Doing Science

In the Light of Philosophy

Mario Augusto Bunge

• Publisher: WSPC
• Year: 2017
• ISBN:
  9789813202795
• Topic: Philosophy
• Subtopic: Philosophy History & Theory

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Nearly all philosophers have dealt with the outcomes of scientific research, and have overlooked its philosophical presuppositions, such as those of rationality and realism. Although these presuppositions are mostly tacit and thus easily overlooked, actually they are supremely important, since some of them favor research whereas others hamper it. For instance, whereas subjectivism leads to navel gazing and uncontrolled fantasy, realism encourages us to explore the world and check our conjectures.

This book examines science in the making, a process it illustrates with many examples from the natural, social, and biosocial sciences. Therefore it centers on the research process and its philosophical presuppositions. It claims that the latter constitutes a sort of matrix for conceiving and nurturing scientific projects.

--> Author Mario Augusto Bunge 0Scientific Research, Philosophy, Rationality, Realism, Science in the Making0

CHAPTER 1

IN THE BEGINNING WAS THE
PROBLEM

To engage in research of any kind is to work on a problem or a cluster of problems of some kind — cognitive, technological, social, artistic, or moral. In imitation of John’s gospel, we may say that in the beginning was the problem. So, those wishing to start doing science must find or invent a problem to work on, as well as a mentor willing to guide them.

1.1At the Source

Free agents prefer to work on problems they like and feel are equipped to tackle. But of course most budding scientists are not fully free to choose: their supervisors or employers will assign them their tasks — for one does not know one’s own ability before trying and, above all, because finding a suitable problem is the first and hardest step.

However, problem choice is only part of a whole package, which includes also such noncognitive items as advisor’s suitability and availability, research facilities, and financial assistance. In other words, the budding scientist or technologist does not enjoy the luxury of picking his/her favorite problem — which is just as well because, given his/her inexperience, that choice is likely to be either too ambitious or too humble. In sum, aspiring investigators are given to choose among a set of packages offered by his/her prospective advisor or employer.

For better or for worse, there are no recipes or algorithms for generating problems other than reviewing the recent literature. In particular, computers cannot pose problems, for they are designed, built, and sold to help solve well-posed problems, such as curve fitting a given set of data points. After listening to Stanislav Ulam’s panegyric of the abilities of computers, I left him speechless by asking him, at a congress packed with sages, whether such marvels might invent new problems. He paused for a long while and finally admitted that this question had never occurred to him. Such is the power of raw data and data-processing devices.

Half a century ago, Alan Turing proposed the test that bears his name as the way to discover whether one’s interlocutor is a human or a robot. Later work in AI showed that Turing’s test is not foolproof. There is an alternative: ask your interlocutor to pose a new and interesting question. Computers will fail this test, because they are designed to operate on algorithms, not to deal with questions that demand invention, in particular problems, such as guessing intention from behavior. This test is therefore one about natural intelligence, or thinking out of the digital box.

1.2Types of Problems

The logical positivists like Philipp Frank, as well as their critic Karl Popper, banned questions of the “What-is-it?” type. By contrast, the great physiologist Ivan Pavlov (1927:12) held that they exemplify what he called the investigatory reflex, for they elicit an animal’s response to environmental changes. Indeed, they constitute existential dilemmas, hence the most basic of all, for they include “Friend or foe?,” “Safe or risky?,” “Edible or inedible?,” and the like.

Admittedly, only humans and apes capable of communicating with us via sign language or computers will formulate problems in a sentence-like fashion. But this is a moot point: what matters mostly is that the animals that do not solve their existential dilemmas are unlikely to survive — unless they are tenured philosophy professors.

The importance of problems in all walks of life is such, that someone said that living is basically tackling problems. For those who have solved the subsistence problem, to live is to fall in and out of love with cognitive, valuational, or moral problems. Those of us who ask Big Questions, such as “How and why did civilization start?” are called bold scientists. And the few who ask the biggest questions of all, such as “What exists by itself?,” “What is truth?,” and “Is science morally neutral?” are called philosophers. These and similar questions are transdisciplinary, whereas all the others are unidisciplinary.

1.3Erotetics

Most philosophers have overlooked problems and their logic, namely erotetics, which should be the subject of countless original philosophical research projects. The next few pages will recall the author’s erotetics discussed in what is likely to have been the first treatise in the philosophy of science to sketch it (Bunge 1967b, vol. 1).

Whatever the kind of cognitive problem, we may distinguish the following aspects of it: (a) the statement of the problem regarded as a member of a particular epistemological category; (b) the act of questioning — a psychological subject; and (c) the expression of the problem by a set of interrogatives or imperatives (the linguistic aspect). In the present section we shall focus on the first of these aspects.

From an action-theoretic viewpoint, a problem is the first link of a chain: Problem — Search — Solution — Check. From a logical point of view, the first link may be analyzed into the following quadruple: background, generator, solution (in case it exists), and control — or BGSC for short.

Let us clarify the preceding by way of a “hot” example in astrophysics, namely “What is dark matter?” We start by reformulating the given problem as “Which are the properties P of the Ds?,” or (?P)Px, where x designates an arbitrary member of the class D of all possible pieces of dark matter, and P a conjunction of known and new physical properties. The BGSC components of this particular problem are

Background B = Contemporary astrophysics plus particle physics.

Generator G = Px, where P = a conjunction of first-order properties.

Solution S = The cluster P of properties assignable to any D.

Control C = The laboratory analysis of a piece of dark matter or of the radiation (other than light) that it emits.

Let us close by listing the elementary problem forms.

Which-problems Which is (are) the x such that Px? (?x)Px

What-problems Which are the properties of item c? (?P)Pc

How-problems How does c, which is an A, happen? (?P)[Ac ⇒ Pc]

Why-problems Which is the p such that q? (?p)(p ⇒ q)

Whether-problems What is the truth-value of p? (?v)[V(p) = v]

Inverse problems Given B and A → B, find A. (A?)[A→B]

The direct/inverse distinction may be summarized thus:

In the simplest case, the input–output relation is functional, and it can be depicted as follows:

However, most real-life problems of are of the means-end kind, most of which have multiple solutions, so they are not functional.

Whereas direct problems are downstream, or from either causes or premises to effects or conclusions, the inverse ones are upstream, or from effects or theorems to causes or premises. A common inverse problem is that of conjecturing a probability distribution from statistics such as average and mean standard deviation. A far less common inverse problem is the axiomatization of a theory known in its ordinary untidy version (see Chapter 7).

Like most inverse problems, axiomatics has multiple solutions. The choice among them is largely a matter of convenience, taste, or philosophy. For example, whereas an empiricist is likely to start with electric current densities and field intensities, the rationalist is likely to prefer starting with current densities and electromagnetic potentials, because the latter imply the field intensities (see Bunge 2014; Hilbert 1918).

All the prognosis problems, whether in medicine or elsewhere, are direct, whereas the diagnostic ones are inverse. For example, having diagnosed a patient as suffering from a given disease on the strength of a few symptoms, checking for the occurrence of further symptoms is a direct problem. But the problem of medical diagnosis is inverse, hence far harder, for it consists in guessing the disease from some of its symptoms.

Ordinary logic and computer algorithms have been designed to handle direct problems. Inverse problems require inventing ad-hoc tricks, and such problems have multiple solutions or none. For example, whereas 2 + 3 = 5, the corresponding inverse problem of analyzing 5 into the sum of two integers has four solutions.

Inverse problems may be restated thus: given the output of a system, find its input, mechanism of action, or both. That is, knowing or guessing that A → B, as well as the output B of a system, find its input A or the mechanism M that converts A into B. For example, given a proposition, find the premises that entail it; design an artifact that will produce a desired effect; and given the beam of particles scattered by an atomic nucleus, guess the latter’s composition, as well as the nature of the scattering force (For the pitfalls of this task see, e.g., Bunge 1973a).

A fever may be due to umpteen causes, and its cure may be achieved through multiples therapies, which is why both biomedical research and medical practice are so hard (see Bunge 2013). As a matter of fact, most inverse problems are hard because there are no algorithms for tackling them. This is why most philosophers have never heard of them. The referees of my first philosophical paper on the subject rejected it even while admitting that they had never encountered the expression ‘inverse problem’ (Bunge 2006).

Finally let us ask whether there are insoluble problems. Around 1900 David Hilbert stated his conviction that all well-posed mathematical problems are soluble in principle, not just unsolved up to now. Here we shall disregard unsolvable mathematical problems because they are arcane questions in the foundations of mathematics, and anyway they have raised no philosophical eyebrows. We shall confine ourselves to noting that some seemingly profound philosophical problems are ill posed because they presuppose a questionable background.

The oldest and most famous of them is, “Why is there something rather than nothing?” Obviously, this question makes sense only in a theodicy that supposes that the Deity, being omnipotent, had the power of inaction before setting out to build the universe: why bother with real existents if He could spend all eternity in leisure? Taken out of its original theological context, the said question is seen to be a pseudoproblem, hence not one that will kindle a scientific research project. In a secular context we take the existence of the world for granted, and ask only particular existence problems, such as “Why do humans have nails on their toes?,” which is asked and answered by evolutionary biologists. The answer is of course that toenails descend from the fingernails that our remote ancestors had on their hind legs, which worked as hands.

Yet it is often forgotten that all problems are posed against some context, and that they vanish if the context is shown to be wrong. Let us recall a couple of famous games of this kind.

Pseudoproblem 1: What would happen if suddenly all the distances in the universe were halved? The answer is in two parts: (a) nothing at all would happen, for all distances are relative, in particular relative to some length standard, which would also shrink along with everything else; and (b) since no universal shrinkage mechanism is known in physics, the said event should be regarded as miraculous, hence conceivable but physically impossible.

Pseudoproblem 2: What is the probability that the next bird we spot is a falcon, or that the next person we meet is the pope? Answer: neither belonging to a given biospecies nor holding a particular office are random events, so the given questions should be completed by adding the clause “picked at random from a given population (of birds or people respectively).” No randomness, no applied probability. In conclusion, unless the background of a question is mentioned explicitly, it won’t start a research project.

A final warning: genuine cognitive problems are not word games played just to exercise or display wit. The best known of these games is perhaps the Liar Paradox, generated by the sentence ‘This sentence is false.’ If the sentence is true, then it is false; but if it is false, then it is true.

The paradox dissolves either on noticing that the sentence in question conflates language with metalanguage; or that it does not express a proposition, for propositions have definite truth-values.

The first interpretation warns against such confusions, and the second reminds us that only propositions can be assigned truth values, whence it is wrong to call first-order logic ‘sentential calculus’, the way nominalists do just because of their suspicion of unobservables. In sum, avoid barren paradoxes when stating a cognitive problem, for truth is not a toy.

1.4The Search for Research Problems

How does one find a suitable research problem? The answer depends on the kind of problem: is it a matter of survival, like finding the next meal ticket; a technological problem, such as increasing the efficiency of an engine; a moral problem, such as how to help someone; or an epistemic problem, such as to discover how dark holes arise or evolve?

The question of problem choice has mobilized psychologists, historians, and sociologists. These experts have attacked what Thomas Kuhn (1977) called ‘the essential tension.’ This is the choice between a potboiler that may inflate the investigator’s CV but won’t alter anyone else’s sleep, and a risky adventure with an uncertain outcome that may alter an important component of the prevailing worldview, as was the case when Michael Faraday assumed that electric charges and currents, as well as magnets, interact via massless fields rather than directly.

Familiar examples of the first kind are sp...

Table of contents

• Cover
• Halftitle
• Title
• Copyright
• Preface
• Contents
• Introduction
• Chapter 1 In the Beginning was the Problem
• Chapter 2 Scientific Research Projects
• Chapter 3 Evaluation of Results
• Chapter 4 Science and Society
• Chapter 5 Axiomatics
• Chapter 6 Existences
• Chapter 7 Reality Checks
• Chapter 8 Realisms
• Chapter 9 Materialisms: From Mechanism to Systemism
• Chapter 10 Scientism
• Chapter 11 Technology, Science, and Politics
• Appendix 1 Freeing Free Will: A Neuroscientific Perspective
• Appendix 2 The Philosophy of Mind Needs a Better Metaphysics
• References
• Index