SCIENCE AND WONDER

Thousands of years ago, our explanations about how the world worked were not very good. Things we couldn't understand were attributed to praise or vengeance from gods, or thinking the world was random. Thanks to science, we have a much better idea about why things are the way they are. Issues concerning scientific explanation have been a focus of philosophical attention from Pre-Socratic times through the modern period. However, modern discussion really begins with the development of the Deductive-Nomological (DN) model. A presupposition of most recent discussion has been that science sometimes provides explanations and that the task of a “theory” or “model” of scientific explanation is to characterize the structure of such explanations. Philosophical literature assumes that there is substantial continuity between explanations found in science and some forms of explanation found in ordinary, non-scientific contexts.

Philosophy of science

The philosophy of science is concerned with all the assumptions, foundations, methods, implications of science, and with the use and merit of science. This discipline sometimes overlaps metaphysics, ontology and epistemology, viz., when it explores whether scientific results comprise a study of truth. In addition to these central problems of science as a whole, many philosophers of science consider problems that apply to particular sciences (e.g. philosophy of biology or philosophy of physics). Some philosophers of science also use contemporary results in science to reach conclusions about philosophy. Philosophy of science has historically been met with mixed response from the scientific community. Though scientists often contribute to the field, many prominent scientists have felt that the practical effect on their work is limited; a popular quote attributed to physicist Richard Feynman goes, "Philosophy of science is about as useful to scientists as ornithology is to birds." In response, some philosophers (e.g. Craig Callender[1]) have suggested that ornithological knowledge would be of great benefit to birds, were it possible for them to possess it.

 Scientific explanation

 

In addition to providing predictions about future events, society often takes scientific theories to offer explanations for those that occur regularly or have already occurred. Philosophers have investigated the criteria by which a scientific theory can be said to have successfully explained a phenomenon, as well as what gives a scientific theory explanatory power. One early and influential theory of scientific explanation was put forward by Carl G. Hempel and Paul Oppenheim in 1948. Their Deductive-Nomological (D-N) model of explanation says that a scientific explanation succeeds by subsuming a phenomenon under a general law. An explanation, then, is a valid deductive argument. For empiricists like Hempel and other logical positivists, this provided a way of understanding explanation without appeal to causation.[] Although ignored for a decade, this view was subjected to substantial criticism, resulting in several widely believed counter examples to the theory. In addition to their D-N model, Hempel and Oppenheim offered other statistical models of explanation which would account for statistical sciences. [] These theories have received criticism as well.Salmon attempted to provide an alternative account for some of the problems with Hempel and Oppenheim's model by developing his statistical relevance model.[][] In addition to Salmon's model, others have suggested that explanation is primarily motivated by unifying disparate phenomena or primarily motivated by providing the causal or mechanical histories leading up to the phenomenon (or phenomena of that type).

Analysis and reductionism 

Analysis is the activity of breaking an observation or theory down into simpler concepts in order to understand it. Analysis is as essential to science as it is to all rational activities. For example, the task of describing mathematically the motion of a projectile is made easier by separating out the force of gravity, angle of projection and initial velocity. After such analysis it is possible to formulate a suitable theory of motion. Reductionism can refer to one of several philosophical positions related to this approach. One type of reductionism is the belief that all fields of study are ultimately amenable to scientific explanation. Perhaps a historical event might be explained in sociological and psychological terms, which in turn might be described in terms of human physiology, which in turn might be described in terms of chemistry and physics. Daniel Dennett invented the term greedy reductionism to describe the assumption that such reductionism was possible. He claims that it is just 'bad science', seeking to find explanations which are appealing or eloquent, rather than those that are of use in predicting natural phenomena. He also says that: There is no such thing as philosophy-free science; there is only science whose philosophical baggage is taken on board without examination.—Daniel Dennett, Darwin's Dangerous Idea, 1995.

Induction 

How is it that scientists can state, for example, that Newton's Third Law is universally true? After all, it is not possible to have tested every incidence of an action, and found a reaction. There have, of course, been many, many tests, and in each one a corresponding reaction has been found. But can one ever be sure that future tests will continue to support this conclusion? One solution to this problem is to rely on the notion of induction. Inductive reasoning maintains that if a situation holds in all observed cases, then the situation holds in all cases. 

                                                  So, after completing a series of experiments that support the Third Law, and in the absence of any evidence to the contrary, one is justified in maintaining that the Law holds in all cases. Although induction commonly works (e.g. almost no technology would be possible if induction were not regularly correct), explaining why this is so has been somewhat problematic. One cannot use deduction, the usual process of moving logically from premise to conclusion, because there is no syllogism that allows this. Indeed, induction is sometimes mistaken; 17th century biologists observed many white swans and none of other colours, but not all swans are white. 

                                                                                                                                   Similarly, it is at least conceivable that an observation will be made tomorrow that shows an occasion in which an action is not accompanied by a reaction; the same is true of any scientific statement. One answer has been to conceive of a different form of rational argument, one that does not rely on deduction. Deduction allows one to formulate a specific truth from a general truth: all crows are black; this is a crow; therefore this is black. Induction somehow allows one to formulate a general truth from some series of specific observations: this is a crow and it is black; that is a crow and it is black; no crow has been seen that is not black; therefore all crows are black. The problem of induction is one of considerable debate and importance in the philosophy of science: is induction indeed justified, and if so, how?

WHY-QUESTION

Many scientific explanations are requested by means of why-questions, and even when the request is not actually formulated in that way, it can often be translated into a why-question. For example, "What caused the Chernobyl accident?" or "For what reason did the Chernobyl accident occur?" are equivalent to "Why did the Chernobyl accident occur?" However, not all why-questions are requests for scientific explanations. A woman employee might ask why she received a smaller raise in salary than a male colleague when her job performance is just as good as his. Such a why question might be construed as a request for a justification, or, perhaps, simply a request for more pay. A bereaved widow might ask why her husband died even though she fully understands the medical explanation. Such a why-question is a request for consolation, not explanation. Some why-questions are requests for evidence. To the question, "Why should we believe that the distant galaxies are traveling away from us at high velocities?" the answer, briefly, is the red shift. Recall, as we noted in Section 1.1, that the red shift does not explain the recession. The recession explains the red shift; the red shift is evidence for the recession. For the sake of clarity, we distinguish explanation-seeking why questions from why-questions that seek such other things as justification, consolation, or evidence.

Explanation vs. Confirmation

The term “confirmation” is used in epistemology and the philosophy of science whenever observational data and evidence “speak in favor of” or support scientific theories and everyday hypotheses. Historically, confirmation has been closely related to the problem of induction, the question of what to believe regarding the future in the face of knowledge that is restricted to the past and present. Confirmation takes a qualitative and a quantitative form. Qualitative confirmation is usually construed as a relation, among other things, between three sentences or propositions. In order to justify induction one has to provide a deductively valid argument, or an inductively strong argument, whose premises we know to be true, and whose conclusion says that inductively strong arguments lead from true premises to true conclusions. Prediction (confirmation) involves providing reasons to believe that (or evidence that) certain claims (specifically, scientific theories) are true. The first step in clarifying the notion of scientific explanation is to draw a sharp distinction between explaining why a particular phenomenon occurs and giving reasons for believing that it occurs. 

"There also exists scientific problems where the solution calls for an unusual degree of sensitivity to the philosophical questions  - Sam Harris"

Does philosophy influence Natural Science and vice versa ?

 To be objective, Yes!, science absolutely influences all of philosophy, though often in a very indirect way. Likewise, philosophy forms the cornerstone of science. There are many popular examples that verify the same.

For example, let us consider the example of the Mach’s principle introduced by physicist and philosopher Ernst Mach while the term was later coined by Albert Einstein himself. 

The basic idea is that the existence of absolute rotation (the distinction of local inertial frames vs. rotating reference frames) is determined by the large-scale distribution of matter, as exemplified by this anecdote : 

"You are standing in a field looking at the stars. Your arms are resting freely at your side, and you see that the distant stars are not moving. Now start spinning. The stars are whirling around you and your arms are pulled away from your body. Why should your arms be pulled away when the stars are whirling? Why should they be dangling freely when the stars don't move?"

Mach's principle says that this is not a coincidence—that there is a physical law that relates the motion of the distant stars to the local inertial frame. If you see all the stars whirling around you, Mach suggests that there is some physical law which would make it so you would feel a centrifugal force. There are a number of rival formulations of the principle. It is often stated in vague ways, like "mass out there influences inertia here" which scientifically speaking sounds very impractical or unexplainable. A very general statement of Mach's principle is "local physical laws are determined by the large-scale structure of the universe", at least in this sense some of Mach's principles are related to philosophical holism (Holism is the idea that various systems (e.g. physical, biological, social) should be viewed as wholes, not merely as a collection of parts) which is in complete contradiction with the scientific study of Systems. For example consider the Earth system or Physiological systems where the system is broken down and the parts are studied, trying to identify any feedbacks and external forcings.