ⓘ Models of scientific inquiry. In the philosophy of science, models of scientific inquiry have two functions: first, to provide a descriptive account of how scie ..

                                     

ⓘ Models of scientific inquiry

In the philosophy of science, models of scientific inquiry have two functions: first, to provide a descriptive account of how scientific inquiry is carried out in practice, and second, to provide an explanatory account of why scientific inquiry succeeds as well as it appears to do in arriving at genuine knowledge.

The search for scientific knowledge ends far back into antiquity. At some point in the past, at least by the time of Aristotle, philosophers recognized that a fundamental distinction should be drawn between two kinds of scientific knowledge - roughly, knowledge that and knowledge why. It is one thing to know that each planet periodically reverses the direction of its motion with respect to the background of fixed stars; it is quite a different matter to know why. Knowledge of the former type is descriptive; knowledge of the latter type is explanatory. It is explanatory knowledge that provides scientific understanding of the world. Salmon, 2006, pg. 3

"Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work."

                                     

1.1. Accounts of scientific inquiry Classical model

The classical model of scientific inquiry derives from Aristotle, who distinguished the forms of approximate and exact reasoning, set out the threefold scheme of abductive, deductive, and inductive inference, and also treated the compound forms such as reasoning by analogy.

                                     

1.2. Accounts of scientific inquiry Logical empiricism

Wesley Salmon 1989 began his historical survey of scientific explanation with what he called the received view, as it was received from Hempel and Oppenheim in the years beginning with their Studies in the Logic of Explanation 1948 and culminating in Hempels Aspects of Scientific Explanation 1965. Salmon summed up his analysis of these developments by means of the following Table.

In this classification, a deductive-nomological D-N explanation of an occurrence is a valid deduction whose conclusion states that the outcome to be explained did in fact occur. The deductive argument is called an explanation, its premisses are called the explanans L: explaining and the conclusion is called the explanandum L: to be explained. Depending on a number of additional qualifications, an explanation may be ranked on a scale from potential to true.

Not all explanations in science are of the D-N type, however. An inductive-statistical I-S explanation accounts for an occurrence by subsuming it under statistical laws, rather than categorical or universal laws, and the mode of subsumption is itself inductive instead of deductive. The D-N type can be seen as a limiting case of the more general I-S type, the measure of certainty involved being complete, or probability 1, in the former case, whereas it is less than complete, probability < 1, in the latter case.

In this view, the D-N mode of reasoning, in addition to being used to explain particular occurrences, can also be used to explain general regularities, simply by deducing them from still more general laws.

Finally, the deductive-statistical D-S type of explanation, properly regarded as a subclass of the D-N type, explains statistical regularities by deduction from more comprehensive statistical laws. Salmon 1989, pp. 8–9.

Such was the received view of scientific explanation from the point of view of logical empiricism, that Salmon says "held sway" during the third quarter of the last century Salmon, p. 10.

                                     

2. Choice of a theory

During the course of history, one theory has succeeded another, and some have suggested further work while others have seemed content just to explain the phenomena. The reasons why one theory has replaced another are not always obvious or simple. The philosophy of science includes the question: What criteria are satisfied by a good theory. This question has a long history, and many scientists, as well as philosophers, have considered it. The objective is to be able to choose one theory as preferable to another without introducing cognitive bias. Several often proposed criteria were summarized by Colyvan. A good theory:

  • Agrees with and explains all existing observations unificatory/explanatory power
  • Is elegant Formal elegance; no ad hoc modifications
  • Contains few arbitrary or adjustable elements simplicity/parsimony
  • Makes detailed predictions about future observations that can disprove or falsify the model if they are not borne out.
  • Is fruitful: the emphasis by Colyvan is not only upon prediction and falsification, but also upon a theorys seminality in suggesting future work.

Stephen Hawking supported items 1–4, but did not mention fruitfulness. On the other hand, Kuhn emphasizes the importance of seminality.

The goal here is to make the choice between theories less arbitrary. Nonetheless, these criteria contain subjective elements, and are heuristics rather than part of scientific method. Also, criteria such as these do not necessarily decide between alternative theories. Quoting Bird:

"They cannot determine scientific choice. First, which features of a theory satisfy these criteria may be disputable e.g. does simplicity concern the ontological commitments of a theory or its mathematical form?. Secondly, these criteria are imprecise, and so there is room for disagreement about the degree to which they hold. Thirdly, there can be disagreement about how they are to be weighted relative to one another, especially when they conflict."

It also is debatable whether existing scientific theories satisfy all these criteria, which may represent goals not yet achieved. For example, explanatory power over all existing observations criterion 3 is satisfied by no one theory at the moment.

Whatever might be the ultimate goals of some scientists, science, as it is currently practiced, depends on multiple overlapping descriptions of the world, each of which has a domain of applicability. In some cases this domain is very large, but in others quite small.

The desiderata of a "good" theory have been debated for centuries, going back perhaps even earlier than Occams razor, which often is taken as an attribute of a good theory. Occams razor might fall under the heading of "elegance", the first item on the list, but too zealous an application was cautioned by Albert Einstein: "Everything should be made as simple as possible, but no simpler." It is arguable that parsimony and elegance "typically pull in different directions". The falsifiability item on the list is related to the criterion proposed by Popper as demarcating a scientific theory from a theory like astrology: both "explain" observations, but the scientific theory takes the risk of making predictions that decide whether it is right or wrong:

"It must be possible for an empirical scientific system to be refuted by experience."

"Those among us who are unwilling to expose their ideas to the hazard of refutation do not take part in the game of science."

Thomas Kuhn argued that changes in scientists views of reality not only contain subjective elements, but result from group dynamics, "revolutions" in scientific practice which result in paradigm shifts. As an example, Kuhn suggested that the heliocentric "Copernican Revolution" replaced the geocentric views of Ptolemy not because of empirical failures, but because of a new "paradigm" that exerted control over what scientists felt to be the more fruitful way to pursue their goals.



                                     

3.1. Aspects of scientific inquiry Deduction and induction

Deductive logic and inductive logic are quite different in their approaches.

                                     

3.2. Aspects of scientific inquiry Deduction

Deductive logic is the reasoning of proof, or logical implication. It is the logic used in mathematics and other axiomatic systems such as formal logic. In a deductive system, there will be axioms postulates which are not proven. Indeed, they cannot be proven without circularity. There will also be primitive terms which are not defined, as they cannot be defined without circularity. For example, one can define a line as a set of points, but to then define a point as the intersection of two lines would be circular. Because of these interesting characteristics of formal systems, Bertrand Russell humorously referred to mathematics as "the field where we dont know what we are talking about, nor whether or not what we say is true". All theorems and corollaries are proven by exploring the implications of the axiomata and other theorems that have previously been developed. New terms are defined using the primitive terms and other derived definitions based on those primitive terms.

In a deductive system, one can correctly use the term "proof", as applying to a theorem. To say that a theorem is proven means that it is impossible for the axioms to be true and the theorem to be false. For example, we could do a simple syllogism such as the following:

  • Arches National Park lies within the state of Utah.
  • I am standing in Arches National Park.
  • Therefore, I am standing in the state of Utah.

Notice that it is not possible assuming all of the trivial qualifying criteria are supplied to be in Arches and not be in Utah. However, one can be in Utah while not in Arches National Park. The implication only works in one direction. Statements 1 and 2 taken together imply statement 3. Statement 3 does not imply anything about statements 1 or 2. Notice that we have not proven statement 3, but we have shown that statements 1 and 2 together imply statement 3. In mathematics, what is proven is not the truth of a particular theorem, but that the axioms of the system imply the theorem. In other words, it is impossible for the axioms to be true and the theorem to be false. The strength of deductive systems is that they are sure of their results. The weakness is that they are abstract constructs which are, unfortunately, one step removed from the physical world. They are very useful, however, as mathematics has provided great insights into natural science by providing useful models of natural phenomena. One result is the development of products and processes that benefit mankind.

                                     
  • being represented. Such computer models are in silico. Other types of scientific models are in vivo living models such as laboratory rats and in vitro
  • principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises. Though diverse models for the
  • Instrumental and intrinsic value Logic of information Models of scientific inquiry Pragmatic information Pragmatic theory of truth Pragmaticism Research Uncertainty
  • Inquiry - based learning also enquiry - based learning in British English is a form of active learning that starts by posing questions, problems or scenarios
  • and the process of scientific inquiry The community of inquiry is broadly defined as any group of individuals involved in a process of empirical or conceptual
  • Progressive inquiry is a pedagogical model which aims at facilitating the same kind of productive knowledge practices of working with knowledge in education
  • Model - dependent realism is a view of scientific inquiry that focuses on the role of scientific models of phenomena. It claims reality should be interpreted
  • that encouraged evidence - based inquiry into scientific medical, technological and paranormal claims using scientific scepticism. CASS conducts research
  • works in philosophy of science written after World War II in The Cambridge Dictionary of Philosophy 2015 Models of scientific inquiry Gay 1988, pp. 236 237