Data, Instruments, and Theory: A Dialectical Approach to Understanding Science

by Robert John Ackermann

Overview

Robert John Ackermann deals decisively with the problem of relativism that has plagued post-empiricist philosophy of science. Recognizing that theory and data are mediated by data domains (bordered data sets produced by scientific instruments), he argues that the use of instruments breaks the dependency of observation on theory and thus creates a reasoned basis for scientific objectivity.

Originally published in 1985.

The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Product Details

ISBN-13:	9780691611884
Publisher:	Princeton University Press
Publication date:	07/14/2014
Series:	Princeton Legacy Library , #31
Pages:	230
Product dimensions:	6.00(w) x 9.20(h) x 0.70(d)

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Data, Instruments, and Theory

A Dialectical Approach to Understanding Science

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By Robert John Ackermann

PRINCETON UNIVERSITY PRESS

Copyright © 1985 Princeton University Press
All rights reserved.
ISBN: 978-0-691-07296-8

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CHAPTER 1

LOGIC AND SCIENCE

Epistemology and Science

Epistemologists can begin the task of analyzing knowledge from a variety of viewpoints. At a very abstract level, an epistemologist may attempt to prove the mere possibility of knowledge in an effort to confront skepticism. This effort founders on the problem that the path of proof is not clear unless at least some matters are assumed to be known and settled. Skepticism of a sufficiently brute variety is thus difficult to dislodge by a frontal assault that does not assume as known some of the matters that a resourceful skeptic will want to keep in epistemological abeyance. Perhaps because of this, epistemologists with more to do than engage skepticism philosophically have frequently started with examples of knowledge, and have then attempted to outline the total scope of human knowledge by close scrutiny of these paradigmatic cases. A variety of examples has served in philosophical history — mathematical examples, theological examples, introspective examples, and, in modern times, scientific examples. For many modern epistemologists, epistemology is consequently the anatomy of science. Such epistemologists take scientific knowledge, or at least examples of it, to be paradigmatic cases of human knowledge. The philosophical task is then to analzye these cases to mark out the epistemologically legitimate scope of scientific knowledge.

To see more clearly what is at stake in the approach of epistemology through examples, we can turn to Plato. Plato appropriately chose mathematical examples as his paradigms of knowledge, and then sought to find the limits of the human knowledge that could be construed as similar in nature to the mathematical knowledge that he started with. There is no reason why human knowledge should be uniformly subject to the same philosophical analysis, but Plato proceeded as though an analysis of knowledge should be the same for all types of knowledge, and most philosophers have followed him in this. Philosophers of modern science often assume in a similar vein that all genuine human knowledge must be similar to scientific knowledge. Although Plato took mathematical examples as his starting point, the wisdom of his selection was confirmed by his analysis of these examples. Plato believed that genuine knowledge must be timeless and not subject to later refutation. Mathematical knowledge, as he analyzed it, was about unchanging forms or ideas, whose permanence ensured these properties. Plato then proceeded to look for knowledge in such diverse areas as ethics, aesthetics, and political theory, and he proceeded by attempting to locate forms or ideas in these areas that were similar in nature to mathematical forms.

The philosophy of science is heir to the tradition of epistemology based on examples. Philosophers of science have assumed that at least some examples of scientific knowledge are genuine examples of human knowledge, and that the way to proceed is to analyze the implications of these examples for the total range of human knowledge. This much will not be contested here as a reasonable procedure. Other aspects of the legacy are more troubling. If Plato was correct that mathematical knowledge once gained could not be refuted, is scientific knowledge to be measured against the same standard? There is the awkward fact that much of what scientists believe at any point is later found to be modified or abandoned, although the assumption that scientific knowledge continuously progresses gives hope that there is an accumulating core of scientific experimental fact that will explain this as progress.

Considered at a point in time, a scientist will be acquainted with certain supposed facts and theories, and may intend to work with these materials in order to extend scientific knowledge. An epistemology should discuss the procedure of obtaining and extending knowledge. For this purpose, the concept of rationality plays a central role in philosophical epistemology. The rational agent (or scientist) is said to find and extend knowledge by examining these materials and drawing inferences from them through the use of reason. The rational agent will respond to new facts as they are acquired, and will change the shape of his or her relevant beliefs about the significance of known facts and theories in the light of new facts only on the basis of some coherent strategy. Various facts and theoretical conjectures may be in one's personal belief structure at any time, from which basis reason will project new anticipated data, while also looking for the best possible explanations and justifications for the shape and nature of the acquired basis. Philosophy may also expect of rationality that reason will perform a policeman's role, checking that the basis be logically consistent before projection of new data. Since a person might be rational in this technical sense, but be intuitively irrational because only an excessively narrow, eccentric, and personally tailored basis had been sought, rationality may also call for the basis to be as wide as possible in terms of contained fact, and to be anchored firmly in the real world.

For all of the initial plausibility of this variant of rationality, it has some serious problems. An important consequence of this concept of rationality, even when it is fully spelled out in some particular variant, is that it is not sufficiently clear to entail the actions of a rational scientist in many of the situations that arise in scientific practice. The irritating and apparently insurmountable results of undecidability and incompleteness of modern logic suggest that interesting bases for scientific practice may not be provably consistent, and logic can press for revision only after the actual crime of inconsistency has been observed. There are in addition to these matters many problems with no known mathematical or logical solution. A salesperson with a list of towns to visit who wishes to visit each town once only on a projected sales trip, and is subject to various other constraints, cannot in general reason to an optimal route, but must proceed by instinct, or trial and error. In addition to all of these problems, the consequences of much of the work that has been done on inductive inference suggest that logic cannot coerce toward a single possibility the direction in which projection from a basis to anticipated future data should take place. None of these problems begins to deal with empirical uncertainty in the data, or other difficulties with the supposition that a good basis for scientific practice can be provided, but they are sufficient to show why the normative thrust of philosophies of science based on rationality is blunted by confrontation with actual scientific practice.

Similar objections stand in the path of any attempt to utilize game theoretic rationality as definitive of sound scientific practice. Where the value of a set of alternative actions can be expressed in terms of utilities, and a probability measure for the likelihood of obtaining these values on the given actions is available, rationality seems to compel the view that that end should be sought promising the highest expected utility on these measures, or one of those ends with the highest expected utility if there are more than one. This view relaxes the concept of philosophical rationality, since different probability measures can be defended by different scientists, leading to the conclusion that alternative strategies on some basis may be equally reasonable. Hope then arises that the formal philosophy of science might be brought into closer conformity with the observed practice of science through a cooperative game theoretic analysis. But problems again mount as the details are considered. Game theory is of great value and can lead to deep explanatory insights when utilities and their attainment are related in a clear way, as in many standard gambling situations. In science, as Levi and others have shown, to apply game theoretic analyses requires some notion of epistemic utilities and risks that are difficult to quantify as probabilities. Where there are difficulties in quantification, as Newcomb's puzzle indicates, pairs of seemingly obvious maximal strategies may come into conflict even when the quantification is not at issue. Perhaps game theoretic analyses of scientific behavior will be insightful in limited cases, where the possible outcome of a range of experiments is pretty well settled by past experience, or where a group of scientists share a sufficient range of opinion to make the probabilities and properties of outcomes of various options a matter of agreement. Even in such cases, however, game theoretic analyses would reveal similarities between scientific practice and other game strategies, and although such similarities might be illuminating of the actions of scientists in certain situations, they could not by themselves give an insight into the distinctive nature of scientific knowledge, one of the presumed objects of philosophical analysis.

All of these attempted analyses of rationality have in common the Cartesian assumption that the isolated scientist dealing with a basis for scientific practice by means of reason is the appropriate basic framework for understanding the nature and scope of scientific knowledge. Philosophers may slide into this assumption by taking philosophical reflection as a model for scientific thought, or by thinking in terms of Cartesian epistemology. Its consequence, however, is a conception of scientific activity in which scientists are viewed as a group of individuals all more or less trying to do the same thing, with variance explained by the fact that some of them do it faster, or do it better, than the rest. On the Cartesian assumption that the individual scientist is the appropriate locus of philosophical reflection into the nature and scope of scientific practice, the only significance of having groups of scientists involved in scientific practice can be that the practice is speeded up, accelerating the rate of scientific progress. This is implicit in Popper's metaphor that the community of scientists can be compared to masons working on a cathedral. Here the masons are conceived of as more or less interchangeable in function, and the consequence of more or fewer masons seems simply that a greater or lesser number of stones will be laid in a fixed period of time. It is the first intention of this text to challenge this natural philosophical starting point. Our considerations in later chapters will lead us to argue that the social structure of science requires for maximal advance of knowledge that the rational scientist react not only to the known theories and evidence but also to what other scientists are doing, and can do, in connection with possible research topics. Because of this, philosophical epistemologies that attempt to isolate scientific rationality by using merely the theory and data available to a scientist at a time, and logical functions over this basis, cannot achieve a satisfactory epistemology for science.

In line with prevalent philosophical practice, we have taken a basis for an analysis of scientific practice to be a personal belief structure containing facts and theoretical conjectures. Refining this conception of a basis, we can note that various philosophers have taken differing positions on the kind of facts that such a basis should contain, and on the nature of permitted theoretical conjectures over such a basis. It is also possible for philosophers to conceive of the faculty of reason used to test and enlarge the basis in different ways. The resulting complexity of possible positions is enormous, but we shall make a first division of epistemologies constructed on personal belief structures into two traditions, the empiricist and rationalist traditions.

Both the rationalist and empiricist traditions depend on a presupposition that a single person, isolated from his or her fellows, could in principle encounter the real world and reason about it in terms of ideas. Empiricists emphasize the reliability of careful sense experience as the foundation and evaluation of scientific knowledge. Empiricism as a philosophy of science has attempted to analyze scientific knowledge as the logical organization and augmentation of scientific observations. An empiricist who believes that the relevant logical organization is capable of expression within modern symbolic logic is usually a positivist as well. By contrast, one could define rationalism as the view that knowledge about the world is the development of what, in some sense, we already know in the form of clear, distinct, and mutually consistent ideas present to our consciousness. Rationalists emphasize the power of reasoned theorizing in science, and make theoretical coherence the foundation of scientific knowledge and the valuator of scientific data.

Empiricists tend not to find necessary connections between sense experiences. One experience might always be imagined as followed by almost any other experience so far as the logical analysis of experience is concerned. Logic has no empirical content, but merely allows sense experiences to be organized in a useful way. Such organization can never lead to a substantive extension of what has been observed except in the form of a hypothesis or conjecture. For rationalists, the fixed connections between ideas that are discovered by reason may be discovered in the world that is accessible to experience, but only after the connections have been thought. These connections can be recognized in the world, but they cannot be first noticed in the confusion of experience. The precise relationships of ideas are never precisely equivalent to the loose and indeterminate relationships of data.

Empiricism and rationalism have been discussed as philosophical temptations to ground scientific knowledge, and by extension all of human knowledge, in either experience or ideas. Scientists have been subject to the same conflicting temptations. Many scientists, notably Newton and many later Newtonians, have wanted the arbiter of science to be experience, and have held that mathematics and theorizing cannot discover knowledge, although theory may anticipate knowledge where it manages to accurately mirror empirical data. Empiricism is the natural ally of all experimentalists who feel that scientific knowledge can only properly advance on the basis of careful, repeated experimental results. For these scientists, some mathematical systems will be helpful in organizing data and expressing conjectures, but such systems could not be provably insightful into reality apart from their shaping in experience. Experience has to perform the separation between what is useful and what is not useful. Other scientists have seen experience as a snare, partly because of errors of perception and measurement, and have seen experience as little more than a check on whether the precise assumptions of theory point in the right direction. For these scientists, reality must have the precision and beauty of a mathematical development if it is to be understood, and hence mathematical system must be the primary instrument for insight into natural process. Only mathematical systems can transcend the limitations of human perception. Rationalism is the natural ally of all scientists who see theorizing as the primary motor of scientific advance. Scientists have generally spent little time on the philosophical articulation of these themes, but it should not be imagined that the incapacities and problems of empiricism and rationalism are totally isolated from tendencies in the practice of scientists.

Empiricism and rationalism, however, have joint difficulties that cannot be traced to the clash between their intuitions about the relative importance of theory and data. Neither empiricism nor rationalism can satisfactorily explain sufficiently rapid theoretical changes in the sciences. Both have historically seen the object of knowledge as unaffected by the process of acquiring knowledge. After the knower studies the object of knowledge, it remains unaffected, and the knower changes primarily by adding information about the object to the stock of his scientific basis. These epistemologies would be quite serviceable if the world were stable, and if the main features of the world could be read by human sense organs and human reason. There is a sense in which these epistemologies were defensible before the accelerated growth of modern science and the confusion of data produced by modern scientific instruments. If the properties of the world as revealed in experiment change sufficiently rapidly, then the relatively static traditional epistemologies will not prove adequate to science, since the fit between language and data on which they depend will be constantly disrupted by newer data produced by a rapidly changing instrumentarium. It will be argued here that the features of the world revealed to experiment cannot be philosophically proven to be revealing of the world's real properties, but that experiment produces a text of data that must be interpreted, and whose augmentation may not seem initially consistent. The complexity of the world revealed by modern science, as well as the failure of epistemological independence between knower and known (and between theory and data), seems to point inevitably toward a newer epistemology if the structure of science is to be captured at a satisfactory level of philosophical analysis.

(Continues...)

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Excerpted from Data, Instruments, and Theory by Robert John Ackermann. Copyright © 1985 Princeton University Press. Excerpted by permission of PRINCETON UNIVERSITY PRESS.
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Table of Contents

• FrontMatter, pg. i
• Contents, pg. vii
• Preface, pg. ix
• 1. Logic and Science, pg. 1
• 2. Social Structure in Science, pg. 35
• 3. Science and Nonscience, pg. 74
• 4. Scientific Facts and Scientific Theories, pg. 112
• Appendix. The Human Sciences, pg. 165
• Notes, pg. 187
• Bibliography, pg. 201
• Index, pg. 215