Chapter 3: Philosophy of Science After Logical...

Chapter 3: Philosophy of Science After Logical Empiricism

Logical Empiricism started out in Austria and Germany in the 20th Century between the World Wars. After the Second World War it dominated much of the thinking about science in the United Kingdom and United States.

Problems with it accumulated, however. The Verification Criterion, the principle for distinguishing scientific statements from nonscientific ones based upon their ability to be empirically verified, was under assault by Popper and others. Reductionism, the principle that all a posteriori knowledge should grounded in experience, was criticized by Quine. Also, Quine, Maxwell and Goodman all identified problems with the Logical Empiricists goal of using language to define theoretical terms based on sense terms in a culturefree, biasfree fashion.

In retrospect it may have been a bit arrogant for philosophers of science (who were not necessarily scientists) to think they could define what science was and was not for the benefit of scientists. Scientists like Richard Feynman often ignored, or even mocked, their work.

The Logical Empiricists sought to define an a priori program for scientific development and justification in belief in it. They failed, however, to anticipate (among other things) how well intertwined cultural and other “metaphysical” assumptions were in the very essence of their approach.

By the early 1960s the stage had been set for people to try to put culture in science by explicitly appealing to how scientists have or do actually behave in a given cultural context. Historians of science considered how scientists have behaved. Sociologists considered how scientists currently do behave.

3.1 How to Conceptualize an Ancient Science

Say you are a reasonably scientificallysavvy graduate student. Your university has given you the task of teaching a course on physics in the GrecoRoman world of 1 B.C.E. How would you fulfill your assignment?

You might do the following. First, get into the ancient mindset. To the ancients the Universe looked like the following:

Copyright (c) 2008 by Joseph Phillips. All Rights Reserved.

The Earth is firmly at the center. Above the Earth is a realm of air, then a realm of fire, and then the heavenly bodies. The heavenly bodies orbit the Earth in perfect circles. The closest is the Moon. Mercury, Venus, the Sun, Mars, Jupiter and Saturn all follow as one ascends further from the Earth. Beyond the circular orbit of Saturn are the stars embedded on a large, outermost crystal sphere.

The second thing to do would be to study the worldview in which their science existed. To the ancients there were two basic realms: the Terrestrial (below the orbit of the Moon) and the Celestial (the Moon's orbit and above).

Motion in the Terrestrial realm is idealized as linear. For example, if one shot an arrow into the air its ideal motion would be to go up at an angle for some distance and then to fall mostly vertically down to Earth.

Besides being linear Terrestrial motion also is temporary. All Terrestrial motions eventually grind to a halt.

Motion in the Celestial Realm, however, is uniform, circular and everlasting. This motion is “perfect” in the sense that it goes on forever in perfect circles without slowing or stopping.

One big problem of this view of perfect Celestial motion is that it does not easily agree with observations. When viewed against the background Zodiac some heavenly bodies are mostly circular (the Moon, the Sun). Others, however, tend to doubleback (or retrograde) on themselves.


Plot of the 2003 Retrograde of Mars

The solution that Ptolemy (a Greek astronomer in Egypt) came up with is the following. The Earth is no longer at the direct center of the Universe, but is offset slightly from the X in the diagram. Also, there still is a main orbital circle for the retrograding planets like Mars. Mars, however, is not directly on this main circle. Instead the center of a smaller, second circle orbits on the larger circle. Mars lies on this smaller circle.

There was also a theory of matter that corresponds with this Celestial/Terrestrial Universe. This theory had five elements. Listed from most refined to most base they are ether, fire, air, water, and earth. Ether was the stuff of the heavens. It is light, perfect, circularmoving and nonexistent on Earth. All other elements tend to eventually stop while ether keeps moving. Fire was the lightest of all terrestrial materials. Its tendency is to rise just short of the heavens. Air was a little more dense than fire. It tends to rise above water and earth. Water


being more dense only rises above earth. Earth is the lowest, basest stuff in existence.This theory of matter explains many natural motions. Flames rise because fire is lighter

than even air. Smoke, a combination of fire and air, also rises. Water tends to flow down. Rain is water flowing down through air. Rivers and streams are water flowing down to the ocean. However, even below streams, rivers and oceans are riverbeds and seabeds composed of the densest, basest stuff: earth. Except for ever circularmoving ether all other elements tend to move straightup or down to their natural level, and tend to stop when they reach it.

3.2 Kuhn's Insight

Thomas Kuhn's insight was to realize and accept that to the ancients this worldview made sense. It is largely selfconsistent. It is also explanatory. It explains why smoke rises and why rain and stones fall. It is even predictive. We can consider what would happen if we were underwater (in the ocean, or a deep lake) and we threw a rock. The theory would predict (correctly) that the stone would travel through the water for a distance but eventually hit the bottom.

Aspects of this theory may seem incomplete and simplistic to someone of the early 21st

Century. Geocentrism seems quaint. Five elements seems far too few to explain all of the materials that exist. The circular everlasting motion of ether and the linear motion to some stationary equilibrium for everything else seems like they failed to distinguish among ideas that we consider distinct: density, gravity, momentum and friction.

We should, however, be careful not to take individual principles of this belief system out of their larger context. To the ancients they worked together to provide a unified system that predicted the motions of nature on Earth and in Heaven.

Kuhn called such unified world views paradigms. Paradigms are selfcongratulatory things in that they tell researchers which data are important to predict (for example, natural motions) . . . and then they smile broadly after they have predicted it. (Nevermind other details that they do not predict.) They also tell the allowable forms that their rules, principles, laws, and equations must have. For example, the physics of the ancients used linear and uniform circular motion. By specifying which data are relevant paradigms also define which experiments make sense to try, or more generally the set of interesting phenomena to observe. Going underwater to throw a stone makes sense, and its outcome would support the theory.

Paradigms are explicitly taught in schools and universities. They have exemplars: solved cases that serve both to showcase the range of problems that the paradigm can solve and to act as templates for solving similar problems in the future. The decomposition of the orbit of Mars into a greater and lesser circle is one such exemplar. The framework of that solution could be applied to the specifics of Jupiter, Saturn and Venus. They facilitate the creation of a community of students and researchers over time and space that adhere to the same principles, think in similar ways, and share the same intellectual culture.


When such a coherent intellectual culture exists its practitioners can share ideas and build upon each others work. In fact, most scientists most of the time do just this. They identify issues that the given dominant paradigm solves poorly and use the tools given by the paradigm to work out solutions. This is a puzzlesolving activity, and would be exemplified by working out the circles for Jupiter and Saturn given that the exemplar for Mars had already been computed.

It is important to realize that there always is a body of problems that the paradigm has yet to solve. Some predictions cannot be computed. Others predictions disagree with observations. Some aspects of the theory appear to be inconsistent. Attempting to solve these discrepancies keeps the paradigm's practitioners employed.

Further, the paradigm's practitioners are restricted by their paradigm. They cannot just use any theory that happens to fit the data. The theory that they use must also organically fit in with the other aspects of the paradigm. For example, more accurate measurements of Mars may show that even using two circles cannot predicts its location well. The natural thing to do is to introduce a third circle whose center orbits on the second circle, and around whose circumference Mars moves.

Problems always accumulate but they generally fall to the problemsolving activities of the paradigm's practitioners. Crises result, however, when a body of persistent and fundamental problems accumulate that resist being solved. For example, the positions of the planets became known more accurately and more precisely. Describing Mars' orbit with two circles was insufficient. Initially the crises was appeased by introducing more circles, and then evern more. These solutions, however, rapidly lost their elegance as more circles were added to explain more planets.

The solution to “circlecreep” is of course to dispose of the system all together in favor of a Copernician heliocentric/Keplerian elliptical one. Adopting this approach, however, takes a break in thinking; a radical revolution in science. The two systems (geocentric and heliocentric) are fundamentally incompatible, or incommensurate in Kuhnian terms. This incompatibility extends beyond the simple disagreement about whether it is the Earth or Sun at the center of the Universe. The two systems potentially disagree over which data are worth matching, what the acceptability criteria for statements ought to be, and what the important problems are.

In the geocentric vs. heliocentric case the two systems probably agree that planetary position data ought to be matched, but definitely disagree over statement acceptability. Geocentrism is part of a larger paradigm that holds uniform circular motion as perfect. The goal is not just to fit the data, but to support and be supported by the other tenets of ancient physics (the five elements and rules for their motion).

Heliocentrism with elliptical orbits has different rules for acceptability. One is that the Sun as the light source of the planets ought to be the center of the Universe. Another is that the elegance of a single ellipse in describing an orbit outweighs the elegance of uniform circular motion applied to multiple circles.

They are also incommensurate in terms of mechanism too. The geocentrists' mechanism was the inherent properties and tendencies of matter held by the ancients. Earth, water, air,


and fire each had their separate equilibrium positions while ether moved ceaselessly above all. The geocentrists could point to the heliocentrists as not having a mechanism until Newton's positing of gravity in Philosophiae Naturalis Principia Mathematica in 1687, 82 years after Johannes Kepler found his 3 Laws of Planetary Motion. And even then Newton's conception of gravity was thought of by some as “spooky” for its instantaneous “action at a distance”. Resolving that took another paradigm shift, Albert Einstein's General Relativity of 1915 and 1916.

These differences in acceptability also highlight differences in which problems are more important. For the geocentrists compatibility with the motionbased theory of matter held by the ancients and with a definite mechanism is more important. For the heliocentrists fitting planetary observations is more important.

Kuhn thought of scientific revolutions as potentially bitter affairs in large part because of this incommensurateness. The fundamental disagreement over the data to match; the standards for statement acceptance; the presence, absence and details of a mechanism; and the relative importance of different problems created an environment for the two sides to talk past each other. If scientists just joining the field perceived that more progress was being made with the newer paradigm then they might join it. If some practitioners of the old paradigm see more progress then they might switch too. However, some scientists schooled in the old paradigm might hold its tenets too dear to part with, and may never reject it. The old paradigm might not die until the last of its practitioners does.

For Kuhn there is no way to objectively say that one paradigm is better. The two systems disagree about the relative importance of data, statement evaluation, mechanism importance and details, and problems to solve. Both paradigms can probably point to things that they do better than the other. Also, initially the newer paradigm may not solve as many problems as (and may have poorly described mechanisms relative to) the older because it is still being refined.

Kuhn's next step follows from this lack of the ability to make objective choices, but goes further than many philosophers of science wanted to go. Because we cannot say objectively say which paradigm is better, we also cannot say that science is really making progress. One paradigm is just different from another; we cannot rationally convince ourselves that it is better or worse.

Kuhn gives up any notion of “rational progress” within science but gains agreement with history. In his book The Structure of Scientific Revolutions he gives us several examples of scientists paradigms, exemplars, puzzlesolving activities, crises and revolutions. He also gives examples of how paradigms are incommensurate, and of paradigms only dying after the last of its practitioners do.

3.3 Kuhn, With Rational Progress

Many of philosophers of science after Kuhn wanted the best of both worlds. From Logical Empiricism they wanted the notion of “rational progress” in science, they wanted to argue that


science may proceed nonmonotonically but, over the long term, its models are more predictive. From Kuhn they wanted agreement with history, and perhaps some notion of “paradigm” and “revolution”.

Imre Lakatos was one such philosopher. His background was similar to that of many of the German Logical Empiricists, with a slight modification. He was born Imre Lipschitz in Hungary in 1922. He was a Jew in Central Europe during the Second World War. He switched his family name to Molnár to avoid the Nazis. He graduated college in 1944, became a communist and survived the war. (His mother and grandmother were less fortunate and were killed at Auschwitz.)

After the war he changed his name again to Lakatos (Hungarian for “locksmith”) to honor Geza Lakatos, a Hungarian general who lead a brief revolt against the proNazi Hungarian government during the war. He fellout with the Communists in the early 1950s and fled Hungary when the Soviets invaded in 1956. He first went to Austria but settled in London.

Lakatos modeled his research programmes after Kuhn's paradigms, but with a difference. A research programme could be identified with the statements to which its practitioners adhered. These statements could be separated into two bodies. The hard core of the research programme was the subset of the statements that to which its practitioners proclaim unshakeable belief. For example, for modern microbiology one such statement is “DNA encodes heredity and is transcribed into RNA. RNA is then translated into protein.” Lakatos then borrows from the Logical Empiricists, particularly Popper, when he defines the negative heuristic of science is to protect the hard core from falsification at all costs.

The chief way to protect a research programme's hard core is with its second set of statements, the auxiliary hypotheses. These statements are more provisionally included in the research programme. If it is no longer convenient to hold them then they may be modified or discarded. The gist of the research programme remains unaffected. Revisiting the microbiology example, such a auxiliary hypothesis might be that “Gene ABC regulates pathway XYZ.” Later we might find out that gene ABC is only part of the story, or that both it and XYZ are actually regulated by something else. Learning this would not decrease our commitment to our belief that “DNA encodes heredity and is transcribed into RNA. RNA is then translated into protein.”

With research programmes so defined Lakatos turned to consider how we can say that science is making progress over the long term. A progressive research programme is one that is growing its body of statements to make new predictions and include new techniques. A research programme does particularly well if it was makes novel predictions. This echos Popper's idea of wanting scientists to make as strong and as novel claims as possible so that their models are (in principle) falsifiable.

Progressive research programmes are contrasted with degenerate ones. Degenerate ones do not grow. Rather, they propose ad hoc changes to the hard core instead of adding statements to the protective belt of the auxiliary hypotheses.

Larry Laudan proposed a similar approach, but which emphasized solved problems over Logical Empiricistlike statements. Laudan is an American with a less dramatic past than


many of the Central Europeans who ran from Nazism and/or Communism. His research traditions kept track of which problems were solved, and tried to maximize their quantity.

For Laudan research traditions faced two kinds of problems. The first were empirical problems related to missing or incorrect predictions. These were akin to Kuhn's anomalies. The second were conceptual problems related to selfconsistency, and consistency of a given research tradition with those in related disciplines. (For example, the related disciplines of microbiology might be biochemistry and the rest of biology.)

Like Lakatos, Laudan wanted a rational basis for accepting or rejecting a given research tradition, but his approach was more nuanced. One should accept the research tradition that has solved most problems on the basis that it had the widest empirical support, and therefore we have the confidence in its applicability. One should, however, pursue the research tradition that has is currently solving the most problems. The rationale for this is that this area offers scientists the most opportunities to publish and gain a reputation for him or herself.

In general the research traditions to accept and pursue are different. For example, in physics the field of statics predicts the stability of stationary structures in timeinvariant gravitational fields. It is a thoroughly studied, thoroughly believed field. One of its uses is in predicting whether a given bridge design will stand. Such decisions impact the expenditure of millions of dollars and affect the safety of thousands of lives daily.

Important as statics is physicists probably view string theory as a “sexier” area of physics. As of the earlier 21st Century there is no agreement about whether string theory is the “correct” way to view the Universe, but that is not the point. String theorests pursue a new and exciting area of physics, and they (hopefully) have fun doing it.

Laudan's notion of progress is more nuanced than Lakatos' in more ways than just distinguishing between the research tradition to believe vs. the one to pursue. He gives up Lakatos' desire for cumulative progress. Some problems that were solved now may become “unsolved” if the research traditions changes dramatically enough. Also, unlike both Popper and Lakatos there is no special reward for novel predictions. Predictions are more equal.

3.4 Sociology of Science

For completeness we should cover a second, parallel way of describing science. A new breed of sociologists started covering scientists in the 1960s, shortly after Kuhn's seminal work. While historians of science like Kuhn emphasized agreement with historical developments in science, sociologists studied how contemporary scientists behave.

Some of their work highlights familiar organizations in interesting and novel ways. ___, for example, studied a high energy physics lab in Toronto for __ years. He concluded that it was run as a “benign dictatorship”.

It was a “dictatorship” in the sense that it run by the more senior (read: “tenured”) scientists. They exerted their control in committees and other governing bodies of the department, and it went unchallenged.

The dictatorship was “benign” in the sense that the younger (read: “untenured”) scientists knew their older colleagues ran the show, and were happy about it. The senior scientists were not trying to aggrandize money or power, but merely trying to keep the department


running smoothly for everyone. This let the younger faculty spend more time in lab, and hopefully publish more.

Sociologists have also considered how scientists interact with the knowledge they produce. The philosopher Ronald Giere has labeled two proposals of sociologists on the more extreme end of the spectrum. On the topic of “do scientists believe that the things they discuss (quarks, genes, etc.) actually exist” the epistemological constructist answer is “Maybe yes, maybe no, but it does not really matter.” The important thing is that scientists are acting in their own selfinterest and doing what it took to, for example, get published.

An even more extreme idea was ontological constructivism, which held that quarks, genes and such were just socially constructed and interpreted things, like poems a literary tradition or laws in a judicial system. One could ascribe no importance or relevance to these ideas outside the sociallyconstructed realm that scientists created in which they were believed. Scientific knowledge, then, might just be a specialcase of convenient social fictions like Santa Claus or Bugs Bunny.

Perhaps this sociological view is too extreme for some. After all, sociologists like to think of themselves as scientists too. Are their models also just convenient fictions? Excuses to get published? Some sociologists embraced this reduction to nihilism, others did not.

3.5 ModelBased Philosophy of Science

At the end of the 20th Century a new philosophy of science gained momentum, the ModelBased theory of Science. It was advocated by the American Ronald Giere, and others. It rejected the statementbased theory of science and proposed a “modelbased” one, where a model could be statements, maps or physical representations.

How ever they are instantiated, models are of limited applicability to describing the Universe. All models describe just some subset of the Universe, and only for a few attributes. For example, consider Hooke's Law, nowadays commonly written as F = -kx. Here F represents the force on some body due to something pulling or pushing on it, like a spring. The k is a constant that represents the spring's stiffness. The x is a variable that tells how far the spring is from its equilibrium position. By using this equation in conjunction with Newton's Laws of motion we can predict the behavior of lowfriction springmass systems for awhile.

But look, however, at what we did not do. We only attempted to predict the position, velocity and acceleration of the mass. Most of the mass' properties; its composition, its color, its taste, its smell, how much it costs, etc., are not predicted. Furthermore, we only apply Hooke's Law to a certain aspects of the Universe. Besides the obvious masses on springs, we might also apply this model to mechanisms inside windup clocks and watches, rulers snapped on tables, etc. However, most aspects of the Universe (those associated with gravity, electromagnetism, etc.) are not modeled.

3.6 The TakeHome Message

In this chapter and the last we discussed some of the history of developments in the


philosophy of science over the 20th century. We did this because there are philosophical assumptions and ramifications to various approached to computational scientific discovery. If you ignore this, philosophers of science will “do you the favor” and remind you. They might do this by rehashing problems with Logical Empiricism.

If they do this, do not take it personally. Philosophers make a living attempting to poke holes in the reasoning of other philosophers. If they did not do this they would not perceive “progress”. If they did not do this they would have fewer papers to write, and have less ways to distinguish themselves. It just what philosophers do.

We computer scientists could play this game too. We could say to the Logical Empiricists “Is language really devoid of metaphysics?” and “Is reduction possible?”. We could say to Kuhn “Is there really no notion of progress in science?” We could say to Lakatos “Is the “research programme” view itself a progressive view of science?” We could say to Laudan “What exactly is a research tradition and how do you know how many problems it has solved?” We could say to the sociologists “Isn't your work just sociallyconstructed too?”

We could say these things, but then we would sink to their level. Instead I hope my fellow computer scientists would rise above the din of philosophical conversations about knowledge and reality that have continued since before Aristotle. We should make concentrate on writing programs that please scientists. We should let our program speak for themselves and us.

“Is it science if it is not done by humans?” In the 1970s mathematicians asked themselves the related question “Is it mathematics if it is not done by humans?” when they some mathematicians proved The Four Color Map Theorem, an important problem in mathematics, with significant computer assistance. The Four Color Map Theorem states that given any plane separated into regions, such as a political map of the provinces of a country, the regions may be colored using no more than four colors in such a way that no two adjacent regions receive the same color. Some mathematicians may not accept the theorem because it is impractical to check the correctness of the compiler and hardware by hand. Most modern mathematicians, however, probably accept it as part of the body of mathematics. (Did not Kuhn have something to say about revolutions, how everybody does not necessarily switch, and how the older paradigm does not die until its practitioners do?)

After having fun at the expense of philosophers of science let us learn from their discussions. In all cases I recommend that we adopt the stance favored by our client scientists:

1. Realism vs. Instrumentalism. Are scientific models our best guess at the nature of reality or are they just computational tools? Giere pointed out that all models are “tools” in the sense that they only predict small aspects of reality. Cognizant of that point, we can see that computer programs range the spectrum. One can write, for example, a program that simulates the motions of the planets given Newtonian Laws of motion, gravity, and information about the position and velocities of the solar systems larger objects. (Again, we realize that not all comets and asteroids will be included.) This will be realist in the sense that there will be actual data structures pertaining to the Sun, planets, moons, etc. On the other hand, computer simulations often are purposefully


instrumentalist in that we know we may lack the memory, time, knowledge about the initial state, or information about the exact interactions that all objects being simulated, yet we want to do the simulation any way to get a gross feel for what they simulated system might do. Examples include simulating how a protein folds in a vacuum (as opposed to being surrounded by water, the environment that most proteins exists in) and simulations of large economies.

2. Language. How general should the language be in which our programs writes its statements? Should we be able to construct new terms? If so these terms will probably be definitional: “New term = some relationship among existing terms.” The artificial intelligence field of constructive induction deals with precisely this issue. It tries to create new attributes to hasten discovery during machine learning. Our programs might have added expressive power if given an expressive set of language primitives, but this might increase the search time for something interesting to say. Is it possible for a program to define its own primitives? If so, how? And how would the program rewrite old knowledge in this new format?

3. Reductionism. Sometimes scientists want to find a small set of principles that seem to underlie reality, and sometimes they do not. Often the task is constructive: build a world that looks like ours from these primitives. This, of course, may be embedded in a larger, reductionist goal of trying to find the best set of primitives. Our programs may or may not directly influence the choice of the primitive set. Sometimes there is no “reductionism” in the sense of physics. This might be the case evolutionary biology, in which evolution might be viewed as a template to superimpose upon the data rather than to reduce the data into.

4. Progress. Of course scientists want progress in the Kuhnian “Normal Science” meaning. But which do scientists what more: empirically justifying as many claims as possible (Logical Empiricists), making model that falsifiable, but not yet falsified (Popper), increasing the body of theories (Lakatos), increasing the number of solved problems (Laudan), or perhaps something else?

5. Revolutions. This, of course, is the toughest issue. It assumes we have solved the “define your own primitives” problem already. Should we worry about this? An argument for “no” is that (according to Kuhn) most of the scientists most of the time do “normal science”. Restricting our programs to the search space implicit in the current paradigm (by restricting the primitives of the language, by restricting the acceptability of statements, by restricting the data that we will try to predict, etc.) we will make our programs faster. However, the appeal writing a program that could affect the next revolution is tempting to both scientists and computer scientists. (Though scientists might want to steal all of the credit.) Computers probably are capable of thought experiments, though doing this will require some thought on our part.

References:Giere, Ronald N. “Science Without Laws” The University of Chicago Press, 1999.Kuhn, Thomas S. “The Structure of Scientific Revolutions, Second Ed., Enlarged” The

University of Chicago Press, 1970.


Thompson, Mel. “Teach Yourself Philosophy of Science”, Hodder Headline Plc., 2001.


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