Chapter 4 

Foundations for a Moral Code: Rationalism and Its Flaws

In Western philosophy, rationalism is the main alternative to empiricism for describing the human mind and for modeling what knowing is. It is the way of Plato in Classical Greek times and of Descartes in the Enlightenment. Rationalism suggests that the human mind can build a system for understanding itself and for how it knows its universe, only if that system is first of all grounded in the human mind by itself, before any sensory experiences or memories of them enter the thinking system.


 

 

Descartes, for example, points out that our senses give us information that can easily be faulty. As was noted above, the stick in the pond looks bent at the water line, but if we remove it, we see it is straight. The hand on the pocket warmer and the hand in the snow can both be immersed in tepid tap water; to one hand, the tap water is cold and to the other, it is warm. And these are the simple examples. Life contains many much more difficult ones. Therefore, the rationalists say, if we want to think about thinking in rigorously logical ways, we must try to construct a system for modelling human thinking by beginning from some concepts that are built into the mind itself before any unreliable sense data or memories of sense data even enter the picture.

Plato says we come into the world at birth already dimly knowing some perfect “forms” that we then use to organize our thoughts. He drew the conclusion that these useful forms, which enable us to make sense of our world, are imperfect copies of the perfect forms that exist in a perfect dimension of pure thought, before birth, beyond matter, space, and time—a dimension of pure ideas. The material world and the things in it are only poor copies of that other world of pure forms ultimately derived from the pure Good. The whole point of our existence, for Plato, is to discipline the mind by study until we learn to more clearly recall, understand, and live by the perfect forms—perfect tools, perfect cooking, perfect medicine, perfect beauty, perfect justice, perfect tools, perfect animals, and many others.

Descartes formulated a similar system of thought that begins from the truth the mind finds inside itself when it carefully and quietly contemplates just itself. During this quiet and totally concentrated self-contemplation, the thing that is most deeply you, namely your mind, realizes that whatever else you may be mistaken about, you can’t be mistaken about the fact that you exist; you must exist in some way in some dimension in order for you to be thinking about whether you exist. For Descartes, this was a starting point that enabled him to build a whole system of thinking and knowing that sets up two realms: a realm of things the mind deals with through the physical body attached to it, and another realm the mind deals with by pure thinking, a realm built on the “clear and distinct ideas” (Descartes’s words) that the mind knows before it ever takes in the impressions coming from the physical senses.

These two rationalists have had millions of followers—in Descartes’s case for four hundred years and in Plato’s case for well over two thousand. They have attacked empiricism for as long as it has been around (since the 1700s, or in a simpler form, some argue, since the time of Aristotle, who was Plato’s pupil, but who disagreed diametrically with Plato on several matters).

The debate between the rationalists and the empiricists has not let up, even in our time. But in our quest to find a universal moral code, we will find that we must discard rationalism just as we did empiricism; rationalism contains a flaw worse than any of empiricism’s flaws.

Chapter 3.                          (continued) 

As we seek to build a moral system we can all try to live by, we feel driven to look for a way of thinking about thinking and knowing that is deeper and based on stronger logic, a way of thinking about thinking that we can believe in profoundly. We need a new model of human thinking, one built around a core philosophy that is different from empiricism, not just in degree but in kind.

Empiricism’s disciples have achieved some impressive results in the practical sphere, but then again, for a while in their times, so did the followers of medieval Christianity, Communism, 

Nazism, and several other worldviews/theories. They even had their own “sciences,” dictating in detail what their scientists should study and what they should conclude from their studies.

Perhaps the most disturbing examples are the Nazis. They claimed to base their ideology on empiricism and science. In their propaganda films and in all academic and public discourse, they preached a warped form of Darwinian evolution that enjoined and exhorted all nations, German or non-German, to go to war, seize territory, and exterminate or enslave all competitors—if they could. They claimed this was the way of the world, and it must be so. Hitler’s team were gambling, confidently, that in that struggle, the Aryans, with the Germans in the front ranks, would win.  

  

                                                           Nazi leader Adolf Hitler.

“In eternal warfare, mankind has become great; in eternal peace, mankind would be ruined.” —Adolf Hitler, Mein Kampf.

Such a view of human existence, they claimed, was not cruel or cynical. It was simply built on a mature and realistic acceptance of the truths of science. If people calmly and clearly look at the evidence of history, they can see that war always comes. Mature, realistic adults learn and practice the arts of war, assiduously in times of peace and ruthlessly in times of war. According to the Nazis, this was merely a logical consequence of accepting the survival-of-the-fittest rule that governs life.

Hitler’s ideas about race and about how the model of Darwinian evolution could be applied to humans, were, from the viewpoint of the real science of genetics, largely unsupported. But in the Third Reich, this was never acknowledged.

                           

                                                                           Werner Heisenberg.

The disturbing thing about physicists like Werner Heisenberg, chemists like Otto Hahn, and biologists like Ernst Lehmann becoming willing tools of Nazism is not so much that they became Hitler’s puppets, but that their life philosophy as scientists did not equip them to break free of the Nazis’ distorted version of science. Their religions failed them, but clearly, in moral terms, science failed them too.

                         

                                                                             Otto Hahn.

There is certainly evidence in human history that the consequences of science being misunderstood can be horrible. Nazism became humanity’s nightmare. Some of its worst atrocities were committed in the name of advancing science.¹⁴ For practical, evidence-based reasons, then, as well as for theoretical reasons, millions of people around the world today have become deeply skeptical about all systems and, in moral matters at least, about scientific idea systems in particular. At primal levels we are driven to wonder: Should we trust something as critical as the survival of our culture, our knowledge, our children and grandchildren, and even our science itself to a way of thinking that, in the first place, can’t explain itself, and in the second place, has had some large and dismal practical failures in the past?

In the meantime, in this book, we must get on with trying to build a base for a universal moral code. Reality requires that we do so. It will not let us procrastinate. It forces us to think, choose, and act every day, and to do these well, we need a guide—that is, a moral code. Empiricism as base for the moral code project simply does not inspire confidence.

Is there something else to which we might turn?

Notes

1. “Lysenkoism,” Wikipedia, the Free Encyclopedia. Accessed April 1, 2015. http://en.wikipedia.org/wiki/Lysenkoism.

2. Rudolf Carnap, The Logical Structure of the World and Pseudoproblems in Philosophy (Peru, IL: Carus Publishing, 2003).

3. Willard V.O. Quine, “Two Dogmas of Empiricism,” reprinted in Human Knowledge: Classical and Contemporary Approaches, ed. Paul Moser and Arnold Vander Nat (New York, NY: Oxford University Press, 1995), p. 255.

4. Hilary Putnam, “Why Reason Can’t Be Naturalized,” reprinted in Human Knowledge, ed. Moser and Vander Nat, p. 436.

5. John Locke, An Essay Concerning Human Understanding (Glasgow: William Collins, Sons and Co., 1964), p. 90.

6. Donelson E. Delany, “What Should Be the Roles of Conscious States and Brain States in Theories of Mental Activity?” PMC Mens Sana Monographs 9, No. 1 (2011): 93–112. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115306/.

7. Antti Revonsuo, “Prospects for a Scientific Research Program on Consciousness,” in Neural Correlates of Consciousness: Empirical and Conceptual Questions, ed. Thomas Metzinger (Cambridge, MA, & London, UK: The MIT Press, 2000), pp. 57–76.

8. William Baum, Understanding Behaviorism: Behavior, Culture, and Evolution (Malden, MA: Blackwell Publishing, 2005).

9. Tom Meltzer, “Alan Turing’s Legacy: How Close Are We to ‘Thinking’ Machines?” The Guardian, June 17, 2012. http://www.theguardian.com/technology/2012/jun/17/alan-turings-legacy-thinking-machines.

10. Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (New York, NY: Basic Books, 1999).

11. “Halting Problem,” Wikipedia, the Free Encyclopedia. Accessed April 1, 2015.  http://en.wikipedia.org/wiki/Halting_problem.

12. Alva Noë and Evan Thompson, “Are There Neural Correlates of Consciousness?” Journal of Consciousness Studies 11, No. 1 (2004), pp. 3-28.  http://selfpace.uconn.edu/class/ccs/NoeThompson2004AreThereNccs.pdf.

13. Richard K. Fuller and Enoch Gordis, “Does Disulfiram Have a Role in Alcoholism Treatment Today?” Addiction 99, No. 1 (Jan. 2004), pp. 21–24. http://onlinelibrary.wiley.com/doi/10.1111/j.1360-0443.2004.00597.x/full.

14. “Nazi Human Experimentation,” Wikipedia, the Free Encyclopedia. Accessed April 1, 2015. http://en.wikipedia.org/wiki/Nazi_human_experimentation.

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Chapter 3.                           (continued)  

 

                                                                  

                                                              Diagram of the human brain.

 

                                               A single neuron, showing its branching structure.

The last few paragraphs describe only the dead ends that have been hit in AI. Other sciences searching for this same holy grail—a clear, evidence-backed model of human thinking—haven’t fared any better. Neurophysiology and behavioural psychology also keep striking out.

If a neurophysiologist could set up an MRI or similar imaging device and use his model of thinking to predict which networks of neurons in his brain would be active when he turned the device on and studied pictures of his own brain activities, in real time, then he and his colleagues could finally say they had formulated a reliable working model of what consciousness is. But on both the theoretical and practical sides, neuroscience is not even close to being so complete.

Patterns of neuron firings mapped on one occasion when a subject is performing even a very simple task unfortunately can’t be counted on. We find different patterns of firings every time we look. A human brain contains one hundred billion neurons, each one capable of connecting to as many as ten thousand others, and the patterns of firings in that brain are evolving all the time. Philosophers looking for a solid base for empiricism are disappointed if they go to neurophysiology for that base.¹²


            

Similar problems beset behavioural psychology. Researchers can condition rats and predict what they will do in controlled experiments, but many exceptions have to be made to behaviorist explanations of what humans do in everyday life.

                      

In a simple example, alcoholics who say they truly want to get sober for good can be given a drug that makes them violently, physically ill if they imbibe even very small amounts of alcohol, but that does not affect them as long as they do not drink alcohol. This would seem to be a behaviourist’s solution to alcoholism, one of society’s most intractable problems. But alas, it doesn’t work. Thousands of alcoholics have kept on with their self-destructive ways while on disulfiram.¹³ What is going on in these cases is obviously much more complex than behaviourism’s best theories can account for. And this is but one simple example.

I, for one, am not disappointed to learn that the human animal turns out to be a very complex, evolving, open-ended piece of work, no matter the model under which we analyze it.

At present, it appears that empiricism can’t provide a rationale for itself in theoretical terms and can’t demonstrate the reliability of its methods in material ways. Could it be another set of interlocking, partly effective illusions, like medieval Christianity, Communism, or Nazism once were? Personally, I don’t think so. The number of achievements of science and their profound effects on our society’s way of life argue powerfully that science is a way of thinking that gets results in the real world, even though its theories and models are constantly being updated and even though its way of thinking can’t logically justify itself.

However, sometimes models of reality from some of our once most widely believed and trusted scientific theories—for example, Newton’s laws of motion—have turned out to be inadequate for explaining more detailed data drawn from more advanced observations of reality. The mid-nineteenth-century views of the universe provided by better technologies and bigger telescopes led astronomers past Newton’s laws and eventually toward Einstein’s theory of relativity. Newton’s picture of the universe turned out to be naïve but still quite useful on our everyday scale.

Thus, considering how revered Newton’s model of the cosmos once was, yet knowing now that it gives only a partial and inadequate picture of the universe, can cause philosophers and even ordinary folk to doubt the way of thought that is basic to science. One can’t help but question whether empiricism is trustworthy enough to be used as a base for something as desperately important as a moral code for our species. Our survival is at stake here. Science can’t even provide a model that can explain science itself.

Chapter 3.                         (continued) 

                        

                                                                       Kurt Gödel.

For years, the most optimistic of the empiricists were looking to AI for models of thinking that would work in the real world. Their position has been cut down in several ways since those early days. What exploded it for many was the proof found by Kurt Gödel, Einstein’s companion during his lunch hour walks at Princeton. Gödel showed that no rigorous system of symbols for expressing the most basic of human thinking routines can be a complete system. (In Gödel’s proof, the ideas he analyzed were basic axioms in arithmetic.) Gödel’s proof is difficult for laypersons to follow, but non-mathematicians don’t need to be able to do that formal logic in order to grasp what his proof implies about everyday thinking. (See Hofstadter for an accessible critique of Gödel.¹⁰)

             

                                                                   Douglas Hofstadter.

If we take what it says about arithmetic and extend that finding to all kinds of human thinking, Gödel’s proof says no symbol system exists for expressing our thoughts that will ever be good enough to allow us to express and discuss all the new ideas human minds can dream up. Furthermore, in principle, there can’t ever be any such system of expression.

What Gödel’s proof suggests is that no way of modelling the human mind will ever adequately explain what it does. Not in English, Logic, French, Russian, Chinese, Java, C++, music, or Martian. We will always be able to generate thoughts, questions, and statements that we can’t express in any one symbol system. If we find a system that can be used to encode some of our favourite ideas really well, we will only discover that no matter how well the system is designed, no matter how large or subtle it is, we will have other thoughts that we can’t express at all in that system. Yet we have to make statements that at least attempt, more or less adequately, to communicate our ideas. Science, like most human endeavours, is social. It has to be shared in order to advance.

Other theorems in computer science offer support to Gödel’s theorem. For example, in the early days of the development of computers, programmers were continually creating programs with loops in them. After a program had been written, when it was run it would sometimes become stuck in a subroutine that would repeat a sequence of steps from, say, line 193 to line 511 then back to line 193, again and again. Whenever a program contained this kind of flaw, a human being had to stop the computer, go over the program, find why the loop was occurring, then either rewrite the loop or write around it. The work was frustrating and time consuming.

Soon, a few programmers got the idea of writing a kind of meta-program they hoped would act as a check. It would scan other programs, find their loops, and fix them, or at least point them out to programmers so they could be fixed. The programmers knew that writing such a program would be difficult, but once it was written, it would save many people a great deal of time.

However, progress on the writing of this check program encountered difficulty after difficulty. Eventually, Alan Turing published a proof showing that writing a check program was, in principle, not possible. A foolproof algorithm for checking other algorithms is, in principle, not possible. (See “Halting Problem” in Wikipedia.¹¹)

 This finding in computer science, the science many people see as our bridge between the abstractness of thinking and the concreteness of material reality, is Gödel all over again. It confirms our deepest feelings about empiricism. It is doomed to remain incomplete. No completely effective check program has ever been found. Programs that are able to catch beginner programmers’ simpler mistakes have been written, but no foolproof one has ever been created in any of the many programming languages that have evolved in the field over the years.

The possibilities for arguments and counter-arguments on this topic in AI are fascinating, but for our purposes in trying to find a base for a philosophical system and a moral code, the conclusion is much simpler. The more we study both the theoretical points and the real-world evidence, including evidence from science itself, the more we’re driven to conclude that the empiricist way of seeing or understanding what thinking and knowing are will probably never be able to explain itself. If Gödel’s proof is right, and nearly every expert in math and computer science thinks it is, and if it is extended to human thinking in general, empiricism’s own methods have ruled out the possibility of an unshakeable empiricist beginning point for epistemology.

If I think I have found a way to describe what thinking is, then I will have to express what I want to say about the matter in a language of some kind—English, Russian, C++, or some other kind of language for encoding thoughts. But there is not, nor can there be, a code capable of capturing and communicating what the thinker is doing as she is thinking about her own thinking. It is a mental conundrum with no solution. (What is the meaning of the word meaning?)

Chapter 3.                              (continued) 

Our system of knowledge – in the end – gets its credibility with us because it, mostly, gets results. It enables us to, at least partly, predict and control what’s coming.

We can see that most of the laws that have been formulated by scientists really do work. They guide us toward ways of living that get results. Why they work and how much we can rely on them—i.e. how much we can trust science—are a lot trickier to explain.

Now, while the problems described so far bother philosophers of science a great deal, such problems are of little or no interest to the majority of scientists themselves. They see the lawlike statements that they and their colleagues try to formulate as being testable in only one meaningful way; namely, by the results shown in replicable experiments done in the lab or in the field. Thus, when scientists want to talk about what knowing is, they look for models not in philosophy, but in the branches of science that study human thinking. However, efforts to provide material proof of empiricism—for example, in neurology—also run into problems. 

In his writings, the early empiricist John Locke basically dodged the problem when he defined the human mind as a “blank slate” and saw its abilities to perceive and reason as being due to its two “fountains of knowledge,” sensation and reflection. The first, he said, is made up of stores of sensory experiences and memories of sensory experiences. The second is made up of the “ideas … the mind gets by reflecting on its own operations within itself.” How these kinds of operations got into human consciousness and what is doing the reflecting on these operations, he doesn’t say.⁵

Modern empiricists, both philosophers of science and scientists themselves, don’t care for their forebears giving in to this kind of mystery-making. Scientists in particular aim to figure out what the mind is and how it thinks by studying not words but physical things such as the human genome and what it creates, namely—among its many other creations—the neurons of the brain. That is the modern empiricist way, the scientific way.

For today’s scientists, discussions about what knowing is, no matter how clever, are not bringing us any closer to understanding what knowing is. In fact, typically scientists don’t respect discussions about anything we may want to study unless they are backed with scientific theories or models of the thing being studied, and the theories are further backed with research conducted on real things in the real world.

Scientific research, to qualify as scientific, must also be designed so it can be replicated by any researcher in any land or era. Otherwise, it’s not credible; it could be a coincidence, a mistake, wishful thinking, or simply a lie. Thus, for modern scientists, the analysis of material evidence offers the only route by which a researcher can come to understand anything, even when the thing she is studying is what’s happening inside her as she studies.

She sees a phenomenon in reality, gets an idea about how it works, designs an experiment, tests her theory, then records the results and interprets them. The aim of her statements is to guide future research onto more fruitful paths and to build technologies that are increasingly effective at predicting and manipulating events in the real world. Electro-chemical pathways among the neurons of the brain, for example—individual paths and whole patterns of such paths—can be studied in labs and correlated with subjects’ perceptions. (The state of research in this field is described by Donelson Delany in a 2011 article available online and in several other articles, notably Antti Revonsuo’s in Neural Correlates of Consciousness: Empirical and Conceptual Questions, edited by Thomas Metzinger.^6,7)

Observable things are the things science cares about. The philosophers’ talk about what thinking and knowing are is just that—talk.

As an acceptable alternative to the study of brain structure and chemistry, scientists interested in thought also study patterns of behaviour in organisms like rats, pigeons, and people that are stimulated in controlled, replicable ways. We can, for example, try to train rats to work for wages. This kind of study is the focus of behavioural psychology. (See William Baum’s 2004 book Understanding Behaviorism.⁸)

As a third alternative, we can even try to program computers to do things that are as similar as possible to the things humans do. Play chess. Knit. Write poetry. Cook meals. If the computers then behave in humanlike ways, we should be able to infer some tentative, testable conclusions about what human thinking and knowing are from the programs that enabled these computers to behave so much like humans. This kind of research is done in a branch of computer science called artificial intelligence or AI.

To many empiricist philosophers and scientists, AI seems to offer the best hope of defining, once and for all, a base for their way of thinking that can explain all of human thinking’s abstract processes and that is also materially observable. A program either runs or it doesn’t, and every line in it can be examined. If we could write a program that made a computer imitate human conversation so well that we couldn’t tell which was the computer responding and which was the human, we would have encoded what thinking is. At last, scientists had a beginning point beyond the challenges of the critics of empiricism and their endless counterexamples. (A layman’s view on how AI is faring can be found in Thomas Meltzer’s article in The Guardian, 17/4/2012.⁹)

Testability and replicability of the tests, I repeat, are the characteristics of modern empiricism and of all science. All else, to modern empiricists, has as much reality and as much reliability to it as creatures in a fantasy novel … amusing daydreams, nothing more.

Chapter 3.                              (continued)

Even the terms contained in natural law statements are vulnerable to attack by the skeptics. Hume argued more than two hundred years ago that we humans can’t really know that any of the laws we think we see in nature are absolutely true because when we state a natural law, the terms we use to name the objects and events we want to focus on exist only in our minds. A simple statement that seems to us to make sense, like the one that says hot objects will cause us pain if we touch them, can’t be trusted in any ultimate sense. To assume that this “law” is true is to assume that our definitions for the terms hot and pain will continue to make sense in the future as they have in the past. But we can’t know whether these assumptions will hold in the future. We haven’t seen the future. Maybe, one day, people won’t feel pain.

Thus, all of the terms in natural law statements, even terms like galaxies, protons, atoms, acids, genes, cells, and so on, are fabrications of our minds, terms that we create because they help us to sort and categorize our sensory experiences and memories of sensory experiences and talk to one another about what seems to be going on around us. But reality does not contain things that are somehow naturally fit as atoms, cells, or galaxies. If you look at a gene, it won’t be wearing a name tag that reads “Gene.” In Somali, it is called “hiddo”.   

Right from the start, our natural law statements must gamble on the future validity of our current mental categories—that is, our human-invented terms for things. The terms can seem sound, but they are still gambles, and some terms that humans once gambled on with great confidence turned out later, in the light of new evidence, to be naïve and inadequate.

 

                                                                           Isaac Newton.

Isaac Newton’s laws of motion are now seen by physicists as being useful, low-level approximations of the subtler, relativistic laws of motion formulated by Einstein. The substance called phlogiston once seemed to explain all of chemistry. Then Antoine Lavoisier did some experiments showing phlogiston didn’t exist. On the other hand, people spoke of genes long before microscopes that could reveal them to the human eye were invented, and people still speak of atoms, even though no one has ever seen one. Some terms last because they enable us to build mental models and do experiments that get the results we predicted. For now. But the list of scientific theories that eventually “fell from fashion” is very long.

 

                                                                       Antoine Lavoisier.

Various further attempts have been made in the last hundred years to nail down what scientific thinking does and to prove that it is a reliable way of knowing, but they have all come with insoluble conundrums of their own.

The logical positivists, for example, tried to bypass Hume’s problems with the terms in scientific laws and to place the burden of meaning and proof onto whole propositions instead. A key point in the logical positivists’ case is that all meaningful statements are either analytic or synthetic. Any statement that does not fit into one of these two categories, the positivists say, is irrelevant noise.

Analytic statements are those whose truth or falsity is determined by the definitions of the terms they contain. For example, “All bachelors are unmarried men” is an analytic statement. If we understand the terms in the sentence we can immediately verify, by thinking it through, whether the statement is true.

Synthetic statements are those whose truth or falsity we must work out by referring to evidence found in the real, empirical world, not in the statement itself. “All substances contract when cooled” is a synthetic statement—not quite a true one, as observations of water and ice can show. So is “If a creature is a whale, then it is a mammal.”

The logical positivists aimed to show that discussions between scientists in all disciplines can be made rigorously logical and can therefore lead us to true knowledge. They intended to apply their analytic–synthetic distinction to all statements in such a rigorous way that any statement made by anyone in any field could be judged by this standard. If the truth or falsity of a statement had to be checked by observations made in the real, material world, then it was clearly a synthetic statement. If the statement’s truth value could be assessed by careful analysis of its internal logic, without reference to observations and measurements made in the material world, then the statement was clearly an analytic statement. Idea exchanges that were limited to only these types of statements could be logically sound. All other statements were to be regarded as meaningless.

The logical positivists argued that following these prescriptions was all that was needed for scientists to engage in logically sound discussions, explain their research, and size up the research of their fellow scientists. This would lead them by gradual steps on to true, reliable knowledge of the real world. All other communications by humans were to be regarded as forms of emotional venting, empty of any real content or meaning.

                                   

                                                                                 Rudolf Carnap.

Rudolf Carnap, especially, set out prove that these prescriptions were all that science needed in order for it to work and to progress in a rigorously logical way toward making increasingly accurate statements about the real world—generalizations that could be trusted as universal truths.²

                                  

                                                                                Willard V.O. Quine.

But the theories of Carnap and the other positivists were taken apart by later philosophers such as Willard Quine, who showed that the crucial positivist distinction between analytic and synthetic statements was not logically defensible. Explaining what makes an analytic statement (e.g., “All bachelors are unmarried men”) analytic requires that we first understand what synonymous terms like bachelors and unmarried men are. But if we go into the logic carefully, we find that explaining what makes two terms synonymous presupposes that we first understand what analytic means. In short, trying to lay down precise rules for defining the difference between analytic statements and synthetic ones only leads us to reason in circles.³

  

Quine’s reasoning, in turn, was further critiqued and refined by later philosophers like Hilary Putnam. As Putnam eventually put the matter:

“… positivism produced a conception of rationality so narrow as to exclude the very activity of producing that conception” and “… the whole system of knowledge is justified as a whole by its utility in predicting [future] observations.”⁴

In other words, logical positivism’s rigid way of talking about thinking, knowing, and expressing ends up in a logically unsolvable paradox. It creates new problems for all our systems of ideas and doesn’t help with solving any of the old problems. 

Chapter 3.                                        (continued) 

Scientists now know that most of the concepts we use to recognize and respond to things are concepts we were taught by the mentors and role models we had as children; we don’t discover very many concepts on our own. Our childhood programming teaches us how to cognize things. After that, almost always, we don’t cognize things, only recognize them. (Why our childhood mentors programmed us in the ways they did will be explored in upcoming chapters.)

Empiricists claim that all human knowing and thinking happens in this way. Watch the world. Notice the patterns that repeat. Store them up in memories. Pull the memories out and, when they fit, use them to make smart decisions and react effectively to life. Remember what works and keep trying. For individuals and nations, according to the empiricists, that’s how life goes. The most effective way of human life, the way that makes this common sense process rigorously logical, is science.

There are arguments against this way of thinking about thinking and this model of how human thinking and knowing work. Empiricism is a way of seeing ourselves and our minds that sounds logical, but it has its problems.

Opponents of empiricism and science have long asked, “When a human sees things in the real world and spots patterns in the events going on there, then makes statements about what she is spotting, what is doing the spotting? The human mind, and the sense data–processing programs it must already contain to be able to do the tricks empiricists describe, obviously came before any data processing could be done. What is this equipment, and how does it work?”  

Philosophers of science have had trouble explaining what this mind that does the knowing is, and thus what science, the most rigorous form of knowing, is and is trying to do.

Consider what science is aiming to achieve. What scientists want to discover, come to understand, and then use in creative ways in the real world are what are usually called the “laws of nature”. Scientists do more than simply observe the events in physical reality. They also strive to understand how these events come about and then to express what they understand in general statements about these events, in mathematical formulas, in chemical formulas, in rigorously logical sentences in one of the world’s many languages, or in some other symbol system used by people for conveying their thoughts to other humans. A “natural law” statement must describe one of the ways in which reality works, and, to be considered scientific, the statement must be set down in such a way that it can be tested in the real world.

If claims about this newly discovered real-world truth are going to be worth considering, scientists must be able to test those claims in some real, material way. Thus, any natural law statement that is made, to be of any practical use whatever and to stand any chance of enduring, must first be expressed in some language or symbol system that humans use to communicate ideas with other humans. A theory or model that can be expressed only inside the head of its inventor will die with her or him.

The following is a verbal statement of Newton’s law of universal gravitation: “Any two bodies in the universe attract each other with a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between them.”

In contrast, the mathematical formula expressing Newton’s law of universal gravitation looks like this:

                                  

And consider another example:

The Pythagorean theorem is a mathematical law, but is it a scientific one? Can it be tested in some absolutely unshakable way in the real world? (Hint: How can you measure the sides and know you’re exactly accurate?)

The big problem occurs when we try to analyze logically just how true statements like Newton’s laws of motion or Darwin’s theory of evolution are. Do statements of these laws express unshakeable truths about the real world or are they just temporarily useful ways of roughly describing what appears to be going on in reality – ways that are followed for a few decades while the laws appear to enable scientists to predict events in reality, but that then are revised or dropped when new problems they can’t explain are encountered?

Many scientific theories in the last four hundred years have been revised or dropped altogether. Do we dare to say about any natural law statement that it is true in the unassailable way in which 5 + 7 = 12 is true or the Pythagorean theorem is true?

This debate is a hot one in Philosophy right up to the present time. Many philosophers of science claim that natural law statements, once they’re supported by enough experimental evidence, can be considered to be true in the same way as valid math theorems are. But there are also many who say the opposite —that all scientific statements are tentative. These people believe that, given time, all such statements get replaced by new statements based on new models or theories.

If all natural law statements are seen as being only temporarily true, then science can be seen as a kind of fashion show whose ideas have little more shelf life than the fads in the usual parade of clothes, makeup, hairstyles, television shows, and songs on the radio. Or put another way, science’s law statements all become just more narratives, not necessarily true so much as useful, but useful only in the lands in which they gain some currency and only for limited time periods at best.

And the logical flaws that can be spotted in empiricist reasoning are not small ones.