Tag Archives: Newton

The Use of Fiction and Falsehood in Science

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Astrophysicist Neil deGrasse Tyson has some interesting and provocative things to say about religion in a recent interview. I tend to agree with Tyson that religions have a number of odd or even absurd beliefs that are contrary to science and reason. One statement by Tyson, however, struck me as inaccurate. According to Tyson, “[T]here are religions and belief systems, and objective truths. And if we’re going to govern a country, we need to base that governance on objective truths — not your personal belief system.” (The Daily Beast)

I have a great deal of respect for Tyson as a scientist, and Tyson clearly knows more about physics than I do. But I think his understanding of what scientific knowledge provides is naïve and unsupported by history and present day practice. The fact of the matter is that scientists also have belief systems, “mental models” of how the world works. These mental models are often excellent at making predictions, and may also be good for explanation. But the mental models of science may not be “objectively true” in representing reality.

The best mental models in science satisfy several criteria: they reliably predict natural phenomena; they cover a wide range of such phenomena (i.e., they cover much more than a handful of special cases); and they are relatively simple. Now it is not easy to create a mental model that satisfies these criteria, especially because there are tradeoffs between the different criteria. As a result, even the best scientists struggle for many years to create adequate models. But as descriptions of reality, the models, or components of the models, may be fictional or even false. Moreover, although we think that the models we have today are true, every good scientist knows that in the future our current models may be completely overturned by new models based on entirely new conceptions. Yet in many cases, scientists often respect or retain the older models because they are useful, even if the models’ match to reality is false!

Consider the differences between Isaac Newton’s conception of gravity and Albert Einstein’s conception of gravity. According to Newton, gravity is a force that attracts objects to each other. If you throw a ball on earth, the path of the ball eventually curves downward because of the gravitational attraction of the earth. In Newton’s view, planets orbit the sun because the force of gravity pulls planetary bodies away from the straight line paths that they would normally follow as a result of inertia: hence, planets move in circular orbits. But according to Einstein, gravity is not a force — gravity seems like it’s a force, but it’s actually a “fictitious force.” In Einstein’s view, objects seem to attract each other because mass warps or curves spacetime, and objects tend to follow the paths made by curved spacetime. Newton and Einstein agree that inertia causes objects in motion to continue in straight lines unless they are acted on by a force; but in Einstein’s view, planets orbit the sun because they are actually already travelling straight paths, only in curved spacetime! (Yes this makes sense — if you travel in a jet, your straightest possible path between two cities is actually curved, because the earth is round.)

Scientists agree that Einstein’s view of gravity is correct (for now). But they also continue to use Newtonian models all the time. Why? Because Newtonian models are much simpler than Einstein’s and scientists don’t want to work harder than they have to! Using Newtonian conceptions of gravity as a real force, scientists can still track the paths of objects and send satellites into orbit; Newton’s equations work perfectly fine as predictive models in most cases. It is only in extraordinary cases of very high gravity or very high speeds that scientists must abandon Newtonian models and use Einstein’s to get more accurate predictions. Otherwise scientists much prefer to assume gravity is a real force and use Newtonian models. Other fictitious forces that scientists calculate using Newton’s models are the Coriolis force and centrifugal force.

Even in cases where you might expect scientists to use Einstein’s conception of curved spacetime, there is not a consistent practice. Sometimes scientists assume that spacetime is curved, sometimes they assume spacetime is flat. According to theoretical physicist Kip Thorne, “It is extremely useful, in relativity research, to have both paradigms at one’s fingertips. Some problems are solved most easily and quickly using the curved spacetime paradigm; others, using flat spacetime. Black hole problems . . . are most amenable to curved spacetime techniques; gravitational-wave problems . . . are most amenable to flat spacetime techniques.” (Black Holes and Time Warps). Whatever method provides the best results is what matters, not so much whether spacetime is really curved or not.

The question of the reality of mental models in science is particularly acute with regard to mathematical models. For many years, mathematicians have been debating whether or not the objects of mathematics are real, and they have yet to arrive at a consensus. So, if an equation accurately predicts how natural phenomena behave, is it because the equation exists “out there” someplace? Or is it because the equation is just a really good mental model? Einstein himself argued that “As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.” By this, Einstein meant that it was possible to create perfectly certain mathematical models in the human mind; but that the matching of these models’ predictions to natural phenomenon required repeated observation and testing, and one could never be completely sure that one’s model was the final answer and therefore that it really objectively existed.

And even if mathematical models work perfectly in predicting the behavior of natural phenomena, there remains the question of whether the different components of the model really match to something in reality. As noted above, Newton’s model of gravity does a pretty good job of predicting motion — but the part of the model that describes gravity as a force is simply wrong. In mathematics, the set of numbers known as “imaginary numbers” are used by engineers for calculating electric current; they are used by 3D modelers; and they are used by physicists in quantum mechanics, among other applications. But that doesn’t necessarily mean that imaginary numbers exist or correspond to some real quantity — they are just useful components of an equation.

A great many scientists are quite upfront about the fact that their models may not be an accurate reflection of reality. In their view, the purpose of science is to predict the behavior of natural phenomena, and as long as science gets better and better at this, it is less important if models are proved to be a mismatch to reality. Brian Koberlein, an astrophysicist at the Rochester Institute of Technology, writes that scientific theories should be judged by the quality and quantity of their predictions, and that theories capable of making predictions can’t be proved wrong, only replaced by theories that are better at predicting. For example, he notes that the caloric theory of heat, which posited the existence of an invisible fluid within materials, was quite successful in predicting the behavior of heat in objects, and still is at present. Today, we don’t believe such a fluid exists, but we didn’t discard the theory until we came up with a new theory that could predict better. The caloric theory of heat wasn’t “proven wrong,” just replaced with something better. Koberlein also points to Newton’s conception of gravity, which is still used today because it is simpler than Einstein’s and “good enough” at predicting in most cases. Koberlein concludes that for these reasons, Einstein will “never” be wrong — we just may find a theory better at predicting.

Stephen Hawking has discussed the problem of truly knowing reality, and notes that it perfectly possible to have different theories with entirely different conceptual frameworks that work equally well at predicting the same phenomena. In a fanciful example, Hawking notes that goldfish living in a curved bowl will see straight-line movement outside the bowl as being curved, but despite this it would still be possible for goldfish to develop good predictive theories. He notes that likewise, human beings may also have a distorted picture of reality, but we are still capable of building good predictive models. Hawking calls his philosophy “model-dependent realism”:

According to model-dependent realism, it is pointless to ask whether a model is real, only whether it agrees with observation. If there are two models that both agree with observation, like the goldfish’s model and ours, then one cannot say that one is more real than the other. One can use whichever model is more convenient in the situation under consideration. (The Grand Design, p. 46)

So if science consists of belief systems/mental models, which may contain fictions or falsehoods, how exactly does science differ from religion?

Well for one thing, science far excels religion in providing good predictive models. If you want to know how the universe began, how life evolved on earth, how to launch a satellite into orbit, or how to build a computer, religious texts offer virtually nothing that can help you with these tasks. Neil deGrasse Tyson is absolutely correct about the failure of religion in this respect.  Traditional stories of the earth’s creations, as found in the Bible’s book of Genesis, were useful first attempts to understand our origins, but they have been long-eclipsed by contemporary scientific models, and there is no use denying this.

What religion does offer, and science does not, is a transcendent picture of how we ought to live our lives and an interpretation of life’s meaning according to this transcendent picture. The behavior of natural phenomena can be predicted to some extent by science, but human beings are free-willed. We can decide to love others or love ourselves above others. We can seek peace, or murder in the pursuit of power and profit. Whatever we decide to do, science can assist us in our actions, but it can’t provide guidance on what we ought to do. Religion provides that vision, and if these visions are imaginative, so are many aspects of scientific models. Einstein himself, while insisting that science was the pursuit of objective knowledge, also saw a role for religion in providing a transcendent vision:

[T]he scientific method can teach us nothing else beyond how facts are related to, and conditioned by, each other.The aspiration toward such objective knowledge belongs to the highest of which man is capabIe, and you will certainly not suspect me of wishing to belittle the achievements and the heroic efforts of man in this sphere. Yet it is equally clear that knowledge of what is does not open the door directly to what should be. . . . Objective knowledge provides us with powerful instruments for the achievements of certain ends, but the ultimate goal itself and the longing to reach it must come from another source. . . .

To make clear these fundamental ends and valuations, and to set them fast in the emotional life of the individual, seems to me precisely the most important function which religion has to perform in the social life of man.

Now fundamentalists and atheists might both agree that rejecting the truth of sacred scripture with regard to the big bang and evolution tends to undermine the transcendent visions of religion. But the fact of the matter is that scientists never reject a mental model simply because parts of the model may be fictional or false; if the model provides useful guidance, it is still a valid part of human knowledge.

Scientific Revolutions and Relativism

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Recently, Facebook CEO Mark Zuckerberg chose Thomas Kuhn’s classic The Structure of Scientific Revolutions for his book discussion group. And although I don’t usually try to update this blog with the most recent controversy of the day, this time I can’t resist jumping on the Internet bandwagon and delving into this difficult, challenging book.

To briefly summarize, Kuhn disputes the traditional notion of science as one of cumulative growth, in which Galileo and Kepler build upon Copernicus, Newton builds upon Galileo and Kepler, and Einstein builds upon Newton. This picture of cumulative growth may be accurate for periods of “normal science,” Kuhn writes, when the community of scientists are working from the same general picture of the universe. But there are periods when the common picture of the universe (which Kuhn refers to as a “paradigm”) undergoes a revolutionary change. A radically new picture of the universe emerges in the community of scientists, old words and concepts obtain new meanings, and scientific consensus is challenged by conflict between traditionalists and adherents of the new paradigm. If the new paradigm is generally successful in solving new puzzles AND solving older puzzles that the previous paradigm solved, the community of scientists gradually moves to accept the new paradigm — though this often requires that stubborn traditionalists eventually die off.

According to Kuhn, science as a whole progressed cumulatively in the sense that science became better and better at solving puzzles and predicting things, such as the motions of the planets and stars. But the notion that scientific progress was bringing us closer and closer to the Truth, was in Kuhn’s view highly problematic. He felt there was no theory-independent way of saying what was really “out there” — conceptions of reality were inextricably linked to the human mind and its methods of perceiving, selecting, and organizing information. Rather than seeing science as evolving closer and closer to an ultimate goal, Kuhn made an analogy to biological evolution, noting that life evolves into higher forms, but there is no evidence of a final goal toward which life is heading. According to Kuhn,

I do not doubt, for example, that Newton’s mechanics improves on Aristotle’s and that Einstein’s improves on Newton’s as instruments for puzzle-solving. But I can see in their succession no coherent direction of ontological development. On the contrary, in some important respects, though by no means all, Einstein’s general theory of relativity is closer to Aristotle’s than either of them is to Newton’s. (Structure of Scientific Revolutions, postscript, pp. 206-7.)

This claim has bothered many. In the view of Kuhn’s critics, if a theory solves more puzzles, predicts more phenomena to a greater degree of accuracy, the theory must be a more accurate picture of reality, bringing us closer and closer to the Truth. This is a “common sense” conclusion that would seem to be irrefutable. One writer in Scientific American comments on Kuhn’s appeal to “relativists,” and argues:

Kuhn’s insight forced him to take the untenable position that because all scientific theories fall short of absolute, mystical truth, they are all equally untrue. Because we cannot discover The Answer, we cannot find any answers. His mysticism led him to a position as absurd as that of the literary sophists who argue that all texts — from The Tempest to an ad for a new brand of vodka — are equally meaningless, or meaningful. (“What Thomas Kuhn Really Thought About Scientific ‘Truth’“)

Many others have also charged Kuhn with relativism, so it is important to take some time to examine this charge.

What people seem to have a hard time grasping is what scientific theories actually accomplish. Scientific theories or models can in fact be very good at solving puzzles or predicting outcomes without being an accurate reflection of reality — in fact, in many cases theories have to be unrealistic in order to be useful! Why? A theory must accomplish several goals, but some of these goals are incompatible, requiring a tradeoff of values. For example, the best theories generalize as much as possible, but since there are exceptions to almost every generalization, there is a tradeoff between generalizability and accuracy. As Nancy Cartwright and Ronald Giere have pointed out, the “laws of physics” have many exceptions when matched to actual phenomena; but we cherish the laws of physics because of their wide scope: they subsume millions of observations under a small number of general principles, even though specific cases usually don’t exactly match the predictions of any one law.

There is also a tradeoff between accuracy and simplicity. Complete accuracy in many cases may require dozens of complex calculations; but most of the time, complete accuracy is not required, so scientists go with the simplest possible principles and calculations. For example, when dealing with gravity, Newton’s theory is much simpler than Einstein’s, so scientists use Newton’s equations until circumstances require them to use Einstein’s equations. (For more on theoretical flexibility, see this post.)

Finally, there is a tradeoff between explanation and prediction. Many people assume that explanation and prediction are two sides of the same coin, but in fact it is not only possible to predict outcomes without having a good causal model, sometimes focusing on causation gets in the way of developing a good predictive model. Why? Sometimes it’s difficult to observe or measure causal variables, so you build your model using variables that are observable and measurable even if those variables are merely associated with certain outcomes and may not cause those outcomes. To choose a very simple example, a model that posits that a rooster crowing leads to the rising of the sun can be a very good predictive model while saying nothing about causation. And there are actually many examples of this in contemporary scientific practice. Scientists working for the Netflix corporation on improving the prediction of customers’ movie preferences have built a highly valuable predictive model using associations between certain data points, even though they don’t have a true causal model. (See Galit Shmueli, “To Explain or Predict” in Statistical Science, 2010, vol. 25, no. 3)

Not only is there no single, correct way to make these value tradeoffs, it is often the case that one can end up with multiple, incompatible theories that deal with the same phenomena, and there is no obvious choice as to which theory is best. As Kuhn has pointed out, new theories become widely accepted among the community of scientists only when the new theory can account for anomalies in the old theory AND yet also conserve at least most of the predictions of the old theory. Even so, it is not long before even newer theories come along that also seem to account for the same phenomena equally well. Is it relativism to recognize this fact? Not really. Does the reality of multiple, incompatible theories mean that every person’s opinion is equally valid? No. There are still firm standards in science. But there can be more than one answer to a problem. The square root of 1,000,000 can be 1000 or -1000. That doesn’t mean that any answer to the square root of 1,000,000 is valid!

Physicist Stephen Hawking and philosopher Ronald Giere have made the analogy between scientific theories and maps. A map is an attempt to reduce a very large, approximately spherical, three dimensional object — the earth — to a flat surface. There is no single correct way to make a map, and all maps involve some level of inaccuracy and distortion. If you want accurate distances, the areas of the land masses will be inaccurate, and vice versa. With a small scale, you can depict large areas but lose detail. If you want to depict great detail, you will have to make a map with a larger scale. If you want to depict all geographic features, your map may become so cluttered with detail it is not useful, so you have to choose which details are important — roads, rivers, trees, buildings, elevation, agricultural areas, etc. North can be “up” on your map, but it does not have to be. In fact, it’s possible to make an infinite number of valid maps, as long as they are useful for some purpose. That does not mean that anyone can make a good map, that there are no standards. Making good maps requires knowledge and great skill.

As I noted above, physicists tend to prefer Newton’s theory of gravity rather than Einstein’s to predict the motion of celestial objects because it is simpler. There’s nothing wrong with this, but it is worth pointing out that Einstein’s picture of gravity is completely different from Newton’s. In Newton’s view, space and time are separate, absolute entities, space is flat, and gravity is a force that pulls objects away from the straight lines that the law of inertia would normally make them follow. In Einstein’s view, space and time are combined into one entity, spacetime, space and time are relative, not absolute, spacetime is curved in the presence of mass, and when objects orbit a planet it is not because the force of gravity is overcoming inertia (gravity is in fact a “fictitious force“), but because objects are obeying the law of inertia by following the curved paths of spacetime! In terms of prediction, Einstein’s view of gravity offers an incremental improvement to Newton’s, but Einstein’s picture of gravity is so radically different, Kuhn was right in seeing Einstein’s theory as a revolution. But scientists continue to use Newton’s theory, because it mostly retains the value of prediction while excelling in the value of simplicity.

Stephen Hawking explains why science is not likely to progress to a single, “correct” picture of the universe:

[O]our brains interpret the input from our sensory organs by making a model of the world. When such a model is successful at explaining events, we tend to attribute to it, and the elements and concepts that constitute it, the quality of reality or absolute truth. But there may be different ways in which one could model the same physical situation, with each employing different fundamental elements and concepts. If two such physical theories or models accurately predict the same events, one cannot be said to be more real than the other; rather we are free to use whichever model is more convenient.  (The Grand Design, p. 7)

I don’t think this is “relativism,” but if people insist that it is relativism, it’s not Kuhn who is the guilty party. Kuhn is simply exposing what scientists do.