12/25/2009

Scientific Explanation


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 importantphase; 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 statementswill 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.

Contents

Abstract

Purpose

Science and Explanation

Description & Prediction & Explanation

Scientific Explanation Criteria

Relevance

Testability

Compatibility

Predictive Power

Simplicity

Scientific Explanation Models

Deductive Nomological Model

Schema and Analogy Models

Probabilistic Models

Neural Network Models

Conclusion

References

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]

Before proceeding with accounts of particular computational models ofexplanation, 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 computationally[3].

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.

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 predictions[6].

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 simplicity[7].

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 living[8].

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 suggestions[9].

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.

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 understanding[10].

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 hypotheses[11] (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 modelDN wasthe foundation scientific explanation models and contemporary models followed this Hempel DN model with contributions.

Deductive Nomological Model

According to Hempel, an explanation is:

...an argument to the effect that the phenomenon to be explained ...was to be expected in virtue of certain explanatory facts[12]. (1965 p. 336)

Hempel claimed that there are two types of explanation, what he called 'deductive-nomological' (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.

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 deductive-nomological 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 occurred[13].

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 bedivided into two groups: (i) particular facts and (ii) uniformities expressed by general laws[14].

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. Thecausation 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

Uniqueto 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 explanation[15].

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 effects[16].

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 andthe sentences that imply it, but in schematic or analogical explanation there need only bea 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.

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 of[17]:

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;