note: In this page,
"EKS" replaces "X" to fool the filtering programs.
Why should we wonder if scientific
methods are EKS-Rated? The title for this page is borrowed from Stephen
Brush (1974) who asks a serious question in a humorous title, "Should
the History of Science Be Rated EKS?" Why is this a relevant
question? Because, as Brush explains in a subtitle, "The
way scientists behave (according to historians) might not be a good model for
students."
Should our confidence in science be lessened
by the limits of logic and the influence of culture? This question has
sparked heated debates among scholars who hold contrasting views of science.
Since these views seem irreconcilable, it would be futile to aim for a solution
that is acceptable to everyone. Therefore, this page will just discuss
issues and express opinions. I will also make modest recommendations,
based on a simple principle — that if a good idea
(examining interactions between culture and science) is taken to extremes without sufficient balance from rational critical thinking,
there may be undesirable consequences — and an assumption that undesirable consequences should be
avoided.
The SUMMARIES below explain what is in each section,
so you can decide whether to explore it more deeply:
1. Responsibility in Education
2. The Limits of Logic
3. Radical Relativism
4. Do scientists search for truth?
5. Science and Unobservables
These section-summaries are followed by a
Table of Contents that shows
subsections
for the five FULL SECTIONS.
and there are two more pages about the ideas in this page: Tools
for Analysis describes
concepts and techniques for rationally analyzing the cultural-personal
factors that influence science.
Responsibility in Education (Summary
for Section 1)
We should be deeply concerned about our responsibilities
as educators, about the effects that our educational policies will have on students
and society. One way to express this concern is with a thoughtful evaluation
of different ways to teach the nature of science. We should ask, "What
description of science is the most accurate, and most beneficial for students?"
But is the answer to both questions the same, in all educational situations?
Because my model of Integrated Scientific
Method (ISM) claims that "cultural factors" affect the process and
content of science, ISM can be used to express a wide range of views about "culture
in science," including some that may not be accurate or beneficial.
Should this be a cause for concern?
Is "the way scientists behave (according
to historians)" the way scientists really behave? And if they do,
are students better off not knowing? Are any views of science potentially
dangerous? Should any views be eks-rated (unsuitable for young minds)
because they may be harmful for students? Generally I favor a "free
marketplace of ideas" in the classroom, openly discussing a wide range
of perspectives. But if some scholars are advocating views that seem to
cross over the line of rationality and good taste, moving into areas that seem
foolish or dangerous, should educators avoid these views? Or is it better
to discuss them openly, exposing them to the bright light of critical thinking?
These questions are discussed in Section 1.
The Limits of Logic
(Summary for Section 2)
Yes, there are limits. It is impossible,
using any type of logic, to prove that any theory is either true or false.
Why? If observations agree with a theory's predictions, this does not
prove the theory is true, because another theory (maybe even one that has not
yet been invented) might also predict the same observations, and might be a
better explanation. But if there is disagreement between observations
and theory-based predictions, doesn't this prove a theory is false? No,
because the lack of agreement could be due to any of the many elements (only
one of these is the theory being "tested") that are involved in making
the observations and predictions, and in comparing them.
Or the foundation of empirical science can
be attacked by claiming that observations are "theory laden" and therefore
involve circular logic, with theories being used to generate and interpret the
observations that are used to support theories. This circularity makes
the use of observation-based logic unreliable. And when this shaky observational
foundation is extended by inductive generalization, the conclusions become even
more uncertain.
Yes, these skeptical challenges are logically
valid. But a critical thinker should know, not just the limits of logic,
but also the sophisticated methods that scientists have developed to cope with
these limitations and minimize their practical effects. By using these
methods, scientists can develop a rationally justified confidence in their conclusions,
despite the impossibility of proof or disproof.
We should challenge the rationality of an
implication made by skeptics — that if we cannot claim certainty, we can
claim nothing. Modern science has given up the quest for certainty, and
has decided to aim for a high degree of plausibility, for a rational way to
determine "what is a good way to bet."
The question, "Can science cope with
the limits of logic?", is discussed in Section 2.
Radical Relativism
(Summary for Section 3)
An extreme relativist claims that no idea
is more worthy of acceptance than any other idea. Usually, relativism
about science is defended by arguing that, when scientific theories are being
evaluated, observation-based logic is less important than cultural factors.
But if theories are determined mainly by culture, not logic, in a different
culture our scientific theories would be different. And we have relativism.
As with many ideas that seem extreme, radical
relativism begins on solid ground. Most scholars agree with its two basic
premises: the limits of logic and the influence of culture. But there
is plenty of disagreement about balance, about the relative contributions of
logic and culture in science, about how far a good idea can be extended before
it becomes a bad idea that is harmful to rationality and society.
This section ends by asking, "Does scientific
knowledge improve over time?" Although a skeptic may appeal to the
impossibility of proof, "the best way to bet" seems obvious.
To illustrate, we'll imagine a million dollar wager involving a "truth
competition" between scientific theories from the past, present, and future:
from 1503, 2003, and 2103. Would a relativist really be willing to bet
on theories from 500 years ago?
The "relativism" question, "Is
one idea as good as another?", is discussed in Section
3.
Do scientists search for truth? (excerpts
from Section 4)
I haven't yet written
a summary for this section, but here are some quotations from it:
One response to the impossibility
of proof is an instrumentalist perspective, in
which scientific theories are interpreted as making claims for usefulness, but
not for probable truth. instrumentalism and
realism differ in their answer to the question,
"Does science try to find truth?" realism says yes, but instrumentalism
says no. .....
• Section 4A
begins by describing two essential components of my own view, critical
realism:
First, a realist can place a high value on
both plausibility (an estimate of whether a theory
is likely to be true) and utility (an estimate
of whether a theory seems to be useful). ... Compared with an instrumentalist
— who adopts a restrictive view that eliminates one of the two major criteria
(plausibility and usefulness) by excluding a consideration of truth-estimating
plausibility — a realist has a wider vision that looks for both plausibility
and utility.
Second, a critical realist (CR) distinguishes
between goals and claims. A CR is a realist about goals,
and a critic about claims. A CR combines realist
goals (wanting to find the truth) with critical
evaluation (willing to be skeptical about claims for the truth status
of a particular theory). ... For example, it is difficult to deny that scientists
in the early 1950s who studied the structure of DNA were aiming for a theory
that would describe the actual structure of DNA. They wanted to find the
truth, so they were realists. Before 1953, however, their claims were
modest, because all of their theories had a low truth-plausibility. They
were evaluating critically, in an effort to achieve their
realist goals. But after April 1953 the claims for truth became
bold, and those who were most knowledgeable quickly decided that the double
helix structure deserved to have a very high plausibility because it almost
certainly was true.
Do most scientists usually search for truth?
Yes. Of course, searching for truth is not the only goal. Scientists
are also motivated by the intellectual stimulation and satisfaction of solving
problems, and by practical benefits such as obtaining grants, earning salaries,
publishing papers, gaining respect from scientific colleagues and nonscientists,
and developing science-based technologies that will bring practical benefits
like improved health care or new consumer products. Yes, all of these
are motivations, but it's not an either-or choice, and most scientists also
want to construct accurate theories; they want their theories to be true
by corresponding to the reality of what is happening in nature.
But attitudes toward
utility and truth differ in science and design. An engineer whose
main goal is to design an improved product will tend to be more satisfied with
viewing a theory only in terms of its usefulness in promoting progress toward
this goal, without thinking too much about whether the theory is true.
When a theory is viewed as a practical tool whose function is to be useful during
the process of design, the question of truth becomes less important than in
science where accurate understanding is the main goal. ..... {there
is more in 4A}
• Section 4B
describes arguments for and against instrumentalism. 4C asks a silly question — Do Scientists Create Reality? — and
gives a rational answer. { This answer, plus Reality 101 (basic concepts
about distinctions between truth and truth-claims), are in another
page. } 4D describes Six Types
of Status (relative and intrinsic, pursuit and acceptance, truth and utility)
plus two interpretations (realist and instrumentalist) and variable-strength
claims about truth.
The question, "Do scientists search
for truth?", is discussed in Section 4.
Science and Unobservables (Summary for Section 5)
A positivist (an empiricist
*) believes that scientific theories should not postulate the existence of unobservable
entities, actions, or interactions. By contrast, empirically based hypothetico-deductive
logic allows "unobservables" in a theory, if this theory makes
predictions (or retroductions) about observable outcomes. {* empiricist
is not the same as empirical }
Positivism is rare among scientists, who
bristle at the constraints, who cherish their intellectual freedom and welcome
a wide variety of ways to describe and explain. Many modern theories include
unobservable actions and entities — such as thinking (in psychology) or
electrons and electrical force (in chemistry) — among their essential
components.
{ terminology: In the 1830s Auguste
Comte, motivated by anti-religious ideology, invented positivism. In the
early 1900s a philosophy of logical positivism combined positivism with
other ideas. Currently, "positivism" has many meanings;
I use it to mean a "no unobservables" constraint, but it can also
refer to anything connected with logical positivism (logical empiricism), including
the "other ideas" and more. }
The question, "Should scientists think
about unobservables?", is discussed in Section 5.
1. Responsibility in Education
2. Logical Skepticism
2A. Potential Problems and Actual
Problems
2B. Limitations
of Hypothetico-Deductive Logic
2C.
Limitations of Observations
2D. Limitations
of Inductive Logic
2E. A Summary
3. Relativism
3A. Radical
Relativism
3B. Strong
Criticisms of The Strong Program
3C. Two
Analytical Tools for Critical Thinking
3D. Is
there Scientific Progress? A Million Dollar Wager
4. Instrumentalism and Realism
4A. Critical
Realism
4B. Pros
and Cons of Instrumentalism
4C. Do
scientists create reality?
4D. Six
Types of Status
5. Positivism
1. Responsibility in Education
When selecting a description of science to be used for education, we should ask two important questions: What is the most accurate description of science, and what educational approach is most beneficial for students?
ONE
FRAMEWORK, MANY VIEWS.
These questions — asking whether a model of science is accurate and beneficial
— are important if we want to use ISM in education because: 1) ISM
claims that cultural influences [thought styles and cultural-personal factors]
affect the process and content of science; 2) there is a wide range
of views about cultural influence; 3) ISM can be used to express
each of these views.
For example, a teacher
using ISM could claim that although some extremists emphasize the rare cases
where cultural factors exert significant influence on the evaluation of scientific
theories, for most evaluations the effect of cultural factors is minimal.
This teacher could explain how checks-and-balances occur when a scientific community
evaluates claims for knowledge, and how this communal process tends to counteract
individual biases. Or a community could be the source of pressures that
produce bias. This teacher might think that cultural-personal bias is
common, but is not a part of authentic science, so it should be avoided.
This could be the entry point for a discussion about sources of bias, and for
warnings about the dangers — because bias is detrimental to objective
critical thinking, yet is difficult to avoid, tough to detect, and easy to rationalize
— along with practical strategies for detecting bias and minimizing its
effects.
Another teacher might criticize "scientific
objectivity" because it indicates a lack of cultural conscience.
This could lead to an activist stance with appeals for patriotism or populism,
by exhorting students to use science for the benefit of a nation or "the
people."
Or a teacher may prefer the extreme relativism of radical sociologists who propose that a
central characteristic of science is the cultural
activity of "creating objects and facts" in the laboratory.
This activity produces a relationship in which
observations — the supposedly firm foundation for empirical evaluation
— are caused by culture, not by nature. And there is a change in
the balance of criteria used for theory evaluation,
with a dramatic shift toward cultural-personal factors and away from empirical
factors.
Comparing this radical view of science with
a more conventional view, such as my own, we see a sharp contrast. But
both views can be expressed using the framework of ISM. These "alternative
elaborations of ISM" would use the same basic elements — thought
styles and cultural-personal factors — but they would propose different
characteristics, relationships, and balances, and would therefore propose a
different view of science. { The GOALS-page
looks at alternative elaborations, and illustrates by showing how ISM could
be used to describe an orthodox view of science (with external consistency and
empirically constrained retroduction) or the anarchist ideas of Feyerabend (with
external inconsistency and unconstrained counterinduction). }
THE
RESPONSIBILITY OF ISM.
The existence of alternative elaborations is intentional; ISM is designed
to be flexible, so that by varying the characteristics, relationships, and balances
of its components, it can be used to describe a wide range of views about science
and scientists. In my opinion, this flexibility is a strength, but it
is also a cause for concern. If wild ideas are expressed using ISM, should
I feel responsible, despite my disclaimer that "the opinions expressed
using ISM are those of the expresser, and not necessarily those of ISM"?
Maybe. But in a classrooms the teacher (not ISM or any other instructional
tool) makes final decisions about the views that are expressed. ISM can
allow and encourage an accurate description, but cannot guarantee it.
Is ISM intended to change behavior?
Philosophers of science make a distinction between a descriptive
model (that tries to describe science as it actually is done) and a normative
model (that tries to describe science as it should be done). Is
ISM descriptive or normative? With respect to scientists, ISM is descriptive,
with no intention of telling scientists how to do science. But for students,
even though the primary function of ISM is descriptive — to allow a complete,
accurate description of science as it really is — when any model of science
is used in the classroom there will be a normative influence on students.
In fact, promoting a change in student thinking and behavior is the purpose
of education, and is the main reason to include a model of science (like ISM)
in education.
Two areas of ISM, cultural-personal factors
and thought styles, are especially susceptible to being misunderstood and abused.
Both of these elements are a part of science, so I defend their inclusion in
ISM, and a strong case can be made for including them in education. As
usual, however, if this good idea is taken to extremes, with exaggerated interpretations,
the result will be a distorted picture of science that is not an accurate description,
and is not beneficial for students.
WHAT IS BENEFICIAL? It is difficult
to answer the "accurate and beneficial" questions with confidence,
due to legitimate questions about what constitutes an accurate view of science,
and about the effects of what we do in the classroom. For example, will
the enthusiasm of future scientists be dimmed if their role models are tarnished
by portrayals of scientists as politically motivated, status-seeking mercenaries?
Or will some students want to become scientists because they see the socially
interactive aspects of science, and they realize that scientists are real people,
like themselves? Similar questions can be asked about extreme skepticism.
Will students stop doing experiments if they are told that observations are
inevitably biased and unreliable? And will students stop trying to learn
the theories in textbooks if they are told that the justification for these
theories is weak or (with anti-realist interpretations) that science does not claim
to describe the truth, and does not even try to search for truth? Or will
skepticism merely encourage healthy critical thinking? And is there a
danger when science education becomes politicized so that it argues for certain
metaphysical or ideological views? Or might certain types of politicization
be beneficial for students?
By combining an earlier question (Should
the History of Science Be Rated EKS?) with the goals of wanting education
to be accurate and beneficial, we can ask whether "the way scientists behave
(according to historians)" is the way scientists really behave; and
if they do, are students better off not knowing? Pedagogical considerations
of "what is beneficial" should be heavily influenced by, but not totally
determined by, what is regarded as most accurate. The possible effects
on students and society should also be considered. If something is true,
it may seem foolish to ask "Are students better off not knowing?",
but this question is worth asking for young students who are not well equipped
to cope with complex new ideas or to defend their own ideas. An essential
ingredient in the art of teaching is to judge the intellectual sophistication
of students, and then use this awareness to make adjustments so the demands
for thinking and learning will be at an appropriate level.
But should any perspectives really be eks-rated,
in the sense that students should not be exposed to them? In general I
favor a "free marketplace of ideas" approach, with an open-minded
tolerance for a variety of viewpoints. In my opinion, a wide range of
views about science can be discussed in the classroom, with effects on students
that are mostly beneficial, especially if the discussion is done wisely by adjusting
the demands for critical thinking to an appropriate level, as described above.
But a responsible educator should avoid the advocacy of views that "cross
over the line" of good pedagogical taste, moving into areas that are foolish
and even dangerous. In my opinion, some scholars in the "study of
science" community have crossed over this line. Way over. Especially
with views such as radical relativism and "creating
reality." I oppose these views mainly because I think they
are not accurate. But this inaccuracy can also produce effects that are
not beneficial.
A SUMMARY.
In an effort to act wisely, motivated by an awareness of our responsibilities
as educators (or as parents or citizens), we should be deeply concerned with
the effects that our educational policies will have on students and society.
This section began by asking, "What is the most accurate description of
science, and what educational approach is most beneficial for students?"
These questions are worth asking, even though (or because) there are no simple
answers. Instead of seeking a solution that will satisfy everyone, which
is impossible, the goal of our question-asking should be to stimulate a thoughtful
evaluation of the merits of different approaches to teaching the nature of science.
And while we're doing this, we can think about how our evaluations are being
influenced by our individual and collective perspectives on the complex relationships
between models of science, quality of education, and quality of life.
2. The Limits of Logic
The limits of logic are summarized in a principle of underdetermination which states that it is impossible, using any type of logic, to prove that a theory is either true or false.
In a reversal of the usual pattern, I'll begin with my conclusions (in Section 2A) before discussing the skeptical challenges to hypothetico-deduction, observation, and induction.
2A. Potential Problems and Actual Problems
Logical skepticism is based on sound
principles. A critical thinker should be aware of the limitations of observations,
and of logic that is hypothetico-deductive, retroductive, or inductive.
But although some skepticism is good, too much of this good thing — without
sufficient balance by thinking critically about the claims of skeptics —
can be detrimental to science and rationality.
In extreme logical skepticism there is a
tendency to ignore the distinction between potential problems and actual problems,
and to therefore propose "cures for which there is
no adequate disease. (Fodor, 1986)" If extreme skeptics assume
that modern science aims for certainty, they are wrong. In response to
claims that nothing can be proved, most scientists would simply say "So
what?", because instead of asking "What can be proved using formal
logic?" it is more practical for scientists to ask "What is a good
way to bet?" Scientists have developed methods for coping with
the concerns of skeptics, so that in most situations the skeptics' potential
problems do not seem to be significant actual problems for science.
note: For access to references (such
as "Fodor, 1986"), check the end of this page.
2B. Limitations of Hypothetico-Deductive Logic
If observations agree with a theory's
predictions, skeptics correctly point out that this does not prove the theory
is true, because another theory — including one that has not yet been
invented, and maybe never will be invented — might also predict the same
observations, and might be a better explanation. And when a theory is
invented using retroductive logic (which is a variation of H-D logic, subject
to the same limitations) an additional reason for caution is that this theory
is being constructed so it will fit known data, and empirical agreement can
be obtained by ad hoc patchwork.
The "Overview of Scientific Method"
describes one method for coping with this logical difficulty: "A
theory can be false even if its predictions agree with observations, so it is
necessary to supplement this 'agreement logic' with another criterion, the degree
of predictive contrast, by asking "How much contrast exists
between the predictions of this theory and the predictions of plausible alternative
theories?" in an effort to consider the possibility that two
or more theories could make the same correct predictions for this system."
{ a detailed explanation of predictive contrast — and the "So What?"
question — is on the "Details of Scientific
Method" page }
Compared with the impossible
task of proving a theory is true, it is generally considered easier to gather
evidence showing that a theory is inadequate. Popper (1963) emphasizes
the asymmetry between verification and falsification: if a theory predicts
"if T then O, and T occurs" and O also occurs, this does not prove
T is true; but if O does not occur, this proves T is false.
Despite this valid logic, it still is impossible
to logically prove a theory is false, because if there is anomaly for a theory (due to a low degree of agreement
between predictions and observations) the disagreement could be due to any of
the many elements that contribute to the predictions, the observations, and
their comparison. Erroneous predictions could be caused by an inadequate
theory or supplementary theory, or by a characterization of the experimental
system that is inaccurate or incomplete, or by mis-applying theories to construct
a model, or using faulty deductive logic to make a prediction. But perhaps
it is the observations that are not reliable, due to poor experimental design
or sloppy technique; or maybe there was defective equipment, such as an observation
detector that did not function as expected. Or the logic used in comparing
the predictions and observations may be deficient, and this has produced an
estimated degree of agreement that is inappropriately low.
There are many possible causes for anomaly,
and each can be illustrated with examples from the history of science.
A rigorous logical analysis (Duhem, 1906; Quine, 1953) leads to the skeptical
conclusion that anomaly cannot ever be localized to any of these possibilities.
But according to Shapere (1982, p. 516), "What this
shows is that formal logic does not exhaust what counts as reasoning in science."
Scientists are quite willing to use "reasoning that goes beyond formal
logic" to cope with a complex situation and to make educated estimates
— based on their confidence in each factor that affects the predictions,
observations, and comparison — about where the anomaly is likely to be
located.
Another reason for the impossibility of
proof or disproof comes from the statistical nature of some predictions and
observations. For example, Grinnell (1992) discusses the differences in
logic between three theoretical claims: "All X are Y" can be
falsified but not verified; "Some X are Y" can be verified but
not falsified; and "90% of X are Y" cannot be verified or falsified.
Most scientists will agree with these conclusions
about what can and cannot be proved. But skeptics will challenge
the first two claims, which assert that a theory "can be verified"
or "can be falsified." And scientists will challenge the pessimistic
conclusion that the third claim "cannot be verified or falsified"
because a sophisticated statistical analysis of data can lead to a rationally
justified confidence about the truth or falsity of a statistical claim such
as "90% of X are Y." But skeptics will question whether this
confidence is justified.
According to formal logic, a theory can
never be proved true. But sometimes a theory correctly predicts old and
new data for a wide variety of experimental systems, even though the combined
empirical constraints (for all experiments) are so demanding that it seems unlikely
any alternative theory could also satisfy them. This is why, for example,
few scientists doubt the double-helix structure of DNA, despite valid logical
arguments that this theory is underdetermined by the data.
Even though it is logically impossible to
prove that any theory is either true or false, scientists can have a rationally
justified confidence that a particular theory is true, or at least approximately
true. Or they may be confident that it is false.
Most scientists will say that an extreme
skeptic is wrong in implying that if science cannot claim certainty, it can
claim nothing. Modern science has given up the quest for epistemological
certainty, and is willing to settle for a high degree of plausibility.
Scientists rarely worry about skeptical challenges such as "Can you be
certain the sun will rise tomorrow?" (argued by Hume), or "How do
you know it isn't all a dream?" (asked by Descartes), or "Can you
prove that scientific theories of today are closer to the truth than theories
of 500 years ago?" (a challenge by extreme relativists). When it
comes to theory evaluation, instead of asking "What can be proved using
formal logic?", it is more practical for scientists to ask "What is
a good way to bet?"
Consistent with the lack of certainty in science, in ISM the concept of status uses a continuum to estimate the degree of confidence in a theory. And the definition (from Giere, 1991) of hypothesis — as a claim that a system and a theory-based model are similar in specified respects and to a specified (or implied) degree of accuracy — allows flexibility in defining what is (and is not) being claimed for a theory. In addition to status and variable-strength hypotheses, other types of status (intrinsic and relative, for pursuit and acceptance, for truth and utility) can be used to modify and thus to more accurately describe the results of evaluation.
2C. Limitations of Observations
For skeptics, another option is to
attack the foundation of empirical science by claiming that observations are
biased and unreliable. Some challenges are described below, along with
the methods {in brackets} that scientists have developed in order to cope with
each potential difficulty.
Why is data collection biased?
During experimental design, scientists
decide what to study and how to study it, and this decision determines the
data that will be collected. { The effects of experimental design
can be analyzed, so these effects can be considered during data interpretation
and during the design of future experiments. More important is the fact
that even though design determines the types of data, nature determines
the data. }
Data will be biased if it is collected
by a human who has expectations for what is worth seeing or what will occur,
or who hopes that certain results will occur. { This is a valid
concern that varies with the situation. In a medical experiment there
will be little concern if an observation arises from reading a digital thermometer.
But if subjective assessments of patients' symptoms are required, scientists
often use a "double blind" experimental design that minimizes errors
due to observational bias at a conscious or unconscious level. }
Why are "theory-laden observations"
a cause for concern?
Circular logic occurs because theories
are used to generate and interpret observations that, in turn, are used to
support theories. { If the theory being evaluated is closely related
to an observation-theory used in an experiment, with overlapping domains and
many shared assumptions and theory components, concerns about circularity
are justified. But if a theory and observation-theory are relatively
independent, there will be minimal circularity. Shapere (1982,
pp. 514-516) discusses logically sophisticated methods for analyzing observation
situations and observation theories, and how scientists use these methods
to check for circularity and reliability. }
Observations depend on theories, and theories
are uncertain, so this lack of reliability transfers to our observations.
{ As discussed above, scientists can have a rationally justified confidence
about the plausibility of theories, including observation-theories concerning
the source, the transmission process, and the receptor for what is being observed
(Shapere, 1982). } { details
}
Theory-based interpretations always occur
during observations. { Yes, but the influence of interpretation
is limited. As noted by Strike (1987), although
in the early 1600s an Aristotelian scientist and Galileo would "see"
a pendulum differently, neither would see a giraffe. }
2E. A Summary
It is logically impossible to prove
a theory is either true or false. Why? Hypothetico-deduction has
logical limitations (because even when a prediction is "if A then B"
and we observe B, this does not prove A) and there can be suspicions about ad
hoc adjustments when (in retroductive inference) a theory is proposed to fit
known data. Other difficulties include biased data collection, circularity
between theories and observation-theories, and the logical limitations of inductive
generalization.
In an effort to cope with their own concerns
about these logical limitations, scientists have developed methods — including
estimates for predictive contrast, and sophisticated techniques for logical
analysis — that encourage them to claim a "rationally justified confidence"
for their scientific conclusions, despite the impossibility of proof or disproof.
3. Radical Relativism
Taken to an extreme, relativism claims that no idea is more well founded, and
deserving of acceptance, than any other idea.
3A. Logic and Culture
Relativism about scientific theories
is usually defended by combining two premises, by claiming that during theory
evaluation: 1) due to the limits of logic (discussed above), observation-based logic
exerts only a weak influence, but 2) a strong influence
is exerted by cultural-personal factors.
If logical input is weak and cultural influence is strong, with ideas determined
mainly by culture, then in a different culture the results of theory evaluations
would be different. { What are cultural-personal
factors? }
While there is a correlation between a heavy
emphasis on cultural factors (in science process)
and relativism (in science content), there is no
necessary link. For example, Hull (1988) thinks that reliable content
can emerge from a chaotic process. So does Bauer (1992), who claims that
during a communal "filtering" process the non-objective behavior of
individuals (or small groups) tends to cancel, thus producing a result that
is more objective than the objectivity of individual scientists. /
In addition to a heavy emphasis on culture, relativism seems to also require
an extreme form of logical skepticism that challenges the credibility (or even
the possibility) of culturally-independent empirical "reality checks"
that might compete with cultural influence.
Or, instead of asking why scholars reach
relativism as a conclusion, perhaps it makes more sense to assume that —
due to cultural factors operating in society and in scholarly communities —
a preference for relativism comes first, followed by the arguments (involving
logic and culture) that are enlisted as support.
In recent decades, radical relativism has
become surprisingly popular among scholars. A catalyst in the rise of
relativism was The Structure of Scientific Revolutions (Kuhn, 1962),
which emphasized the role played by non-logical factors in the revolutionary
overthrow of one paradigmatic "way of thinking" by another.
This book helped inspire a wave of anti-rationalist intellectual activity that
pushed the boundaries of relativism far beyond the original claims of Kuhn.
One group pushing the boundaries, the "strong
program" in the sociology of scientific knowledge, has focused on the ways
in which cultural-personal factors affect the content of science.
This is more controversial than claims about the process of science,
which is generally agreed to be influenced by social factors. Scholars
in the strong program usually adopt a radical relativism, claiming that the
content of scientific theories is influenced more by culture than by nature.
(or at least they claim that we should assume culture is stronger than nature,
when we are studying the process and content of science)
One of the most harmful features of radical sociology is that, in important
ways, it can undermine the conventional view that "a
central aim of education... is the fostering of rationality, or its educational
cognate, critical thinking. (Siegel, 1989, p. 21)"
Slezak and Siegel are not alone in their
distaste for extreme relativism. Their views are shared by many scholars,
including myself and Laudan (1990, p. x) who declares that "The
displacement of the idea that facts and evidence matter by the idea that everything
boils down to subjective interests and perspectives is... the most prominent
and pernicious manifestation of anti-intellectualism in our times."
Therefore, it is disturbing to see large segments of the intellectual community
either approving radical relativism, or not being active in arguing against
it.
Briefly stated, my opinion, based on the principle that without balance "too much of a good thing" can be harmful, is that extreme relativism is the result of taking useful ideas — such as critical thinking, logical skepticism, and an awareness of cultural-personal factors — and stretching them to the point where they not only lose intellectual credibility, but they become dangerous for science and society.
3C. Two Analytical Tools for Critical Thinking
When evaluating extremist interpretations
of science, it helps to have tools that encourage flexible critical thinking
and precise, accurate conclusions. I have developed two useful tools for
analysis: idealizations and range diagrams.
These tools can facilitate a critical examination of the ways that science is
influenced by cultural-personal factors, and will help avoid dichotomous generalizations
such as "no cultural influence" or "all cultural influence."
The use of idealizations to study science is based on the principle
that an oversimplified model can be useful for
estimating the effects of a component that has been intentionally omitted from
the model. In this case, cultural-personal influence is studied by trying
to imagine what science (especially as it is exemplified in a specific historical
episode) would be like without this influence, and comparing this idealization
with the actual science.
The second type of analytical tool, range
diagrams, can be used to help determine how accurately a sample represents
a larger population, and in deciding what conclusions can be drawn about a population
based on a small sample of case studies. For example, when studying the
mutual influence between societal politics and science, different conclusions
will result from studying a sociobiologist (this field can be very politicized)
and a benzene chemist (very little societal politics is happening here).
Although each scientist is part of the total science experience, drawing a general
conclusion based on either sample by itself would be misleading.
These tools are useful for recognizing cultural
influence without overemphasizing it. And they help clarify my own views,
to minimize misunderstandings. When I criticize extreme relativism, I
am not claiming that cultural influence is negligible. My model of Integrated
Scientific Method contains cultural-personal factors (such as psychological
motives and practical concerns, metaphysical worldviews, ideological principles,
and opinions of authorities, operating in complex social and institutional contexts)
because these factors play a role in the process of science and (usually to
a lesser extent but not always) in the content of science.
The tool of idealization
is useful for recognizing bias and coping with its effects, as recommended by
the first teacher of Section 1, who is expressing
my basic views. And range diagrams are useful
for avoiding generalizations that oversimplify and distort, for recognizing
that cultural-personal factors play different roles in different areas of science
and in different communities within each area, and exert different influences
on the process of science and on the content of science.
3D. Is there Scientific Progress? A Million Dollar Wager
Although to most of us the answer
is obvious, skeptics can challenge a claim that scientific knowledge improves
over time. The progress in scientific utility is clear. But progress
in truth is impossible to verify, since none of us can be sure we know the truth,
so a skeptic asks "Can you prove it?" My brief answer is "no,
but it's a good way to bet."
For example, consider a million dollar wager.
Imagine that 1000 scientific theories from the year 2003, covering a wide range
of fields, are compared with 1000 corresponding theories from 600 years earlier,
in 1403. You can choose one set of theories, either 1403 or 2003, and
someone who knows the truth about nature — such as an omniscient being
(God?) or an alien from a scientifically advanced civilization — decides
which theory (in each of the 1000 areas) is closer to this truth. If your
theory is more true, you win $1000, but if the other theory is more true you
lose $1000. Should you care which set of theories you get? According
to those who claim that science does not improve with age, it should not matter.
If there is no scientific progress, the 1403-science and 2003-science have an
equal chance of being closer to the truth. In my opinion, anyone who is
not a fool (or who wants to give away a million dollars) should have a rationally
justified confidence, although no proof, that the science of today is a better
way to bet.
For a rough estimate of how superior you
think the theories of 2003 are, consider a wager with two options: you can pay
$600,000 and choose the theories of today, or decide not to play. If you
play, you break even with a 20-80 split between the theories of 1403 and 2003.
I would eagerly pay the entry fee, with confident assurance that I would win
roughly $400,000. Would you take the bet if the fee was changed to $800,000,
so you need a 10-90 split to break even? What do you think the majority
of scientists would do? I think most would take the bet, even if they
had to pay $900,000 for the chance to win $100,000. After all, 1-to-9
odds aren't too shabby when betting on 600 years of scientific progress.
Please notice that I'm not saying all theories
of 2003 are perfect, just that in general they are better than the theories
of 1403. If our theories continue to apparently improve (as they have
in the past), then in a million dollar wager comparing theories of 2003 and
2103, I would bet against our current theories.
4. Do scientists search for truth? (Instrumentalism and Realism)
One response to the impossibility of proof is an instrumentalist perspective, in which scientific theories are interpreted as making claims for usefulness, but not for probable truth. Instrumentalism and realism differ in their answer to the question, "Does science try to find truth?" Realism says yes, but instrumentalism says no.
Section 4A explains my view, Critical
Realism. { realist goals with critical evaluation }
4B describes the
Pros and Cons of Instrumentalism. { some
arguments for and against }
4C asks a silly
question — Do Scientists Create Reality?
— and gives a rational answer.
4D describes Six
Types of Status. { concepts for precise thinking and communicating }
You can read these sections in any order
you want.
4A. Critical Realism
It is difficult to define either realism
or instrumentalism with precision, because in real life there is a range of
realist views, and a range of instrumentalist views. In our efforts to
to decrease inaccurate stereotyping, to more accurately understand and portray
the characteristics of individual views, a useful thinking tool is offered by
Leplin (1984). He describes ten claims that a realist may or may not believe;
by affirming or denying various claims, a variety of realist positions is possible,
ranging from modest to strong. This analysis shows that more is involved
than a simple "modest to strong" continuum, since the short-list of
claims made by one modest realist may differ from the claims of another modest
realist. Instrumentalist positions are similarly variable. Therefore,
when discussing this topic it is important to avoid either-or dichotomies that
lead to distorted oversimplifications. This section describes one type
of realism — critical realism — that offers many practical benefits.
When thinking about critical
realism, two concepts are crucial.
First, a realist can place a high value on
both plausibility (an estimate of whether a theory
is likely to be true) and utility (an estimate
of whether a theory seems to be useful). This is summarized in
my definition of theory status as an estimate of
"a theory's plausibility and/or utility." For a realist, the
relative importance of plausibility and utility can vary from one theory to
another, or even from one application of a theory to another. Compared
with an instrumentalist — who adopts a restrictive view that eliminates
one of the two major criteria (truth and usefulness) by excluding a consideration
of truth-estimating plausibility — a realist has a wider vision that looks
for both plausibility and utility.
Second, a critical realist (CR) distinguishes
between goals and claims. A CR is a realist about goals,
and a critic about claims. A CR combines realist
goals (wanting to find the truth) with critical
evaluation (willing to be skeptical about claims for the truth status
of a particular theory). As explained in Section 4D,
realism (for goals) is compatible with criticism (of plausibility). For
example, it is difficult to deny that scientists in the early 1950s who studied
the structure of DNA were aiming for a theory that would describe the actual
structure of DNA. They wanted to find the truth, so they were realists.
Before 1953, however, their claims were modest, because all of their theories
had a low truth-plausibility. They were evaluating
critically, in an effort to achieve their realist goals. But after
April 1953 the claims for truth became bold, and those who were most knowledgeable
quickly decided that the double helix structure deserved to have a very high
plausibility because it almost certainly was true.
4B. Pros and Cons of Instrumentalism
What is the status of the main alternative
to realism? Laudan (1984) clearly expresses the two most common arguments
in favor of instrumentalism:
One argument is that the components of many
abandoned theories were once considered real, so why should we be confident
that the components of current theories will not meet this same fate?
But this ignores the analogous counter-argument: History also provides
many examples of postulated components (for entities, actions, or interactions)
that are still considered valid. And sometimes postulated components that
initially could not be observed became observable when improved observation
technologies and techniques were developed. Laudan's argument depends
on inductive "boy who cried wolf" logic that is not deductively valid
and also, as just described, is opposed by many counter-examples. And
it seems to imply that, in a contest of "1403 vs 2003", the fact that current theories consistently
win should be counted as evidence against the possibility that these theories
might be true, or at least approximately true.
In my opinion, the strongest argument for
the reality of many components of modern theories is that it seems extremely
unlikely that these theories could make accurate predictions if none of their
components (or very few of them) corresponds to what is actually happening in
nature. In other words, a claim that "this theory is true, or is
at least approximately true" seems to be a plausible explanation for why
the theory can make accurate predictions. This is not a proof, of course,
but it does seem like a rational way to bet.
Laudan's second argument is that a goal is
"utopian" if there can never be a way to know whether it has been
achieved. Since the truth of a theory can never be proved, we can never
know if we have achieved a realist goal, so this utopian goal should not be
held by rational scientists. Compared with his first argument, I find
this one more impressive. But I remain unconvinced, for reasons similar
to the "best way to bet" arguments against logical
skepticism. Even though there is no way to prove a theory is true
or false, scientists can have a rationally justified confidence about it, and
this is all that most modern scientists expect. More important, we should
remember that the defining characteristic of realism is a goal (our search for
truth), not a claim (for certainty about truth).
To say that scientists do always
think instrumentally is inaccurate*, and to demand that scientists should
always think instrumentally (by never thinking of a theory in terms of its possible
truth) is too restrictive. { * In my experience, most scientists have
difficulty even understanding the concept that scientists don't try to
search for truth, and they certainly don't agree with it. } Compared
with instrumentalism, the eclectic "best of both" framework offered
by critical realism is a much better way to describe the actual practice of
science, because this framework flexibly accommodates the fact that both types
of thinking (in terms of utility and truth) are used in science, with the relative
proportions depending on the scientist and the situation.
Attitudes toward utility
and truth also differ in science and design. An engineer whose
main goal is to design an improved product will tend to be more satisfied with
viewing a theory only in terms of its usefulness in promoting progress toward
this goal, without thinking too much about whether the theory is true.
When a theory is viewed as a practical tool whose function is to be useful during
the process of design, the question of truth becomes less important than in
science where accurate understanding is the main goal.
Do most scientists usually search for truth? Yes. Of course, searching for truth is not the only goal. Scientists are also motivated by the intellectual stimulation and satisfaction of solving problems, and by practical benefits such as obtaining grants, earning salaries, publishing papers, gaining respect from scientific colleagues and nonscientists, and developing science-based technologies that will bring practical benefits like improved health care or new consumer products. Yes, all of these are motivations, but it's not an either-or choice, and most scientists also want to construct accurate theories, theories that match the reality of what is happening in nature.
Webster's New Collegiate Dictionary defines
instrumentalism as "a doctrine that ideas
are instruments of action and that their usefulness determines their truth."
Does this doctrine make sense? An idea may be useful because it is true,
and its usefulness may be an indication of its truth. But an idea cannot
be true — in the sense that it
corresponds to reality (*) — because
it is useful. { * And if we adopt any
other meaning of "true", this word loses its usefulness. With
any other meaning, the essential concept of "truth" is removed from
our vocabulary, and the loss of an important concept should be avoided, even
by those who disagree with the concept. }
What is the relation between instrumentalism
and postmodernism? There is a partial correlation. Arguments against
truth as a goal (and even against truth as a concept!) are especially popular
among scholars who get excited about postmodern theories based on radical relativism.
But anti-realist perspectives are also held by scholars who don't have postmodernist
views. A postmodernist is usually (or always?) an instrumentalist, but
an instrumentalist may not be a radical relativist or a postmodernist.
As explained in Section 4A, there is "a
range of instrumentalist views." I have intellectual respect
for instrumentalists who refuse to make the arrogant claim that "usefulness
determines...truth"), who are humble in their claims about the power
of theories to determine truth (a theory can influence cultural worldviews about
what they think the truth is, but this is not the
same as determining what the truth actually is,
as explained in Section 4C), who say "an instrumentalist is not claiming
(or denying) that if a theory is useful then this makes it true (by making it
correspond with reality), since the claims being made are actually more modest
than those of a realist, with an instrumentalist making claims only about a
theory's usefulness."
By contrast, the following section shows
the foolish self-delusion that occurs when people become arrogant about the
power of their own ideas.
My comments and the quotations above, from a scientist (Grinnell) and a philosopher (Sober), summarize the most important concepts in "Reality 101" so I'll just close this section with an example from science: Anyone who really thinks that "beliefs create reality" should be eager to explain how the real motions of all planets in the solar system changed from earth-centered orbits in 1500 (when this was believed by almost everyone) to sun-centered orbits in 1700 (when this was believed by almost all scientists). Did the change in beliefs (from theories of 1500 to theories of 1700) cause a change in reality (with planets beginning to orbit the sun at some time between 1500 and 1700)?
4D. Six Types of Status, plus Interpretations and Claims
As a reminder that the outcome of
theory evaluation is an educated estimate rather than a claim for certainty,
my model of Integrated Scientific Method (ISM)
uses a continuum of theory status, ranging
from very low to very high, to describe our degree of confidence in a theory.
To allow a more precise description of theory status, eight additional distinctions
are useful.
Each theory has six
types of status (in three pairs) and an interpretation,
plus a range of claims:
1a. Each theory has relative status (compared
with alternative theories) and intrinsic status.
1b. Each theory has pursuit status and acceptance
status. As suggested by Laudan (1977), even if a theory is not
judged to be worthy of acceptance, scientists can rationally view this theory
as worthy of pursuit (for continuing development, application, and testing)
if it seems to have potential for developing in ways that will improve its plausibility
and utility, or is useful (even in its current form) for stimulating new experimental
or theoretical research.
1c. Each theory has
truth status
and utility status.
/ In ISM, truth status is an estimate
of the similarity between the composition-and-operation of real systems and
the composition-and-operation of models (for these real systems) that are constructed
by using the theory. { Here, I am using a correspondence
definition of truth, which says "a theory is true if it corresponds
to what actually exists." I think this is the only meaning worthy
of being called "truth", and is the only definition we should use.
} Truth status (which I call plausibility)
is a human estimate for the probability of truth, rather than a claim for the
certainty of our knowledge about truth. / A theory's utility status is an estimate of the overall usefulness
of this theory, including scientific utility for cognition and research and
(if utility is defined more broadly) for cultural-personal usefulness.
2. Each theory
can be viewed with a realist
interpretation (in which scientists think this theory is intended to
have two types of function: to be useful and to describe what really occurs
in nature) or an instrumentalist
interpretation (that this theory is intended only to be useful, and does
not claim to describe reality). This interpretation can vary along a continuum
from pure realist to pure instrumentalist. A "degree of realism"
interpretation is independent from estimates of plausibility and utility.
For example, a scientist may think that a particular theory is intended to portray
reality (so there is a realist interpretation) but does not do this very well
(so it has a low plausibility). Also, a realist interpretation is compatible
with a strong emphasis on utility, since a theory can aim to be both true and
useful. {back to 4B}
3. Another "flexibility concept"
helps us think more precisely about specific applications of a general theory.
When a theory is used to construct a theory-based model of a particular experimental
system, scientists can use variable-strength hypotheses
to make different "similarity claims" for this system-and-model.
The plausibility estimate (for truth status) that is an outcome of evaluation
can vary with the strength of a hypothetical claim. A strong claim (for
an exact match between all features of a theory-based model and the real system)
may have lower truth status than a weaker claim (for a similarity that is approximate
rather than exact, or for a similarity between some features but not all).
{ more details about the concept of variable-strength
hypotheses }
This definition of hypothesis
(from Giere, 1991) refers to claims about the similarity between a model and
system, and is thus oriented toward truth status, which is relevant only with
a realist interpretation. But we can also think of variable-strength hypotheses
as making different claims about the expected degrees of agreement between predictions
and observations. For example, a medium-strength hypothesis might claim
there will be a close match for some predictions, but a less exact match for
other predictions. This emphasis on "predictive utility" would
be compatible with either realist or instrumentalist interpretations.
Or there might be differing strength-claims about other aspects of a theory's
scientific utility.
THE UTILITY OF THESE CONCEPTS.
Some terms in this section (pursuit and acceptance, plausibility and utility,
realism and instrumentalism) are commonly used in philosophy, while others (intrinsic
status and relative status, truth status and utility status) have been defined
by me, and one (hypothesis) is used with a variety of different meanings.
Only a few of these terms (acceptance, plausibility, hypothesis) are common
in the language of scientists, but I think all of the concepts are common
in the thinking of scientists.
These concepts are useful because, instead
of forcing ideas into the narrow channels of rigid language, they encourage
flexible thinking. They do not limit a thinker to dichotomous alternatives
such as acceptance or rejection, verification or falsification. These
binary categories are still available — because if status rises above
a certain level we can think in terms of acceptance, and if it falls too low
we can choose to reject — but a "yes or no" choice is not forced
on us prematurely because our rigid concepts have limited the options we are
capable of creatively imagining and thoughtfully considering.
In a community of critical thinkers, this
system of concepts (*) will encourage thinking and communication that is more
accurate and precise, whether we are evaluating a theory or describing the scientific
methods used in specific situations or in general. And in a classroom,
these concepts will encourage students to think and communicate more carefully,
with increased precision. {* Sometime soon, I'll be organizing these concepts
into a logical framework that is coherent yet flexible. }
The basic concepts of reality are examined more closely in Reality 101.
The motivations for positivist constraints
can be due to beliefs about utility (what is useful)
and/or ontology (what exists).
utility: One motivation for positivist philosophy is to build science on the firm foundation
of empirical observations, thereby making scientific knowledge more certain.
ontology: Or positivists may want to purge science of metaphysical proposals
for unobservables. But most philosophers have concluded that positivism
does not necessarily make science more certain; and instead of making
science non-metaphysical, it simply replaces one type of metaphysics with another
type.
{ Since modern versions of positivism can be called empiricism, in an effort to avoid confusion I'll call attention to an important difference between two similar terms: empirical science, which uses empirical observations and empirical evaluations, includes all science, both empiricist science (with only observable components in theories) and non-empiricist science (that allows both observable and unobservable components). Since HD logic allows scientists to empirically evaluate the plausibility of components that cannot be observed (but that produce effects which can be observed), non-empiricist science (which can include theories containing non-observable components) requires HD logic. But empiricist science (i.e., positivist science) can be done with or without HD; or, viewed from another perspective, HD logic can be done for theories with observable and/or unobservable components. In fact, a desire to accomodate non-positivist theories in science was a major motivation in developing the current importance of HD logic. }
Here is part of the section on positivism from my "Details of Scientific Method" page:
CONSTRAINTS ON UNOBSERVABLE COMPONENTS. A positivist
believes that scientific theories should not postulate the existence of unobservable
entities, actions, or interactions. For example, behaviorist psychology
avoids the concept of "thinking" because it cannot be directly observed.
A strict positivist will applaud Newton's theory of gravitation, despite its
lack of a causal explanatory mechanism, because it is an empirical generalization
that is reliable and approximately accurate, and it does not postulate (as do
more recent theories of gravity) unobservable entities such as fields, curved
space, or gravitons. But most scientists, although they appreciate Newton's
descriptive theory for what it is, consider the absence of explanation to be
a weakness.
some comments about terminology: Positivism
was proposed in the 1830s by Auguste Comte, who was motivated partly by anti-religious
ideology. In the early 20th century a philosophy of logical
positivism was developed to combine positivism with other ideas.
In current use, "positivism" can be used in a narrow sense (as Comte
did, and as I do here) or it can refer to anything connected with logical
positivism, including the "other ideas" and more. Logical positivism
can also be called logical empiricism. { Notice that empiricism
(i.e., positivism) is not the same as empirical.
A theory that is non-empiricist (because
it some components, such as atoms or molecules, that are unobservable) can make
predictions about empirical data that can
be used in empirical evaluation. }
Although positivism (or empiricism, the name
typically given to the modern versions of positivism currently being proposed)
is considered a legitimate perspective in philosophy, it is rare among scientists,
who welcome a wide variety of ways to describe and explain. Many modern
theories include unobservable entities and actions, such as electrons and electromagnetic
force, among their essential components. Although most scientists welcome
a descriptive theory that only describes empirical patterns, at this point they
think "we're not there yet" because their limited theory is seen as
just a temporary stage along the path to a more complete theory. This
attitude contrasts with the positivist view that a descriptive theory should
be the ending point for science.
The ISM framework includes two types of theories (and corresponding models) — descriptive
and explanatory — so it is compatible with any type of scientific theory,
whether it is descriptive, explanatory, or has some characteristics of each.
My own anti-positivist opinions, which are not part of the ISM framework, are
summarized in the preceding paragraph, and are discussed in more depth on the
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