SYSTEMATIC INQUIRY AND NEW KNOWLEDGE
R. E. Wyllys

Introduction

One of the most important responsibilities of a professional in any field is twofold: to keep up with new knowledge developed concerning the field, and to contribute to developing such new knowledge about the field. This assertion is as true of the field of library and information science (LIS) as of any other professional, scholarly, scientific, or technical field.

I use the adjectives "professional," "scholarly," "scientific," and "technical" merely to reflect shades of emphasis among the many fields in which people carefully and systematically try to improve humanity's knowledge about, and ways of dealing with, the world and universe in which we live. It would be naïve to try to draw sharp boundaries among these adjectives, or among the fields of human knowledge and inquiry to which these adjectives may be applied. There is a continuum of knowledge and inquiry from the geology of plate tectonics to the engineering techniques used to explore for petroleum. There is a continuum of knowledge and inquiry from the neurology of cognition to the art and practice of storytelling in libraries as a way of helping children develop into adults.

Systematic Inquiry and Some Alternatives

How is new knowledge developed in a field? There is one principal way, along with some interesting occasional alternative ways. The principal way is what is often called "systematic inquiry":  i.e., a careful, deliberate effort to deal with a problem, to investigate something inadequately known or understood.

An Alternative: Chance, or Serendipitous Observation and Inference

One alternative to systematic inquiry is chance, better expressed as "serendipitous observation and inference" since accidental observation alone is far less useful than informed inference from an observation, however lucky the observer may be. "Chance favors the prepared mind" ["Dans les champs de l’observation, le hazard ne favorise que les esprits préparés"] was a profound comment by Louis Pasteur (1822-1895), one of the greatest scientists of all time, whom the Encyclopedia Britannica (2001) encapsulates by describing him as a

"French chemist and microbiologist whose contributions were among the most varied and valuable in the history of science and industry. It was he who proved that microorganisms cause fermentation and disease; he who originated and was the first to use vaccines for rabies, anthrax, and chicken cholera; he who saved the beer, wine, and silk industries of France and other countries; he who performed important pioneer work in stereochemistry; and he who originated the process known as pasteurization."

Pasteur's long and fruitful career testifies brilliantly to the rewards of making--both deliberately and serendipitously--observations for which the mind is prepared. A delightful essay on serendipitous observation and inference is "Serendipity, A Graceful Word" by Roald Hoffman. I recommend that you read this essay--for both enjoyment and enlightenment.

Another Alternative: Undiscovered Public Knowledge, Data Mining, and Knowledge Discovery

A less well known, but intriguing alternative to systematic inquiry is what Don R. Swanson (an information-science pioneer and a Dean Emeritus of the lamentably now-closed Graduate Library School of the University of Chicago) has called "undiscovered public knowledge":  i.e., knowledge that has been made available to the public but whose implications and applications, especially in a different area of research or development from the original, have failed to be adequately recognized (Swanson, 1986). In his 1986 paper, Swanson expressed the hope that, eventually, programmatic techniques could be found to accomplish, in a systematic fashion, the kind of recognition of applicability, across different areas of research and development, that the human mind occasionally stumbles upon. Work on such techniques has been conducted by Swanson and others (e.g., Swanson and Smallheiser (1999)).

Related work is also going on currently under such names as "data mining" and "knowledge discovery." Walter Trybula (1997) defines the three areas as follows:

"Data mining (DM) is the basic process employed to analyze patterns in data and extract information. . . . The objective of the process is to generate a hypothesis regarding the selected data rather than to verify a hypothesis. Many of the applications involve large databases with customer information that can be investigated to glean insight on customer behavior given various marketing incentives."

"Knowledge discovery (KD) is the process of transforming data into previously unknown or unsuspected relationships that can be employed as predictors of future actions."

"Undiscovered public knowledge addresses bodies of information that are similar but distinct or not normally connected. An example of this is to be found in the steel- and glass-making processes. Both require raw material to be liquefied and purified through a carefully controlled high-temperature process, then poured, formed, and cooled to create the finished product. Both processes are based on years of experiments. However, the evaluation of steel-making process control by someone in glass-making process control provides additional information that is not available from the glass industry."

Trybula (1997) provides an excellent review of the areas of undiscovered public knowledge, data mining, and knowledge discovery.

How Can One Conduct Systematic Inquiry?

Having considered some alternatives to systematic inquiry, we now turn to a closer examination of the prosaic business of careful investigation of a problem. An off-the-beaten-track approach to describing systematic inquiry is offered by Robert Pirsig in a best-selling book from the 1970s, Zen and the Art of Motorcycle Maintenance (Pirsig 1974). This book is about much more than systematic inquiry, but within its Chapter 9, Pirsig provides a delightful overview of how to inquire systematically into almost anything. Using the example of trying to make a motorcycle run better, Pirsig says that in trying to solve problems,

"Two kinds of logic are used, inductive and deductive. Inductive inferences start with observations of the machine and arrive at general conclusions For example, if the cycle goes over a bump and the engine misfires, and then goes over another bump and the engine misfires, and then goes over another bump and the engine misfires, and then goes over a long smooth stretch of road and there is no misfiring, and then goes over a fourth bump and the engine misfires again, one can logically conclude that the misfiring is caused by the bumps. That is induction: reasoning from particular experiences to general truths.

"Deductive inferences do the reverse. They start with general knowledge and predict a specific observation. For example, if, from reading the hierarchy of facts about the machine, the mechanic knows the horn of the cycle is powered exclusively by electricity from the battery, then he can logically infer that if the battery is dead the horn will not work. That is deduction.

"Solution of problems too complicated for common sense to solve is achieved by long strings of mixed inductive and deductive inferences that weave back and forth between the observed machine and the mental hierarchy of the machine found in the manuals. The correct program for this interweaving is formalized as scientific method."

Scientific method is simply one of the names for careful problem solving, for careful investigation into reality, for systematic inquiry. Pirsig continues:

"Actually I've never seen a cycle-maintenance problem complex enough really to require full-scale formal scientific method. Repair problems are not that hard. When I think of formal scientific method an image sometimes comes to mind of an enormous juggernaut, a huge bulldozer--slow, tedious, lumbering, laborious, but invincible. It takes twice as long, five times as long, maybe a dozen times as long as informal mechanic's techniques, but you know in the end you're going to get it. There's no fault isolation problem in motorcycle maintenance that can stand up to it. When you've hit a really tough one, tried everything, racked your brain and nothing works, and you know that this time Nature has really decided to be difficult, you say, 'Okay, Nature, that's the end of the nice guy,' and you crank up the formal scientific method.

"For this you keep a lab notebook. Everything gets written down, formally, so that you know at all times where you are, where you've been, where you're going and where you want to get. In scientific work and electronics technology this is necessary because otherwise the problems get so complex you get lost in them and confused and forget what you know and what you don't know and have to give up. In cycle maintenance things are not that involved, but when confusion starts it's a good idea to hold it down by making everything formal and exact. Sometimes just the act of writing down the problems straightens out your head as to what they really are."

It is indeed impossible to overstate the benefits of writing down problems. For clarifying issues, there is nothing like trying to put one's thoughts into words to be conveyed to others or to one's self a day or a week hence. Talking about a problem with someone else also helps to solve the problem because it can lead to the synergistic effect of ideas being generated through the exchanges back and forth between you and your friend(s) or colleague(s). Writing a problem out on paper is the next best thing to talking it over with other people. Pirsig continues:

"The logical statements entered into the notebook are broken down into six categories: (1) statement of the problem, (2) hypotheses as to the cause of the problem, (3) experiments designed to test each hypothesis, (4) predicted results of the experiments, (5) observed results of the experiments and (6) conclusions from the results of the experiments. This is not different from the formal arrangement of many college and high-school lab notebooks but the purpose here is no longer just busywork. The purpose now is precise guidance of thoughts that will fail if they are not accurate.

"The real purpose of scientific method is to make sure Nature hasn't misled you into thinking you know something you don't actually know. There's not a mechanic or scientist or technician alive who hasn't suffered from that one so much that he's not instinctively on guard. That's the main reason why so much scientific and mechanical information sounds so dull and so cautious. If you get careless or go romanticizing scientific information, giving it a flourish here and there, Nature will soon make a complete fool out of you. It does it often enough anyway even when you don't give it opportunities. One must be finely careful and rigidly logical when dealing with Nature: one logical slip and an entire scientific edifice comes tumbling down. One false deduction about the machine and you can get hung up indefinitely."

In the preceding paragraph Pirsig strikes at the heart of the difficulty: how to try to avoid being misled by Nature into thinking you understand something that in fact you do not understand, at least not fully. It is, unfortunately, sometimes easy to be misled by Nature. Albert Einstein, the great mathematical physicist, expressed this difficulty in a way related by one of his many biographers, Abraham Pais, who wrote (Pais, 1982) that Einstein

"lived by a deep faith . . . that there are laws of Nature to be discovered. His lifelong pursuit was to discover them. His realism and his optimism are illuminated by his remark: 'Subtle is the Lord, but malicious He is not' ('Raffiniert ist der Herrgott aber boshaft ist er nicht.'). When asked by a colleague what he meant by that, he replied: 'Nature hides her secret because of her essential loftiness, but not by means of ruse' ('Die Natur verbirgt ihr Geheimnis durch die Erhabenheit ihres Wesens, aber nicht durch List.')."

One can add that although Nature may not be maliciously deceitful, its subtlety can be exceedingly difficult to penetrate. As a general guide to how avoid being misled, Pirsig comments:

"In Part One of formal scientific method, which is the statement of the problem, the main skill is in stating absolutely no more than you are positive you know. It is much better to enter a statement 'Solve Problem: Why doesn't cycle work?' which sounds dumb but is correct, than it is to enter a statement 'Solve Problem: What is wrong with the electrical system?' when you don't absolutely know the trouble is in the electrical system. What you should state is 'Solve Problem: What is wrong with cycle?' and then state as the first entry of Part Two: 'Hypothesis Number One: The trouble is in the electrical system.' You think of as many hypotheses as you can, then you design experiments to test them to see which are true and which are false.

"This careful approach to the beginning questions keeps you from taking a major wrong turn which might cause you weeks of extra work or can even hang you up completely. Scientific questions often have a surface appearance of dumb for this reason. They are asked in order to prevent dumb mistakes later on.

"Part Three, that part of formal scientific method called experimentation, is sometimes thought of by romantics as all of science itself because that's the only part with much visual surface. They see lots of test tubes and bizarre equipment and people running around making discoveries. They do not see the experiment as part of a larger intellectual process and so they often confuse experiments with demonstrations, which look the same. A man conducting a gee-whiz science show with fifty thousand dollars' worth of Frankenstein equipment is not doing anything scientific if he knows beforehand what the results of his efforts are going to be. A motorcycle mechanic, on the other hand, who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypothesis by putting the question to nature. The TV scientist who mutters sadly, 'The experiment is a failure; we have failed to achieve what we had hoped for,' is suffering mainly from a bad scriptwriter. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don't prove anything one way or another."

Another way of helping yourself avoid being misled, or misleading others, is brought out in a comment by Richard Feynman—a physicist who was one of the most extraordinary geniuses of the 20th century (see Endnote 1)—on a further aspect of the scientific method. In a 1974 talk entitled "Cargo Cult Science," Feynman (1986) said:

I mentioned . . . examples of what I would like to call cargo cult science. In the South Seas there is a cargo cult of people. During [World War II] they saw airplanes land with lots of good materials, and they want the same thing to happen now. So they've arranged to make things like runways, to put fires along the sides of the runways, to make a wooden hut for a man to sit in, with two wooden pieces on his head like headphones and bars of bamboo sticking out like antennas—he's the controller—and they wait for the airplanes to land. They're doing everything right. The form is perfect. It looks exactly the way it looked before. But it doesn't work. No airplanes land. So I call these things cargo cult science, because they follow all the apparent precepts and forms of scientific investigation, but they're missing something essential, because the planes don't land.

Now it behooves me, of course, to tell you what they're missing. But it would be just about as difficult to explain to the South Sea islanders how they have to arrange things so that they get some wealth in their system. It is not something simple like telling them how to improve the shapes of the earphones. But there is one feature I notice that is generally missing in cargo cult science. That is the idea that we all hope you have learned in studying
science in school—we never say explicitly what this is, but just hope that you catch on by all the examples of scientific investigation. It is interesting, therefore, to bring it out now and speak of it explicitly. It's a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty—a kind of leaning over backwards. For example, if you're doing an experiment, you should report everything that you think might make it invalid—not only what you think is right about it: other causes that could possibly explain your results; and things you thought of that you've eliminated by some other experiment, and how they worked—to make sure the other fellow can tell they have been eliminated.

Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can - if you know anything at all wrong, or possibly wrong - to explain it. . . . In summary, the idea is to give all of the information to help others to judge the value of your contribution; not just the information that leads to
judgment in one particular direction or another. (pp. 310-312)

Does the Knowledge Produced by Systematic Inquiry Destroy Beauty?

Occasionally you will hear people object to the idea of finding out more about something because they fear that increased knowledge of the thing will somehow destroy its beauty. Richard Feynman (1999) countered this fear as follows:

I have a friend who's an artist, and he's sometimes taken a view which I don't agree with very well. He'll hold up a flower and say, "Look how beautiful it is," and I'll agree, I think. And he says--"You see, I as an artist can see how beautiful this is, but you as a scientist, [you] take this all apart and it becomes a dull thing." And I think that he's kind of nutty. First of all, the beauty that he sees is available to other people and to me, too, I believe, although I might not be quite as refined aesthetically as he is; but I can appreciate the beauty of a flower. At the same time I see much more about the flower than he sees. I can imagine the cells in there, the complicated actions inside which also have a beauty. I mean it's not just beauty at this dimension of one centimeter, there is also beauty at a smaller dimension, the inner structure. Also the processes, the fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting--it means that insects can see the color. It adds a question: Does this aesthetic sense also exist in the lower forms? Why is it aesthetic? All kinds of interesting questions which show that a science knowledge only adds to the excitement and mystery and awe of a flower. It only adds; I don't understand how it subtracts.

Systematic Inquiry in Library and Information Science

We have seen that an important way of developing new knowledge is scientific method, which we have earlier equated with careful problem solving, i.e., with systematic inquiry. The need to solve problems is pervasive in life (and not just in human life), and it is part of the job for professionals in any field.

Does problem solving always produce new knowledge? Phrased that way, the question answers itself: Yes. The new knowledge may be of merely local and/or immediate value, or it may be of wider and/or longer-term value, or it may be both: i.e., it may seem merely local and immediate but turn out later on to have implications and effects far beyond those recognized at its inception.

What kinds of problem-solving go on in library and information science? Obviously, many different kinds, from dealing with mundane administrative difficulties, to planning large-scale building projects, to assessing users' attitudes toward library services, to chemical analyses of paper; and many more. But as we consider the range of problems to be solved, a major aspect of LIS repeatedly asserts itself: the fact that the science and the practice of librarianship and information services deals with the interactions of people and the intellectual products of people--books, pictures, maps, audio images, etc. These interactions are highly complex.

Complexity and Statistics

Late in the 19th century the burst of progress known as the Industrial Revolution exploded into full flower in Europe and North America. A major consequence of the Industrial Revolution was the rapid development of complexity in manufacturing, transportation, finance, government, and other human activities--complexity far beyond anything previously experienced in human affairs. A moment's reflection on what was involved in such activities as creating a nationwide railroad system, developing large-scale manufacturing in such industries as steel, automobiles, and chemical plants, and electrifying cities will suggest to you the rapid increase in complexity that resulted.

This complexity depended on, and stimulated, the development of new tools for dealing with complexity. Among these tools, two major areas were:

• Rapid communications, made possible by the electricity-based techniques of telegraphy, telephony, and radio. It will help you to appreciate the historical context to know that telegraphy had a lengthy period of development, beginning around 1800 but was made into a demonstrably practical system by Samuel F. B. Morse in 1846. Telephony was invented by Alexander Graham Bell in 1876. The invention of radio had three leading pioneers: Heinrich Hertz demonstrated that electromagnetic waves traveled through space in 1887; and Nikola Tesla and Guglielmo Marconi developed practical systems of radio transmission and reception during the 1890s, culminating in Marconi's successful transmission of radio signals between England and Newfoundland in 1901, which proved the value of radio.
  
  
• Statistics, which began its present importance with the invention of correlation in 1885 by Francis Galton, with refinements contributed by Karl Pearson in the 1890s. Other statistical techniques were developed in rapid succession, leading in particular to the notable invention in the 1920s, by Ronald Fisher, of the powerful tool known as analysis of variance. (You will learn more about analysis of variance, or ANOVA, in GSLIS courses on research. As to the importance of ANOVA, one example should suffice: without the improvements in agriculture made possible during the 20th century through the use of ANOVA, the world would already have become unable to feed itself.)

Statistics and LIS

What is statistics? It is fair to say that statistics is one of the most important tools--perhaps the single most important tool--that we have for dealing with complexity, including the complexities of library and information science. A definition supporting this assertion is the following: “Statistics is a method of decision making in the face of uncertainty, on the basis of numerical data, and at calculated risks” (Chou, 1969). This definition encapsulates four important aspects of the use of statistics as a tool for dealing with complexity.

First, the use of statistics enables people to make better decisions than they would be able to make without the aid of statistics. The people who are thus helped include not just scientists engaged in "ivory tower" research but ordinary people who want to make better decisions in their jobs and in their personal lives.

Second, statistics is especially designed to enable people to make decisions despite the existence of considerable uncertainty in the real world. Statistics, admittedly, cannot remove all uncertainty but it can often reduce some of the uncertainty.

Third, the fact that statistics works on the basis of numerical data provides an incentive to gather such data via observations relevant to a problem and to make these observations in ways that are sufficiently objective to be expressed in the form of numbers (rather than, for example, merely subjective assessments such as "better" or "nicer" or "prettier.") Once such observations have been gathered, statistics provides sophisticated tools for interpreting the observations.

Fourth, after observations have been gathered and interpreted, statistics provides a solid basis for assessing just how much risk remains of making incorrect decisions on the basis of the observations. It often makes people uncomfortable to be told, as part of a statistical interpretation, that there is, say, a 5% chance that the interpretation is incorrect. Such feelings of discomfort are understandable; but surely it is better to know that you have a 5% chance of being wrong in your decision than to have no idea whatsoever of the chance that your decision is wrong, and to have to wonder whether there is a 50% (or even higher) chance of your being wrong.

An economist and a statistician, jointly writing an excellent introduction to the practice of problem solving through the aid of statistics, put it this way (Wallis and Roberts, 1956):

"The purposes for which statistical data are collected can be grouped into two broad categories, which may be described as practical action and scientific knowledge. Practical action here includes not only such actions by administrators as setting a bus schedule or admitting a student to school, but also such acts by individuals as having the oil changed in a car or carrying an umbrella. Scientific knowledge here includes not only knowledge gained by scientists through research, such as experiments with serums to relieve colds or analyses of business cycles, but also conclusions by an individual on such questions as whether coffee keeps him awake or whether his colds recur at regular intervals.

"These two purposes, practical action and scientific knowledge, are by no means sharply distinct, since knowledge becomes the basis for action. . . . Statistics is . . . a body of methods for obtaining knowledge."

Because of the importance of statistics as a tool for systematic inquiry (i.e., problem solving) in library and information science, this discussion of systematic inquiry is accompanied by some further materials for you to read about statistics and a set of statistical exercises for you to carry out using Microsoft Excel.

Conclusion

Solving problems is something that you will do repeatedly in your career in library and information science (or, indeed, in whatever professional career you may find yourself pursuing in the future). As we noted earlier,you can expect the problems to range from the minor and immediate to the profound, but here is another aspect of problem solving: It can be fun.

Again it is worth noting what Albert Einstein had to say. He spent his life tackling deeply profound problems, and spoke thus about the rewards of investigating problems:

"The most beautiful thing that we can experience is the mysterious; it is the only source of true art and science; and they to whom this emotion is a stranger, they who can no longer pause in wonder or stand rapt in awe--they're already half dead; their eyes are shut." (Translated by John Archibald Wheeler (see Endnote 2))

References

Chou, Ya-Lun. (1969). Statistical Analysis with Business and Economic Applications. New York, NY: Holt, Rinehart and Winston.

Encyclopedia Britannica. (2001). Louis Pasteur. Retrieved 2001 May 30 from the World-Wide Web: http://www.britannica.com/eb/article?eu=114943&tocid=0

Feynman, Richard P. (1985). "Surely You're Joking , Mr. Feynman!": Adventures of a Curious Character. New York, NY: Bantam Books. The talk is available on the World-Wide Web as "Cargo Cult Science" at http://pc65.frontier.osrhe.edu/hs/science/feynman.htm

Feynman, Richard P. (1999). The Pleasure of Finding Things Out. Cambridge, MA: Perseus.

Gleick, James. (1992). Genius: The Life and Science of Richard Feynman. New York, NY: Pantheon.

Hoffman, Roald. (2001). Serendipity, A Graceful Word. Retrieved 2001 May 30 from the World-Wide Web:
http://heart-to-heart.hobby.ru/serendipity_graceful_wor.html

Pais, Abraham. (1982). 'Subtle is the Lord . . . ': The Science and the Life of Albert Einstein. Oxford, UK: Oxford University Press.

Pirsig, Robert M. (1974). Zen and the Art of Motorcycle Maintenance. New York, NY: Bantam; 1984. ISBN:0-553-27747-2. [First published in 1974, this book is impossible to describe concisely. Even the author says "it should in no way be associated with . . . factual information relating to orthodox Zen Buddhist practice. It's not very factual on motorcycles, either." The book must be read to be appreciated; and I urge you to read it, for I know you will not only enjoy it but also learn much from it.]

Swanson, Don R. (1986). "Undiscovered Public Knowledge," Library Quarterly 56(2):103-118. [See also: Swanson, Don R. (1987). "Two Medical Literatures that are Logically but not Bibliographically Connected." Journal of the American Society for Information Science 38(4):228-233.]

Swanson, Don R., and Smalheiser, Neil R. (1999). "Implicit Text Linkages between Medline Records; Using Arrowsmith as an Aid to Scientific Discovery," Library Trends 48(1):48-59. [Also available from the World-Wide Web at: http://kiwi.uchicago.edu/libtrends.html]

Trybula, Walter. (1997). "Data Mining and Knowledge Discovery." In: Williams, Martha, ed. Annual Review of Information Science and Technology (ARIST), Vol. 32. Medford, NJ: Learned Information. ISBN:1-57387-047-1. [A background note: Walt Trybula earned his Ph.D. in Library and Information Science from UT-Austin in 2000.)

Wallis, W. Allen, and Roberts, Harry V. (1956). Statistics: A New Approach. Glencoe, IL: Free Press.

Endnotes

1. Many people who knew Richard Feynman, or just read his papers or heard his talks, regarded him as not merely a genius but an extraordinary genius. Here is a comment by Mark Kac, a mathematician quoted by James Gleick (1992):

There are two kinds of geniuses, the "ordinary" and the "magicians." An ordinary genius is a fellow that you and I would be just as good as, if we were only many times better. There is no mystery as to how his mind works. Once we understand what they have done, we feel certain that we, too, could have done it. It is different with the magicians. . . . [T]he working of their minds is for all intents and purposes incomprehensible. Even after we understand what they have done, the process by which they have done it is completely dark. . . . Richard Feynman is a magician of the highest caliber. (pp. 10-11)

2. The translation from Albert Einstein was provided by Dr. John Archibald Wheeler as a personal communication. Dr. Wheeler is a Professor Emeritus of Physics at both Princeton University and UT-Austin. Often called the "dean of American physicists," he was a personal friend of Albert Einstein. He has written an autobiographical memoir that provides a fascinating history of American and worldwide physics during the last 70 years (Wheeler, John Archibald, with Ford, Kenneth. (1998). Geons, Black Holes & Quantum Foam. New York, NY: W. W. Norton.) For students of LIS it is also interesting to note that Dr. Wheeler is a son of Joseph Lewis Wheeler, who served as the Director of the Reuben McMillan Free Library in Youngstown, Ohio, during 1915-1926 and the Director of the Enoch Pratt Free Library in Baltimore, Maryland, during 1926-1945. Because of his excellence as a manager and his many innovations in public-library service, Joseph Wheeler has been termed the leading figure in American public libraries in the first half of the 20th century. At the GSLIS Graduation Convocation, 1984 May 19, Dr. J. A. Wheeler talked to the graduates about his father's work in an inspiring address entitled "Selling Library Service."