12/25/2009
PHILOSOPHY
OF SCIENCE
WHAT IS THE SCIENTIFIC EXPLANATION
Reviewed by:
Kaan ÖKTEM
Prep. by:
İlhan ÇİFTÇİ
Scientific Explanation
Bahçeşehir University
Page 1
Abstract
Within the philosophy of science there have been competing ideas about what an explanation
is. Historically, explanation has been associated with causation: to explain an event or
phenomenon is to identify its cause. But with the growth and development of philosophy of
science in the 20th century, the concept of explanation began to receive more rigorous and
specific analysis. Scientific Explanation is very important phase; most of the theories try to
explain its nature with a scientific explanation. The structure of the scientific explanation was
changed since 1950. This article focuses “what is a scientific explanation?” Science and the
Explanation statements will be accounted firstly. Difference among the description, prediction
and explanation tried to remark. Scientific Explanation Criteria should add contribution of
evaluating hypothesis which consisted of relevance, testability, compatibility, predictive
power, simplicity. There have been the three major processes involved in explanation and the
four major theoretical approaches that have been taken in computational models of it. The
four major theoretical approaches are: deductive, using logic or rule-based systems;
schematic, using explanation patterns or analogies; probabilistic, using Bayesian networks;
and neural, using networks of artificial neurons. For each of these theoretical approaches, it is
possible to characterize the different ways in which the provision, generation, and evaluation
of explanations are understood computationally. Finally, we summarize the scientific
explanation with the mentioned above.
Purpose
To understand explanation, scientific explanation and difference among the explanation and
other theatricals.
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Contents
Abstract ................................................................................................................................................... 2
Purpose.................................................................................................................................................... 2
Science and Explanation.......................................................................................................................... 4
Description & Prediction & Explanation.................................................................................................. 5
Scientific Explanation Criteria ................................................................................................................. 6
Relevance ............................................................................................................................................ 7
Testability ............................................................................................................................................ 7
Compatibility ....................................................................................................................................... 7
Predictive Power ................................................................................................................................. 8
Simplicity ............................................................................................................................................. 8
Scientific Explanation Models ................................................................................................................. 8
Deductive Nomological Model ............................................................................................................ 8
Schema and Analogy Models ............................................................................................................ 12
Probabilistic Models .......................................................................................................................... 13
Neural Network Models .................................................................................................................... 14
Conclusion ............................................................................................................................................. 15
References ............................................................................................................................................. 16
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Science and Explanation
Before the segregation of Philosophy and Science, science would have taken place in
philosophy, Due to dissegregation, philosophers of science were taken in both areas science
and philosophy. Philosophers was studied science, Aristotle was a good sample who had
wrote physics and metaphysics. Related to development of science, this detach was realized.
Acceptance of science led to bring “explanation” phase together.
Science is not merely a collection of facts, concepts, and useful ideas about nature, or even the
systematic investigation of nature, although both are common definitions of science. Science
is a method of investigating nature – a way of knowing about nature – that discovers reliable
knowledge about it. In other words, science is a method of discovering reliable knowledge
about nature.1
Most of the theories or topics remark its nature with a scientific explanation. Enterprise
Resource Planning (ERP), Customer Relationship Management (CRM), and countless lots of
today’s topic use scientific explanation for accounting their own nature. It is too difficult to
prove it’s useless and aim without scientific explanation.
Historically, explanation has been associated with causation: to explain an event or
phenomenon is to identify its cause. But with the growth and development of philosophy of
science in the 20th century, the concept of explanation began to receive more rigorous and
specific analysis. Of particular concern were theories that posited the existence of
unobservable entities and processes (atoms, fields, genes, and so forth). These posed a
dilemma: on the one hand, the staunch empiricist had to reject unobservable entities as a
matter of principle; on the other, theories that appealed to unobservable entities were clearly
producing revolutionary results. Thus philosophers of science sought some way to
characterize the obvious value of these theories without abandoning the empiricist principles
deemed central to scientific rationality.2
1
Steven D. Schafersman, An Introduction to Science, Scientific Thinking and the Scientific Method, January,
1994, p.1
2
G.RandolphMayes, Theories of Explanation California State University Sacramento, July 2005
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Before proceeding with accounts of particular computational models of explanation, let us
characterize more generally the three major processes involved in explanation and the four
major theoretical approaches that have been taken in computational models of it. The three
major processes are: providing an explanation from available information, generating new
hypotheses that provide explanations, and evaluating competing explanations. The four major
theoretical approaches are: deductive, using logic or rule-based systems; schematic, using
explanation patterns or analogies; probabilistic, using Bayesian networks; and neural, using
networks of artificial neurons. For each of these theoretical approaches, it is possible to
characterize the different ways in which the provision, generation, and evaluation of
explanations are understood computationally3.
The important question about scientific explanation is studying on Science and displaying the
structure is a Scientific Explanation? This question is solved with “Critical Thinking”.
Critical thinking is thinking correctly for oneself that successfully leads to the most reliable
answers to questions and solutions to problems. In other words, critical thinking gives us
reliable knowledge about the nature world. Clearly, scientific and critical thinking are the
same thing, but where (scientific thinking) is always practiced by scientists, the other (critical
thinking) is sometimes used by humans and sometimes not.4
Description & Prediction & Explanation
Explanation term uses different meanings up to date. The significant similarities between the
description and the explanation led to mention both terminologies.
What does the difference between an explanation and a description? Wittgenstein’s use of the
term “description” seems to rely on the sense of description as an activity more fluid, less
rigid, more observational and more descriptive than explanation. Whereas explanation might
rely on explicit rules, or governed by standards of use, description shows us how the rules can
be bent, or how a new use for a concept can express itself. 5
Description: a statement, picture in words, or account that describes; descriptive
representation.
3
Thagard, P., & Litt, A. (forthcoming). Models of scientific explanation. In R. Sun (Ed.), The Cambridge
handbook of computational cognitive modeling. Cambridge: Cambridge University Press.
4
Steven D. Schafersman, An Introduction to Science Scientific Thinking and Scientific Method, January, 1994
5
David Chalmers, Philoposy of Language, July 2007, Explanation vs. Description
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Explanation: something that explains; a statement made to clarify something and make it
understandable; exposition: an explanation of a poem. Saxon used clarifies instead of
explanation.
Description is simply that, a statement of facts, of what precisely happens. An explanation is a
farther reaching attempt that ultimately must revert back to our core fascination with “Why?”
We wish to know more about the reason behind what is happening.
Explanation and prediction are logically indistinguishable. According to Hempel, all
predictions are explanation, but all explanatory arguments are predictive. Prediction is wider
concept and explanations are a sub – class of predictions6.
Scientific Explanation Criteria
Scientific Explanation is an explanation, which is accounted its nature in accordance to
science and explanation amalgam. How can we believe an explanation is scientific or not?
This has some features explained:
 Scientific explanation must be repetitive
 Scientific explanation must be planned
 Scientific explanation is relied on observation
 Scientific explanation is relied on doubt also.
 Scientific explanation can be measurable
 Data which is used for scientific explanation evaluates statistically.
 It is denoted with confidence level and assumptions.
Explanation, which does not carry the any of above criterion, is not a scientific such as
absolute truths.
In addition to above mentioned several criteria that can be used in making the relative
judgments about the reliability of and hypothesis offered as part of a scientific explanation,
these are: relevance, testability, compatibility, predictive power and simplicity7.
6
Harold, I Brown, The New Philosopy of Science, Perception Theory and Commitment, The University of
Chicago, Precedent Publishing, Inc.
7
Garth Kemerling, Scientific Explanation, The Structure of Explanation, 2001
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Relevance
Relevancy is an effect relationship between the causality and results of facts.
The relevant philosophy is the philosophy which results when a man wakes up in the morning and
begins to wonder why he is going to do what he is going to do for the rest of the day. There are
obvious reasons, of course, for his going out to work at his job. He has a family to feed. He has
himself to feed. But why this job and not some other? Suppose he is a man who is able to think
about the lives and behavior of other people. Nine times out of ten, he makes his living from what
we are disposed to regard as the weaknesses and appetites of other people. He may, on that
morning when he begins his questioning, say to himself that this is a terrible way to make a
living8.
Testability
Testability is a measurable of facts. It is the way to acquire evidence and approve hypothesis
accurately.
Testability is a design issue and needs to be addressed with the design of the rest of the
system. Projects that defer testing to the later stages of a project will find their programmers
unwilling to consider testability changes by the time testers actually roll onto the project and
start making suggestions9.
Compatibility
The hypothesis should fit well with what we already believe about the natural order of things.
Since centuries of scientific investigation have provided us with a body of knowledge
comprising many reliable hypotheses, we should expect that each newly added hypothesis
will be consistent with them. Of course, this criterion is not absolute; genuine scientific
revolutions can involve the abandonment or significant modification of previously accepted
hypotheses, and it is important to allow for that possibility.
8
9
Manas Reprint, Lead Article, The Relevence of Philiosopy, Volume 15, No 1, January 1962
Bret Pettichord, Desgin for Testability, 2002
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Predictive Power
The predictive power of a scientific theory refers to its ability to generate testable predictions.
Theories with strong predictive power are highly valued, because the predictions can often
encourage the falsification of the theory. The concept of predictive power differs from
explanatory and descriptive power (where phenomena that are already known are
retrospectively explained by a given theory) in that it allows a prospective test of theoretical
understanding10.
Simplicity
The best hypotheses are rarely intricate in structure. Again, it would be possible to
overemphasize this criterion, since an adequate complex hypothesis is clearly preferable to a
simple but inadequate one. Still, there is a profound elegance and dignity in the simple
character of many scientific hypotheses11 (universal gravitation is a good example again).
Scientific Explanation Models
Addition to mentioned explanations above, there is no common point of views about the
explanations and descriptions which is more different and scientific. Scientific explanations
models are comprised form this differences.
Scientific explanation models are comprised 4 different models. These are: Deductive
nomological model (DN), Schema and Analogy Models, Probabilistic Models, and Neural
Network Models. The first model DN was the foundation scientific explanation models and
contemporary models followed this Hempel DN model with contributions.
Deductive Nomological Model
According to Hempel, an explanation is:
10
11
Web pages, http://www.viswiki.com/en/Predictive_power
Web pages, http://www.philosophypages.com/lg/e15.htm
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...an argument to the effect that the phenomenon to be explained ...was to be expected in
virtue of certain explanatory facts12. (1965 p. 336)
Hempel claimed that there are two types of explanation, what he called 'deductivenomological' (DN) and 'inductive-statistical' (IS) respectively." Both IS and DN arguments
have the same structure. Their premises each contain statements of two types: (1) initial
conditions C, and (2) law-like generalizations L. In each, the conclusion is the event E to be
explained:
C1, C2, C3,...Cn
L1, L2, L3,...Ln
-----------------------E
The only difference between the two is that the laws in a DN explanation are universal
generalizations, whereas the laws in IS explanations have the form of statistical
generalizations. An example of a DN explanation containing one initial condition and one
law-like generalization is:
C. The infant's cells have three copies of chromosome 21.
L. Any infant whose cells have three copies of chromosome 21 has Down's Syndrome.
-------------------------------------------------------------------------------------------------E. The infant has Down's Syndrome.
An example of an IS explanation is:
C. The man's brain was deprived of oxygen for five continuous minutes.
L. Almost anyone whose brain is deprived of oxygen for five continuous minutes will sustain
brain damage.
--------------------------------------------------------------------------------------------------E. The man has brain damage.
12
Web page, The Internet Encyclopedia of Philosophy, http://www.utm.edu/research/iep/e/explanat.htm
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For Hempel, DN explanations were always to be preferred to IS explanations. There were two
reasons for this.
First, the deductive relationship between premises and conclusion maximized the predictive
value of the explanation. Hempel accepted IS arguments as explanatory just to the extent that
they approximated DN explanations by conferring a high probability on the event to be
explained.
Second, Hempel understood the concept of explanation as something that should be
understood fundamentally in terms of logical form. True premises are, of course, essential to
something being a good DN explanation, but to qualify as a DN explanation (what he
sometimes called a potential DN explanation) an argument need only exhibit the deductivenomological structure. (This requirement placed Hempel squarely within the logical positivist
tradition, which was committed to analyzing all of the epistemically significant concepts of
science in logical terms.) There is, however, no corresponding concept of a potential IS
explanation. Unlike DN explanations, the inductive character of IS explanations means that
the relation between premises and conclusion can always be undermined by the addition of
new information. (For example, the probability of brain damage, given that a man is deprived
of oxygen for 7 minutes, is lowered somewhat by the information that the man spent this time
at the bottom of a very cold lake.) Consequently, it is always possible that a proposed IS
explanation, even if the premises are true, would fail to predict the fact in question, and thus
have no explanatory significance for the case at hand.
Firstly, this model was founded as scientific explanation theory by Carl Hempel. Related to
amalgam of Inductive – statistical model, this theory was identified Deductive Nomological
Model(DN).
According to Hempel, such an explanation is always a deductive derivation of the occurrence
of the event to be explained from a set of true propositions including at least one statement of
a scientific law. (The event to be explained is called the explanandum; the set of explaining
statements is sometimes called the explanans.) In other words, a deterministic event
explanation is always a sound, law-involving, deductive argument with the conclusion that the
explanandum event occurred13.
13
Thagard, P., & Litt, A. (forthcoming). , mentioned before
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The explanatory account may be regarded as an argument to the effect that the event to be
explained was to be expected by reason of certain explanatory facts. These may be divided
into two groups: (i) particular facts and (ii) uniformities expressed by general laws14.
The most suitable sample for Deductive Nomological Model (DN) is mercury and
thermometer:
When we put the thermometer in a hot water, mercury level reduces depend upon the
expanding of window. The expansion of window is less than mercury. After this phase, the
level of mercury increase related to exchange of fever.
 The (cool) sample of mercury was placed in hot water, heating it,
 Mercury expands when heated, thus
 The sample of mercury expanded.
Hempel and the contributors of the DN, thought that, scientific explanation must be
measurable. It supports with empirical tests.
Relevance and asymmetry problems break the Hempel Theory’s reliability. The reasing
qualifies as explanatory on Hempel’s Theory despite the fact that premises seem to be
explanatorily irrelevant to the conclusion. The causation of the conclusion cannot be
identifying the conclusion, which were true? The occurrence of causation led to bring
conclusion or the outcome occurrence lead to bring causation. This problem creates
asymmetry.
The absence of exception less generalizations that can be used in deductive explanations is
not
Unique to ethics. In the special sciences—e.g., in biology, psychology, economics, and
history— scientists have yet to find and formulate exceptionless laws. And since
exceptionless laws are not currently available in these disciplines, it follows that if scientists
provide any adequate explanations in these fields at all, their explanations conform to an
alternative, non-deductive, model of explanation15.
14
15
Carl G. Hempel, Two Basic Types of Scientific Explanation
Uri D. Leibowitz, Scıentıfıc Explanation And Moral Explanation, cornellcollege,
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Schema and Analogy Models
In ordinary life and in many areas of science less mathematical than physics, the relation
between what is explained and what does the explaining is usually looser than deduction. An
alternative conception of this relation is provided by understanding an explanation as the
application of a causal schema, which is a pattern that describes the relation between causes
and effects16.
A computational cognitive model of explanation schemas was developed in the Swale project.
If Swale finds more than one relevant explanation pattern, it evaluates them to determine
which is most relevant to the intellectual goals of the person seeking understanding. If the best
explanation pattern does not quite fit the case to be explained, it can be tweaked (adapted) to
provide a better fit, and the tweaked version is stored in memory for future use.
In deductive explanation, there is a tight logical relation between what is explained and the
sentences that imply it, but in schematic or analogical explanation there need only be a
roughly specified causal relation.
Comparison of Deductive and Schematic or Analogical Model
In deductive explanation, there is a tight logical relation between what is explained and the
sentences that imply it, but in schematic or analogical explanation there need only be a
roughly specified causal relation.
Two approaches are not competing theories or explanation, because explanation can take
different forms in different areas of science.
16
Thagard, P., & Litt, A. (forthcoming, mentioned before
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Probabilistic Models
The core idea is that people explain why something happened by citing the factors that made
it more probable than it would have been otherwise.
The main computational method for modeling explanation probabilistically is Bayesian
networks.
Bayesian networks consist of17:




A set of variables and a set of directed edges between variables
Each variable has a finite set of mutually exclusive states
The variables and directed edges form a directed acyclic graph
To each variables A with parents B1, B2 to Bn there is attached the probability table
P(A| B1, B2 …. Bn )
We can represent the causal relationship between facts as follows;
Studying
Successful
Reward
Promotion
Satisfaction
In a Bayesian Network, each node is a variable and the arrow indicates causality represented
by conditional probability.
This kind of explanation, too, is nomological, i.e., it accounts for a given phenomenon by
reference to general laws or theoretical principles; but some or all of these are of probabilisticstatistical form, i.e., they are, generally speaking, assertions to the effect that if certain
17
Tina Birk Jensen, Intoduction to Bayesian Networks, University of Copenhagen, Faculty of Life Sciences, 2009
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specified conditions are realized, then an occurrence of such and such a kind will come about
with such and such a statistical probability18.
Despite the Bayesian Network plausible, and computational power, there are reasons to doubt
the psychological relevance. These are19:
 Computing with Bayesian networks requires a very large number of conditional
probabilities that people not working in statistics have had no chance to acquire.
 there is no reason to believe that people have the sort ofinformation about
independence that is required to satisfy the Markov condition and to make inference in
Bayesian networks computationally tractable,
 Third, although it is natural to represent causal knowledge as directed graphs, there are
many scientific and everyday contexts in which such graphs should have cycles
because of feedback loops
Neural Network Models
The most important approach to cognitive modeling not yet discussed employs artificial
neural networks. Applying this approach to high-level reasoning faces many challenges,
particularly in representing the complex kinds of information contained in scientific
hypotheses and causal relations. Thagard (1989) provided a neural network model of how
competing scientific explanations can be evaluated, but did so using a localist network in
which entire propositions were represented by single artificial neurons and in which relations
between propositions are represented by excitatory and inhibitory links between the neurons.
Although this model provides an extensive account of explanation evaluation that is reviewed
below, it reveals nothing about what an explanation is or how explanations are generated.
Neural network modelers have been concerned mostly with applications to low-level
psychological phenomena such as perception, categorization, and memory, rather than highlevel ones such as problem solving and inference20
The neural network models concerned incorporate multi model aspects which is ignored by
deductive, schematic and probabilistic approach.
18
Carl G. Hempel, mentioned before
Thagard, P., & Litt, A. (forthcoming), mentioned before
20
Thagard, P., & Litt, A. (forthcoming), mentioned before
19
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Conclusion
Explanation and Scientific Explanation are accounted in accordance to philosophy of science
frameworks. Although, there is a relationship among the statements of description, prediction
and explanation, differences are represented. The scientific explanation criteria also explained
such as measurability, reliable and so forth. Hypothesis confidence evaluation made with
testability, predicted power and so forth. Finally what are the model ideas about scientific
explanation tried to remark. Deductive nomological model, schematic or analogical model,
probabilistic model and the last neural network models explained. The contribution of this
studying cannot be separated from the other scientific explanation studying for philosophy of
science.
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References
Bret Pettichord, Desgin for Testability, 2002
Carl G. Hempel, Two Basic Types of Scientific Explanation
David Chalmers, Philoposy of Language, July 2007, Explanation vs. Description
Garth Kemerling, Scientific Explanation, The Structure of Explanation, 2001
G.RandolphMayes, Theories of Explanation California State University Sacramento, July
2005
Harold, I Brown, The New Philosopy of Science, Perception Theory and Commitment, The
University of Chicago, Precedent Publishing, Inc.
Manas Reprint, Lead Article, The Relevence of Philiosopy, Volume 15, No 1, January 1962
Steven D. Schafersman, An Introduction to Science, Scientific Thinking and the Scientific
Method, January, 1994
Thagard, P., & Litt, A. (forthcoming). Models of scientific explanation. In R. Sun (Ed.), The
Cambridge handbook of computational cognitive modeling. Cambridge: Cambridge
University Press.
Tina Birk Jensen, Intoduction to Bayesian Networks, University of Copenhagen, Faculty of
Life Sciences, 2009
Web Pages
http://www.viswiki.com/en/Predictive_power
http://www.philosophypages.com/lg/e15.htm
The Internet Encyclopedia of Philosophy,
http://www.utm.edu/research/iep/e/explanat.htm
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