Home > Articles > Usable Knowledge

Usable Knowledge

by Bill Lauritzen

Part I: What is Knowledge?

The word “abstract” has fallen from fashion, unfortunately. Its root means “to draw away from.” It’s related to the word “extract” which means “to draw out of.” You can “extract” the juice from an orange. “Abstract” also means “the concentrated essence of a larger whole.” (American Heritage.)

Words, ideas, theories, models, etc., are all abstractions. They all attempt to give the concentrated essence of some (much larger) energy event-pattern of the physical universe.

Abstractions (extractions):

ideas, words, maps, theories, models, principles, dogma, knowledge ...

When people abstract, they extract something, hopefully the essence. A wine maker extracts the juice of the grape from the grape—for him the essence of the grape. A reporter describes the essence of what happened in the riot—he or she does not try to tell everything that happened. The newspaper headline attempts to extract or abstract still more.

Mathematics attempts to extract-abstract the essence of patterns and motion.

Korzybski in his classic 1933 book, Science and Sanity discusses abstraction and rightly points out the value of being “aware of abstraction.” He used to say, “The map is not the territory, and the word is not the thing.” He also notes that when you abstract, you leave some things out. (I would say filter them out.)

On the most fundamental level, the human organism, Homo sapiens, abstracts or extracts, from the energy available in the ambient environment. For example, from the energy known as “electromagnetic,” the human organism extracts, via the eyes, only a very small percent of this energy.

Those human organisms that could sense this particular energy (a range of frequencies) had some reproductive advantage over their contemporaries.

Other species see other, different energies. Dogs can hear higher frequencies. We hear only 20% of what bats and dolphins hear. Rattlesnakes and pit vipers can sense infrared heat patterns. Hawks have better vision, bloodhounds have better smell.[i]

Korzybski showed abstraction as coming down from reality as such:






This implies that abstractions are somehow inferior to reality. Hayakawa, on the other hand showed abstractions as higher than reality in an opposite manner:

infer or judge





I prefer to show them both at the same level, something like this:

reality > experience > perceive > describe > make-a-rule


reality > try > see > say

In this manner, we emphasize the importance of both reality as well as abstractions from reality. Neither is more valuable and each is dependent on the other.

The mind can combine various abstractions together and make what we call sentences.

I believe the grammar of these sentences reflects (and through a feedback loop, helps create) the world view (as proposed by Whorf). Thus, grammar matches (and is derived from) the energy events surrounding our species. “He kicked the ball.” The noun (subject) causes action (verb) and the ball was acted upon. This mimics our perception of events in the English speaking world: object-action-object (cause-effect) (noun-verb-noun). Grammar contains a hidden Unified Field Theory.

As shown the diagram, we have different levels of abstraction. Reality includes all the sounds we cannot hear and sights we cannot see such as radio waves and cosmic rays. The most juicy level we have is experience. Would you rather have sex, or watch someone have sex, or talk about sex? Having sex is the at experience level, watching someone have sex is at the perceive level, and talking about it is at the describe, or judge level.

In this book, I shall take the position that knowledge is a “cognitive map.” You could also call it a “mental map.” Or even “knowledge map.” Sometime after the perceive level one begins to form a “map” in the mind (though various neuronal, and synaptic, and dendritic connections). One of the main themes of this book is that religion is a knowledge map also.

These maps form naturally and enable us to navigate through life. Hopefully, our maps become more accurate as we get older. Also our cultural maps (or societal or civilization maps) become more concise and accurate.

It is possible that the civilization that will survive will have the most concise and accurate (refined) knowledge maps. More on this later.

Abstractions (mental maps) enable us to extract the essence of something and then perhaps combine it with other abstractions and make fairly accurate predictions, which leads to better survival.

Part II:

Gaining Knowledge: The Try-See-Say Cycle

Nowadays the formal method of gaining knowledge is called science. Although the many people think of science as a body of facts, in its pure form it is considered a method. An attempt is made in the schools to teach it. This is a model of how science is supposed to work. As it is usually represented, it goes something like this:

1) Hypothesis 2) Experiment 3) Observation
4) Results 5) Discussion 6) Conclusion

In this article, I will present what I believe is a simpler and easier to remember model of science.
Of course, no model is perfect. The new model, like the standard model, is merely an attempt to simply represent what is really going on in the process of gaining knowledge called science.
I call this new model the Try-See-Say cycle. It can be represented as a triangle with each of the three key words in one corner. The whole triangle can be set in a circle that represents the “survival problems of the organism and community.”

Figure 1.

Science (and life) ultimately is an attempt to solve problems relating to survival of the organism, community, and species.

science = prediction = future say

I believe that science can be equated with prediction. The theories, models, laws, principles, etc., are all an attempt to say what is going to happen. On a primitive level, a scientist might say, based on a theory of the seasons, or a theory of astronomy, when the best time to plant the crops will be. Searching for correlation is an attempt to be able to predict what will happen to one variable when another variable changes.

Try would represent the experiment and the experimental design, see would represent observation, data (or results), and statistical analysis, and say the discussion, hypothesis, theory, model, and prediction. The cyclical nature of this new model is, I believe, more accurate in terms of what scientists actually do. In other words, when they get their results, they often then make a new hypothesis or modify the existing one, etc.

Noted science philosopher Karl Popper (Popper, 1996) summed his view of the scientific method in these four steps: 1) Problems 2) Theories 3) Criticisms 4) New Problems. It is instructive to note the cyclical similarity to the Try-See-Say cycle.

The Try-See-Say cycle is easy to remember because we only have to remember 3 points. This is in line with psychological research that says that data should be broken up into chunks for easy of memory. (See Wortman and Loftus, p. 180) (For example, your phone number is broken up into area code, prefix, and then a number.) In fact, it may be easier to remember the standard model by clustering two items with each. For example: try (Experiment and Control), see (Observation and Data) and say (Conclusion and new Hypothesis).

Figure 2.

The new model is much more flexible than the old model, in that one can start anywhere in the cycle. Thus, there are three possibilities:

1) Try-See-Say

2) See-Say-Try

3) Say-Try-See

1) Try-See-Say: a scientist is doing an experiment, or doing anything, a try and perhaps notices something strange (an anomaly) or something interesting. The thing that he sees encourages him to say something (make a hypothesis, theory, or model about how the thing came to be). Then he (or she) can do an experiment, a try, to test the say. And the cycle goes on around. This is a good approximation of how much science occurs. Many scientists have said that important discoveries came about by accident. For example, the discovery of penicillin was made in 1928 when Alexander Flemming was searching (trying) for a way to stop certain dangerous bacteria. He forgot to throw away one of the dishes he was using which contained the dangerous bacteria. Later, when he went to throw it away, he noticed (a see) that none of the bacteria culture was growing around a certain mold, and that mold eventually led to penicillin.

2) See-Say-Try: a scientist sees something in the real world, or sees the results of another’s experiment, and, based on this, decides to say something (a theory, hypothesis, or model), which leads them to try something (an experiment), and then through the cycle again. Wegener’s theory of Continental Drift, which later evolved into the theory of plate tectonics, might be a good example of this category. Wegener noticed (saw) certain similarities between the plants and animals of Africa and South America. He also saw a fit between the outlines of continents. This led to his theory, or say, that the continents had once been part of a supercontinent that he called Pangaea.

3) Say-Try-See: a theory, hypothesis or model (or what someone says), encourages or inspires the scientist to try something, (an experiment), which leads to him or her seeing something, perhaps something that they hadn’t seen before, and on around the cycle again. The famous Michelson-Morley experiment might be a good example of this type. The current theories of physics at the time, 1887, predicted (said) that one should be able to measure the velocity of the earth through a theoretical “ether” of space. Michelson and Morely tried to measure it, but were unable to see it. (This later helped lead Einstein to say his theory of relativity.)

Of course, in science, we often create an artificial situation in order to find principles or laws that adequately describe or approximate reality. In terms of the new model, we try things that do not normally occur, in order to see better, and say better.

Thus, science is a systematic and organized way of trying and seeing and saying. Through good experimental design, we can get the most seeing for the least trying, which saves bucks. It is to be hoped that we end up with a nice, concise saying, that will last us a little while, before a newcomer tries something that discredits it.

The say is actually the Homo sapiens’ storage of what it has learned about its surroundings through trying and seeing. This say can be stored within the organism, or without, as in books, tapes, CDs, computer chips, etc.

Now that we have a model of science, let’s see if it gives us any insight into what is non-science and what is fake-science. Anything that doesn’t include both try and see would be classified as not-science or fake-science, and that includes 3 possibilities:

1) Say-Say-Say (no Try or See): if some were to just say something, as a braggart often does, or “divinely inspired” charlatan, or someone merely out to gain status or money, then this would be an example of not-science. Certainly much fake-science would fit into this category, and also perhaps certain religious dogma. Much of education, as it is currently practiced, is just Say-Say-Say. Very boring for the student.

1) Say-Say-Say (no Try or See): if some were to just say something, as a braggart often does, or “divinely inspired” charlatan, or someone merely out to gain status or money, then this would be an example of not-science. Certainly much fake-science would fit into this category, and also perhaps certain religious dogma. Much of education, as it is currently practiced, is just Say-Say-Say. Very boring for the student.

3) See-Say (no Try): Here is the armchair theorist at his best. Here perhaps is Aristotle describing the motion of falling bodies. Aristotle perhaps saw something, and then made a theory for it, but never tried the theory to see if it was true, or if it was true under all conditions. Galileo put the try back in the study of what was then called “natural philosophy.” When he dropped two balls, one of iron, one of wood, from the leaning tower of Pisa, (although he probably never did this, the story is instructive) and they both hit at the same time, try echoed throughout the world, Aristotle’s theory was shattered, and along with it the See-Say of the ancient natural philosophers. Scientists, encouraged by Galileo’s success, have been trying things ever since.

What about the importance of replication in science? This could be represented by repetitive Try-See: Try(1)-See(1)-Try(2)-See(2)-Try-(3)-See(3) ... (In science, our motto might be, “If at first you succeed, try, try, again.”) No say is necessary in replication, as you just want to see if you get the same results each time.

What about the importance of a control group? This might be represented by: Try(c)-See(c)-Try(e)-See(e). (e for Experimental and c for Control.) If you had many experimental groups they would be added on and numbered.

There is an even simpler model (probably too simple) which is just good old Try-See (try and see). It is what your mother or father might have said if you asked them, “Mom, will this work?” The Try-See cycle requires no language. Thus, we can generalize that a baby is using primitive science as it explores its environment: testing, examining, poking, looking.

I believe the Try-See-Say cycle parallels to a degree the actions of an organism. The information processing model of Input-Organism-Output has been used in psychology and biology. The Input would, of course, be our see and the Output would be our try. The say would include the thinking (pre-language thinking for a baby), which occurs during the Organism phase. (In other words, I use say in the most general sense to include thinking, deciding, and all other mental processes.)

A more elaborate and correct model of the organism’s behavior is contained in the field of perceptual control theory as originally elaborated by William Powers. This theory shows that the (evolved) organism does not respond to stimuli, but that rather responds in order to control stimuli. (The temperature is too cold so he puts on a sweater. He is trying to control his input stimuli—to keep it at a certain level.

The Try-See-Say cycle is a continuous activity of an organism’s nervous system, and therefore, it is very difficult to say which of the three came first. It is like asking which came first: the chicken or the egg?

It appears that all organisms do a kind of science. Science being a formalized extension of life’s process.

Figure 3.

Obviously, it would be more accurate to use Try-Perceive-Say, or Try-Sense-Say, rather than Try-See-Say. A blind person can get sensory information from his environment without the use of sight. He can experiment. However, to be honest, I chose not use Perceive or Sense, as I think people would not remember it. More on this later.

In conclusion, I believe the Try-See-Say model could replace the traditional one (Hypothesis, Experiment, Observation, Results, Discussion, Conclusion) currently taught in our school systems. The Try-See-Say cycle is simpler, yet more accurate in some respects. It also may be useful in helping people detect certain forms of non-science and fake-science. It suggests that science is merely Homo sapiens formalized extension of life’s basic process of responding to, sensing, and processing stimuli in order to survive.

All of what I have said concerning science probably applies as well to all of Homo sapiens knowledge. In other words, the acquisition of knowledge, itself, may be modeled using the Try-See-Say cycle. The knowledge is the say.

Part III:
The Useability of a Mental Map

During the 18th and 19th century perhaps many natural philosophers (who later came to be called scientists), were trying to verify theories. Karl Popper, in the 20th century, brought into fashion the idea of falsification of a theory. To him, a theory was scientific if there was some critical test that could be done that might prove the theory wrong. Popper was apparently inspired by Einstein’s theory of relativity, which made specific predictions (the bending of light by matter) which were highly risky because events could have proved them wrong.

18th & 19th Centuries Verficationism (Bacon) show theory to be true
20th Century Falsificationism (Popper) show theory to be false
21 Century Useability? show theory to be workable?

In the Stanford Encyclopedia of Philosophy (Stanford University, available on-line) Steven Thorton gives a summary of Popper’s life and works. Apparently Popper backpedaled later in life. “Popper’s final position is that he acknowledges that it is impossible to discriminate science from non-science on the basis of falsifiability alone ... This is itself clearly a major alteration of his position, and arguably represents a substantial step down on his part.”

It appears that the concept of “falsifiability,” so dear to the hearts of skeptics, is itself falsifiable (but useable). Newton’s work has been shown to be false, yet it is still very useful as science.

Aristotle heavy bodies fall faster falsified by experiment
Galileo/Newton gravity/motion equations falsified by bending light
Einstein relativity equations false inside a “black hole”?

How many scientific theories, laws, or principles are always true? Perhaps none. If one examines the sequence from Aristotle to Galileo/Newton to Einstein one sees changes in theory. Each theory apparently corrected the flaws in the previous theory.

However, it is possible that Einstein’s equations do not hold true inside a black hole. (Thorne, 1993, p. 476). (It is also quite possible that black holes do not exist. See the work of Mamikon Mnatsakanian.) Instead of talking about the truth or falsity of a theory, we should be talking about its useability.

An analogy could be drawn to a map (which is a say). A map is useful, but is it true or false? Imagine a map of the United States. Someone interested in verification would take the map and venture out into reality (a try). This investigator might find the Mississippi River and the Ohio River and say, “Yep. Here they are. Right where the map said they would be. This is a true map.” Another investigator, interested in falsification, might take the map to the same area (a try) and say, “ Here is a stream not on the map. Here is another stream not on the map. This is a false map.”

Theories, laws, words, principles, and ideas have strong similarities to a map. As I said earlier, they are all abstractions of the physical universe, and so leave out some part of that universe in an attempt to capture a smaller essence. Although it can appear that some theories are perfect, and thus somehow were abstracted from the essence of the universe, I believe that over time they are shown (or will be shown) to have some correspondence with reality, but not a perfect correspondence.

Oh Say, Can You See?

The say may affect the see. Thomas Kuhn wrote about this in his classic work, The Structure of Scientific Revolutions, and popularized the word “paradigm.” What we say, our world-view or paradigm, said Kuhn, may affect what we see. I think the say affects the see, for a period of time, until reality intrudes.

Perhaps one example of this is the word “sunset.” This word describes an event that we have suspected to be false for about 500 years--ever since Copernicus first suggested that the Earth goes around the sun, instead of the sun going around the Earth. Yet, what do most of us see when this energy-event-pattern takes place? We see the sun going “down.”

I have suggested using the word “spin-out” instead of “sunset.” The point on the Earth where you are standing is spinning to face the outer part of the solar system. The energy event pattern known as “sunrise” could be called “spin-in.” (Maybe you can think of a better word.)

Although the say probably affects the see to some degree in all of us, the say may affect the see, in certain individuals, at certain times, more than others. In fact, an individual of Homo sapiens may lie along a scale, or spectrum, of Belief-Disbelief that ranges from fanatic to cynic. (We might call this the belief in the say.)

+180 Fanatic Unjustified enthusiasm. Zealot. Sometimes willing to die so that a principle, model, theory, dogma may live.
+120 Gullible Believer Blindly buys, swallows, and trusts despite evidence to the contrary. Looks for minor similarities between theory and reality as evidence in favor of the theory; naive.
+60 Cautious Believer Looks for major similarities between theory and reality as evidence in favor of the theory.
0 Pragmatist Realist; realizes that no theory/model can be perfect. Accepts those that work (or parts of those that work) in aiding survival. Discards what doesn’t work.
-60 Healthy Skeptic Looks for major differences between theory and reality as evidence against the theory.
-120 Nitpicker Doubter; looks for minor differences between theory and reality as evidence against the theory. Seldom buys, swallows, or trusts despite evidence to do so; suspicious.
-180 Cynic Suspicious, scoffer; nihilist; believes in nothing; lives in order to prove others wrong, but may commit suicide as there is no “reason” to live.

Figure 4: Fanatic-Cynic Scale

I believe that sanity lies in the region between +90 and -90 on this scale. The scale could also be represented as a circle. This scale, or Belief-Disbelief Circle, may be of use in training individuals to think clearer.

Much more could be said about this scale, but here I will add that I don’t believe that a “pragmatist” or 0 on the scale should be the goal, but rather that one should be able to move freely between -90 and +90, in response to the given situation. Obviously, with the proliferation of unsubstantiated belief in “alien abduction,” some healthy skepticism is needed. Also, I believe one could be a cynic in one area and a fanatic in another area, etc.

Another example of a healthy skeptic would be someone on the lookout (see) for maps that showed the Mississippi River running east-west instead of north-south. Or a map that showed no Mississippi River.

Useable “False” Theories

There will probably always be work for the nitpicker, as no model, by definition, is ever perfect. For example, for a map to be perfect, it would have to be as large as the Earth. In fact, it would have to work exactly like our Earth, and would therefore have to have the same mass, etc. If one tried to construct this Earth-2, it would totally disrupt the dynamics of the original Earth-1, and Earth-2 would have to change accordingly, and thus the whole thing becomes impossible.

Even theories that have been “proven false” are sometimes still highly useable. Newton’s equations are much more useable than Einstein’s, at speeds much lower than the speed of light.

My acquaintance, Dr. James Counsilman, is well known for using Newton’s Laws and Bernoulli’s principle in the analysis of swimming strokes (1994). (Since swimmers will probably never approach the speed of light, we have, in effect, principles and laws that could be considered always “true” in certain contexts.)

In another example of this, robotic planetary probes have been sent to most of the various planets of our home star in the past few decades. This has resulted in an explosion of our knowledge concerning these planets. Some think these past decades might one day be called the “golden age of planetary exploration.” All these probes have used the mathematics of Newton’s laws and the law of gravity to get where they were going. This was because these spacecraft traveled at speeds much, much, much lower than the speed of light.

The Useability of the Say

The useability of a theory, model, etc., or abstraction might equal the predictive power of that abstraction divided by its cognitive size. Cognitive size being the amount of “brain storage space” taken up by the abstraction. (It is probably not good survival practice for an organism to use up too many of its “bytes” of storage capacity, especially with information that does not match reality well.) Predictive power of a theory would increase as the correspondence between theory (in its major aspects) and reality increased. I call this correspondence the match-to-reality. Thus, we could say that the useability of an abstraction is equal to the match divided by the cognitive size. This is part of the mathematics of memes:

Useability = Match / Cognitive Size
Match = Useability x Cognitive Size
Cognitive Size = Match / Useability

Thus, advertisers are great at coming up with “catchy” phases that are easy to remember. Scientists are also. John Wheeler changed “Schwarzschild singularity” to “black hole,” and thereby helped to bring this phenomenon to public awareness.

A very detailed map may have a greater match to reality, but may become too bulky to be of much use on an expedition.

A model of the universe may fill twelve volumes in abstruse philosophical prose, but due to its sheer size would have limited useability. Newton’s laws and the theory of gravity have high useability because they match fairly well to reality (at speeds much lower than the speed of light) and they are small in size. The basic mathematical theory of gravity can be stated in one sentence. (Two objects will attract each other with a pull proportional to their masses and inversely proportional to the second power of the distance between them.)

“Useability” may partially explain the popularity of certain “non-scientific” beliefs and models. They are often small in perceived size and easy to remember.

Fake-science, or pseudo-science, is usually considered to mean models that have a low match-to-reality. Of course, a very low match would lower the useability, no matter how simple the model. Scientific theories that have a very large size would have limited useability, despite their good match.

In Part I, when I developed the Try-See-Say model of science, I choose to use Try-See-Say rather than Try-Perceive-Say, because even though Try-Perceive-Say has a greater match with reality, the storage capacity required, I felt, was too much.

An important point is that this idea (of useability as the match divided by the brain storage capacity required) defeats the postmodernist position of “any belief is just as valid as any other belief.” The beliefs that are most valid are those that have a strong match with reality. Those that are useable also require a minimum amount of brain storage space.

In Summary

1. Knowledge exists in the mind as a cognitive map.
2. Organisms, in trying to survive, and gain knowledge, can be simply modeled by the Try-See-Say cycle, which is sometimes formalized by Homo sapiens as science.
3. What Homo sapiens says are abstractions of reality, which store what the individual and species has learned.
4. What Homo sapiens says, may then affect what he sees.
5. An individual Homo sapiens may lie on a spectrum of Belief-Disbelief that goes something like this: fanatic | gullible believer | cautious believer | pragmatist | healthy skeptic | nitpicker | cynic.
6. Homo sapiens’ abstractions from reality, such as ideas, words, maps, models, scientific theories, principles (all forms of knowledge) have a useability that is equal to the match-to-reality of the abstraction, divided by the cognitive size (to Homo sapiens) of the abstraction.
Counsilman, James, and Counsilman, Brian, The New Science of Swimming, 1994.
Davis, Raymond, et al., Chemistry, Holt, Rinehart, and Winston, Austin, 1999.
Thorne, Kip, Black Holes and Time Warps, Norton, NY, 1994.
Korzybski, Alfred, Science and Sanity, Business Press, Penn., 1933, 1948.
“Popper, Karl,” Stanford Encyclopedia of Philosophy, Stanford University, (available on- line).
Popper, Karl, Karl R. Popper, (ed. M.A. Notturno), Routledge, 1996 (as quoted in Skeptic magazine, Vol. 4, No. 4, 1996, p. 104.)
Powers, William, Behavior: The Control of Perception, 1973.
Wortman, Camille B., Loftus, Elizabeth F., and Marshall, Mary, Psychology, Alfred A. Knopf, 1981.

© 1999, 2002 W. Lauritzen

[i] Wood and Wood, The Essential World of Psychology, Allyn and Bacon, Boston,1999, p.65-66.