr. Sharp’s blog, I would be willing to delete all references to her writing from this post.

However, I am going to keep NASA’s description and the photo intact. It serves as a good example of how a good visualization can help in properly apprehending big data.

In case I delete references to Sharp’s blog, I will simply add another passage on my own, bringing out how man-made emissions are not the real cause for concern.

But in any case, I would refuse to be drawn into those ugly political wars surrounding the issue of Global Warming. I have neither the interest nor the bandwidth to get into it, and further, I find (though can’t off-hand quote) that several good modelers/scientists have come to offer very good, detailed, and comprehensive perspectives that justify my position (mentioned in the preceding paragraph). [Off-hand, I very vaguely remember an academic, a lady, perhaps from the state of Georgia in the US?]


The value of pictures:

One final point.

But, regardless of it all (related to Global Warming and its politics), this picture does serve to highlight a very important point: the undeniable strength of a good visualization.

Yes I do find that, in a proper context, a picture is worth a thousand words. The obvious validity of this conclusion is not affected by Aristotle’s erroneous epistemology, in particular, his wrong assertion that man thinks in terms of “images.” No, he does not.

So, sure, a picture is not an argument, as Peikoff argued in the late 90s (without using pictures, I believe). If Peikoff’s statement is taken in its context, you would agree with it, too.

But for a great variety of useful contexts, as the one above, I do think that a picture is worth a thousand words. Without such being the case, a post like this wouldn’t have been possible.


A Song I Like:
(Hindi) “dil sajan jalataa hai…”
Singer: Asha Bhosale
Music: R. D. Burman [actually, Bertha Egnos [^]]
Lyrics: Anand Bakshi


Copying it right:

“itwofs” very helpfully informs us [^] that this song was:

Inspired in the true sense, by the track, ‘Korbosha (Down by the river) from the South African stage musical, Ipi Ntombi (1974).”

However, unfortunately, he does not give the name of the original composer. It is: Bertha Egnos (apparently, a white woman from South Africa [^]).

“itwofs” further opines that:

Its the mere few initial bars that seem to have sparked Pancham create the totally awesome track [snip]. The actual tunes are completely different and as original as Pancham can get.

I disagree.

Listen to Korbosha and to this song, once again. You will sure find that it is far more than “mere few initial bars.” On the contrary, except for a minor twist here or there (and that too only in some parts of the “antaraa”/stanza), Burman’s song is almost completely lifted from Egnos’s, as far as the tune goes. And the tune is one of the most basic—and crucial—elements of a song, perhaps the most crucial one.

However, what Burman does here is to “customize” this song to “suit the Indian road conditions tastes.” This task also can be demanding; doing it right takes a very skillful and sensitive composer, and R. D. certainly shows his talents in this regard, too, here. Further, Asha not only makes it “totally, like, totally” Indian, she also adds a personal chutzpah. The combination of Egnos, RD and Asha is awesome.

If the Indian reader’s “pride” got hurt: For a reverse situation of “phoreenn” people customizing our songs, go see how well Paul Mauriat does it.

One final word: The video here is not recommended. It looks (and is!) too gaudy. So, even if you download a YouTube video, I recommend that you search for good Open Source tools and use it to extract just the audio track from this video. … If you are not well conversant with the music software, then Audacity would confuse you. However, as far as just converting MP4 to MP3 is concerned, VLC works just as great; use the menu: Media \ Convert/Save. This menu command works independently of the song playing in the “main” VLC window.


Bye for now… Some editing could be done later on.