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Nature of scientific concepts and statements

Demarcation

Karl Popper contended that the central question in the philosophy of science was distinguishing science from non-science.^[1] Early attempts by the logical positivists grounded science in observation while non-science (e.g. metaphysics) was non-observational and hence nonsense.^[2] Popper claimed that the central feature of science was that science aims at falsifiable claims (i.e. claims that can be proven false, at least in principle).^[3] No single unified account of the difference between science and non-science has been widely accepted by philosophers, and some regard the problem as unsolvable or uninteresting.^[4]

This problem has taken center stage in the debate regarding evolution and intelligent design. The vast majority of opponents of intelligent design claim that it does not meet the criteria of science and should thus not be treated on equal footing as evolution.^[5] Those who defend intelligent design either defend the view as meeting the criteria of science or challenge the coherence of this distinction.^[6]

Scientific realism and instrumentalism

Two central questions about science are (1) what are the aims of science and (2) how ought one to interpret the results of science? Scientific realists claim that science aims at truth and that one ought to regard scientific theories as true, approximately true, or likely true. Conversely, a scientific antirealist or instrumentalist argues that science does not aim (or at least does not succeed) at truth and that we should not regard scientific theories as true.^[7] Some antirealists claim that scientific theories aim at being instrumentally useful and should only be regarded as useful, but not true, descriptions of the world.^[8] More radical antirealists, like Thomas Kuhn and Paul Feyerabend, have argued that scientific theories do not even succeed at this goal, and that later, more accurate scientific theories are not "typically approximately true" as Popper contended.^[9][10]

Realists often point to the success of recent scientific theories as evidence for the truth (or near truth) of our current theories.^{[11][12][13][14][15]} Antirealists point to either the history of science,^[16][17] epistemic morals,^[8] the success of false modeling assumptions,^[18] or widely termed postmodern criticisms of objectivity as evidence against scientific realisms.^[19] Some antirealists attempt to explain the success of our theories without reference to truth^[8][20] while others deny that our current scientific theories are successful at all.^[9][10]

Scientific explanation

In addition to providing predictions about future events, we often take 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 theoryexplanatory 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.^[21] Although ignored for a decade, this view was subjected to substantial criticism, resulting in several widely believed counter examples to the theory.^[22]

In addition to their D-N model, Hempel and Oppenheim offered other statistical models of explanation which would account for statistical sciences.^[21] These theories have received criticism as well.^[22] Salmon attempted to provide an alternative account for some of the problems with Hempel and Oppenheim's model by developing his statistical relevance model.^[23][24] 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).^[24]

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 enterprises. 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 in science can have several different senses. 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.

Arguments made against greedy reductionism through reference to emergent phenomena rely upon the fact that self-referential systems can be said to contain more informationthan can be described through individual analysis of their component parts. Examples include systems that contain strange loops, fractal organization and strange attractors inphase space. Analysis of such systems is necessarily information-destructive because the observer must select a sample of the system that can be at best partially representative. Information theory can be used to calculate the magnitude of information loss and is one of the techniques applied by Chaos theory.

Grounds of validity of scientific reasoning

Empirical Verification

Science relies on evidence to validate its theories and models. The predictions implied by those theories and models should be in agreement with observation. Ultimately, observations reduce to those made by the unaided human senses: sight, hearing, etc. To be accepted by most scientists, several disinterested, competent observers should agree on what is observed. Observations should be repeatable, e.g., experiments that generate relevant observations can be (and, if important, usually will be) done again. Furthermore, predictions should be specific; one should be able to describe a possible observation that would falsify the theory or a model that implies the prediction.

Nevertheless, while the basic concept of empirical verification is simple, in practice, there are difficulties as described in the following sections.

Induction

It is not possible for scientists to have tested every incidence of an action, and found a reaction. How is it, then, that they can assert, for example, that Newton's Third Law is in some sense true? They have, of course, tested many, many actions, and in each one have been able to find the corresponding reaction. But can we be sure that the next time we test the Third Law, it will be found to hold true?

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 allcases. So, after completing a series of experiments that support the Third Law, one is justified in maintaining that the Law holds in all cases.

Explaining why induction commonly works has been somewhat problematic. One cannot use deduction, the usual process of moving logically from premise to conclusion, because there is simply no syllogism that will allow such a move. No matter how many times 17th century biologists observed white swans, and in how many different locations, there is no deductive path that can lead them to the conclusion that all swans are white. This is just as well, since, as it turned out, that conclusion would have been wrong. Similarly, it is at least possible 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 law.

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; 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?

Test of an isolated theory impossible

According to the Duhem-Quine thesis, after Pierre Duhem and W.V. Quine, it is impossible to test a theory in isolation. One must always add auxiliary hypotheses in order to make testable predictions. For example, to test Newton's Law of Gravitation in our solar system, one needs information about the masses and positions of the Sun and all the planets. Famously, the failure to predict the orbit of Uranus in the 19th century led, not to the rejection of Newton's Law, but rather to the rejection of the hypothesis that there are only seven planets in our solar system. The investigations that followed led to the discovery of an eighth planet, Neptune. If a test fails, something is wrong. But there is a problem in figuring out what that something is: a missing planet, badly calibrated test equipment, an unsuspected curvature of space, etc.

One consequence of the Duhem-Quine thesis is that any theory can be made compatible with any empirical observation by the addition of suitable ad hoc hypotheses.

This thesis was accepted by Karl Popper, leading him to reject naïve falsification in favor of 'survival of the fittest', or most falsifiable, of scientific theories. In Popper's view, any hypothesis that does not make testable predictions is simply not science. Such a hypothesis may be useful or valuable, but it cannot be said to be science. Confirmation holism, developed by W.V. Quine, states that empirical data are not sufficient to make a judgment between theories. In this view, a theory can always be made to fit with the available empirical data. However, that empirical evidence does not serve to determine between alternative theories does not necessarily imply that all theories are of equal value, as scientists often use guiding principles such as Occam's Razor.

One result of this view is that specialists in the philosophy of science stress the requirement that observations made for the purposes of science be restricted to intersubjectiveobjects. That is, science is restricted to those areas where there is general agreement on the nature of the observations involved. It is comparatively easy to agree on observations of physical phenomena, harder for them to agree on observations of social or mental phenomena, and difficult in the extreme to reach agreement on matters of theology or ethics (and thus the latter remain outside the normal purview of science).

Theory-dependence of observations

When making observations, scientists peer through telescopes, study images on electronic screens, record meter readings, and so on. Generally, at a basic level, they can agree on what they see, e.g., the thermometer shows 37.9 C. But, if these scientists have very different ideas about the theories that supposedly explain these basic observations, they can interpret them in very different ways. Ancient "scientists" interpreted the rising of the Sun in the morning as evidence that the Sun moved. Later scientists deduce that the Earth is rotating. Or some scientists may conclude that observations confirm some hypothesis; skeptical co-workers may suspect that something is wrong with the test equipment. Observations when interpreted by a scientist's theories are said to be theory-laden.

Observation involves perception as well as a cognitive process. That is, one does not make an observation passively, but is actively involved in distinguishing the thing being observed from surrounding sensory data. Therefore, observations depend on some underlying understanding of the way in which the world functions, and that understanding may influence what is perceived, noticed, or deemed worthy of consideration. More importantly, most scientific observation must be done within a theoretical context in order to be useful. For example, when one observes a measured increase in temperature, that observation is based on assumptions about the nature of temperature and measurement, as well as assumptions about how the thermometer that is used to measure the temperature functions. Such assumptions are necessary in order to obtain scientifically useful observations (such as, "the temperature increased by two degrees"), but they make the observations dependent on these assumptions.

Empirical observation is used to determine the acceptability of some hypothesis within a theory. When someone claims to have made an observation, it is reasonable to ask them to justify their claim. Such a justification must make reference to the theory – operational definitions and hypotheses – in which the observation is embedded. That is, the observation is framed in terms of the theory that also contains the hypothesis it is meant to verify or falsify (though of course the observation should not be based on an assumption of the truth or falsity of the hypothesis being tested). This means that the observation cannot serve as an entirely neutral arbiter between competing hypotheses, but can only arbitrate between the hypotheses within the context of the underlying theory.

Thomas Kuhn denied that it is ever possible to isolate the hypothesis being tested from the influence of the theory in which the observations are grounded. He argued that observations always rely on a specific paradigm, and that it is not possible to evaluate competing paradigms independently. By "paradigm" he meant, essentially, a logically consistent "portrait" of the world, one that involves no logical contradictions and that is consistent with observations that are made from the point of view of this paradigm. More than one such logically consistent construct can paint a usable likeness of the world, but there is no common ground from which to pit two against each other, theory against theory. Neither is a standard by which the other can be judged. Instead, the question is which "portrait" is judged by some set of people to promise the most in terms of scientific “puzzle solving”.

For Kuhn, the choice of paradigm was sustained by, but not ultimately determined by, logical processes. The individual's choice between paradigms involves setting two or more “portraits" against the world and deciding which likeness is most promising. In the case of a general acceptance of one paradigm or another, Kuhn believed that it represented the consensus of the community of scientists. Acceptance or rejection of some paradigm is, he argued, a social process as much as a logical process. Kuhn's position, however, is not one of relativism.^[25] According to Kuhn, a paradigm shift will occur when a significant number of observational anomalies in the old paradigm have made the new paradigm more useful. That is, the choice of a new paradigm is based on observations, even though those observations are made against the background of the old paradigm. A new paradigm is chosen because it does a better job of solving scientific problems than the old one.

That observation is embedded in theory does not mean that observations are irrelevant to science. Scientific understanding derives from observation, but the acceptance of scientific statements is dependent on the related theoretical background or paradigm as well as on observation. Coherentism, skepticism, and foundationalism are alternatives for dealing with the difficulty of grounding scientific theories in something more than observations. And, of course, further, redesigned testing may resolve differences of opinion.

Coherentism

Induction attempts to justify scientific statements by reference to other specific scientific statements. It must avoid the problem of the criterion, in which any justification must in turn be justified, resulting in an infinite regress. The regress argument has been used to justify one way out of the infinite regress, foundationalism. Foundationalism claims that there are some basic statements that do not require justification. Both induction and falsification are forms of foundationalism in that they rely on basic statements that derive directly from immediate sensory experience.

The way in which basic statements are derived from observation complicates the problem. Observation is a cognitive act; that is, it relies on our existing understanding, our set of beliefs. An observation of a transit of Venus requires a huge range of auxiliary beliefs, such as those that describe the optics of telescopes, the mechanics of the telescope mount, and an understanding of celestial mechanics. At first sight, the observation does not appear to be 'basic'.

Coherentism offers an alternative by claiming that statements can be justified by their being a part of a coherent system. In the case of science, the system is usually taken to be the complete set of beliefs of an individual scientist or, more broadly, of the community of scientists. W. V. Quine argued for a Coherentist approach to science, as does E O Wilson, though he uses the term consilience (notably in his book of that name). An observation of a transit of Venus is justified by its being coherent with our beliefs about optics, telescope mounts and celestial mechanics. Where this observation is at odds with one of these auxiliary beliefs, an adjustment in the system will be required to remove the contradiction.

Ockham's razor

The practice of scientific inquiry typically involves a number of heuristic principles that serve as rules of thumb for guiding the work. Prominent among these are the principles of conceptual economy or theoretical parsimony that are customarily placed under the rubric of Ockham's razor, named after the 14th century Franciscan friar William of Ockhamwho is credited with giving the maxim many pithy expressions, not all of which have yet been found among his extant works.^[26]

The motto is most commonly cited in the form "entities should not be multiplied beyond necessity", generally taken to suggest that the simplest explanation tends to be the correct one. As interpreted in contemporary scientific practice, it advises opting for the simplest theory among a set of competing theories that have a comparable explanatory power, discarding assumptions that do not improve the explanation. The "other things being equal" clause is a critical qualification, which rather severely limits the utility of Ockham's razor in real practice, as theorists rarely if ever find themselves presented with competent theories of exactly equal explanatory adequacy.

Among the many difficulties that arise in trying to apply Ockham's razor is the problem of formalizing and quantifying the "measure of simplicity" that is implied by the task of deciding which of several theories is the simplest. Although various measures of simplicity have been brought forward as potential candidates from time to time, it is generally recognized that there is no such thing as a theory-independent measure of simplicity. In other words, there appear to be as many different measures of simplicity as there are theories themselves, and the task of choosing between measures of simplicity appears to be every bit as problematic as the job of choosing between theories. Moreover, it is extremely difficult to identify the hypotheses or theories that have "comparable explanatory power", though it may be readily possible to rule out some of the extremes. Ockham's razor also does not say that the simplest account is to be preferred regardless of its capacity to explain outliers, exceptions, or other phenomena in question. The principle of falsifiability requires that any exception that can be reliably reproduced should invalidate the simplest theory, and that the next-simplest account which can actually incorporate the exception as part of the theory should then be preferred to the first. As Albert Einstein puts it, "The supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience".

Objectivity of observations in science

It is vitally important for science that the information about the surrounding world and the objects of study be as accurate and as reliable as possible. For the sake of this,measurements which are the source of this information must be as objective as possible. Before the invention of measuring tools (like weights, meter sticks, clocks, etc) the only source of information available to humans were their senses (vision, hearing, taste, tactile, sense of heat, sense of gravity, etc.). Because human senses differ from person to person (due to wide variations in personal chemistry, deficiencies, inherited flaws, etc) there were no objective measurements before the invention of these tools. The consequence of this was the lack of a rigorous science.

With the advent of exchange of goods, trades, and agricultures there arose a need in such measurements, and science (arithmetics, geometry, mechanics, etc) based on standardized units of measurements (stadia, pounds, seconds, etc) was born. To further abstract from unreliable human senses and make measurements more objective, science uses measuring devices (like spectrometers, voltmeters, interferometers, thermocouples, counters, etc) and lately - computers. In most cases, the less human involvement in the measuring process, the more accurate and reliable scientific data are. Currently most measurements are done by a variety of mechanical and electronic sensors directly linked to computers—which further reduces the chance of human error/contamination of information. This made it possible to achieve astonishing accuracy of modern measurements. For example, current accuracy of measurement of mass is about 10⁻¹⁰, of angles—about 10⁻⁹, and of time and length intervals in many cases reaches the order of 10⁻¹³ - 10⁻¹⁵. This made possible to measure, say, the distance to the Moon with sub-centimeter accuracy (see Lunar laser ranging experiment), to measure slight movement of tectonic plates using GPS system with sub-millimeter accuracy, or even to measure as slight variations in the distance between two mirrors separated by several kilometers as 10⁻¹⁸ m—three orders of magnitude less than the size of a single atomic nucleus—see LIGO.

Philosophy of particular sciences

In addition to addressing the general questions regarding science and induction, many philosophers of science are occupied by investigating philosophical or foundational problems in particular sciences. The late 20th and early 21st century has seen a rise in the number of practitioners of philosophy of a particular science.

(WIKEPEDIA EXCERPT)