This is Philosophy of Science

An Introduction

Franz-Peter Griesmaier, Jeffrey A. Lockwood, Steven D. Hales

• Publisher: Wiley-Blackwell
• Year: 2022
• Print ISBN:
  9781119757993
• eBook ISBN:
  9781119758006
• Edition: 1
• Topic: Philosophy
• Subtopic: Philosophy History & Theory

A clear and engaging introduction to the philosophy of science, exploring the role of science within the broader framework of human knowledge and engagement with the world

What are the central features and advantages of a scientific worldview? Why do even reasonable scientists sometimes disagree with each other? How are scientific methods different than those of other disciplines? Can science provide an objective account of reality? This is Philosophy of Science introduces the most important philosophical issues that arise within the empirical sciences.

Requiring no previous background in philosophy, this reader-friendly volume covers topics ranging from traditional questions about the nature of explanation and the confirmation of theories to practical issues concerning the design of physical experiments and modeling. Incisive and accessible chapters with relevant case-studies and informative illustrations examine the function of thought experiments, discuss the realism/anti-realism debate, explore probability and theory testing, and address more challenging topics such as emergentism, measurement theory, and the manipulationist account of causation.

• Describes key philosophical concepts and their application in the empirical sciences
• Highlights past and present philosophical debates within the field
• Features numerous illustrations, real-world examples, and references to additional resources
• Includes a companion website with self-assessment exercises and instructor-only test banks

Part of Wiley-Blackwell's popular This Is Philosophy series, This is Philosophy of Science: An Introduction is an excellent textbook for STEM students with interest in the conceptual foundations of their disciplines, undergraduate philosophy majors, and general readers looking for an easy-to-read overview of the subject.

1
PILLARS OF SCIENCE: REASONS, KNOWLEDGE, AND TRUTH

The empirical sciences are dedicated to describing, explaining, and predicting natural phenomena, such as ionic bonding, predation rates, formation of galaxies, bird migrations, gravitational waves, protein metabolism, or the probability of an earthquake in northern California. The ultimate goal is to gain genuine knowledge about such phenomena – what exactly they are, what regularities govern their occurrence, why and how they occur, when they might happen in the future, and with what probability.

In subsequent chapters, we will investigate many of the difficult and fascinating issues involved in a successful exploration of the natural world. Some of those issues arise with equal force in different sciences, while others may arise in only one discipline, but not in any of the others, at least not in the same form. For now, however, we are going to discuss some of the concepts and procedures that arguably can be found in all empirical sciences. These include (i) the fundamental nature of reasons we give for accepting or rejecting a scientific hypothesis, or any ordinary claim about the world for that matter; (ii) the general structure of the different kinds of inferences we can make on the basis of observations; (iii) the nature of truth and the specter of relativism; and (iv) the relation between facts, hypotheses, laws, and theories. Having a good grasp of these concepts will prove invaluable for understanding the intricacies of empirical science and the philosophical problems that arise in the course of scientific investigations.

The initial discussion will be a bit on the abstract side, and you might at first find it puzzling how questions such as the ones we are going to raise in this chapter are of any relevance to the practice of science. The situation is perhaps similar to learning the rules of baseball before swinging a bat. Sure, you might hit the ball if you don’t know the how the game is played, but you might well run in the wrong direction afterward! And so, as our discussion proceeds in the later chapters, you will find that having gained mastery of some fundamental concepts in epistemology (the study of knowledge) is of great value. So let’s get going.

1.1 Epistemic Reasons

Typically, when we wonder whether we should accept some claim, such as a scientific hypothesis, or not, we look for reasons for doing so. Another way of putting this is that we don’t believe something without having reasons. For example, if someone asks you to accept that there are intelligent, extraterrestrial life forms, you’re likely to ask for reasons before you adopt this belief. And if the other person just hems and haws, you’re not going to believe in extraterrestrials. On the other hand, if you are presented with the cosmic background radiation as evidence for the occurrence of the so-called Big Bang, you have some reason for believing that the universe emerged through this sort of process. (Notice that when we use the term “belief,” we do not mean to talk about religious faith.)

Of course, not any old reason is a good reason. If I believe something because I would like it if it were true, or because I am better off believing it, the belief might well turn out to be false – and in the overwhelming number of cases, it will be. Wishful thinking rarely leads to true beliefs. Thus, we need another kind of reason for believing something if we want to find out the truth about the world.

Reasons of the desired kind are called epistemic reasons. They are the sort of reasons that allow us to accept a belief only if there is good evidence for its truth, or only if the belief doesn’t contradict other, already well-established beliefs derived from good evidence. Of course, it is very contentious what makes for a good epistemic reason. The debate over which (types of) epistemic reasons are to be preferred over others constitutes part of what’s called epistemology, or the study of knowledge. Epistemic reasons are usually divided into two kinds: those which guarantee, in a sense to be specified momentarily, the truth of their target beliefs, and those that merely make the truth more probable. We start with the former.

1.1.1 Conclusive Reasons

The first type of epistemic reasons are called conclusive reasons. A reason (R) is conclusive for some belief (B) if and only if the belief B must be true if R is true. And this condition holds even if there is not just one reason for B, but also in cases in which B rests on many reasons. In more general terms, if all the reasons for a belief are true, and if they are conclusive reasons, then their target belief must be true. Conclusive reasons guarantee true beliefs, which is strongest basis one can have for believing something. So, how can we understand this definition?

A good example for conclusive reasons are the premises of a deductively valid argument. In such an argument, if all the premises are true, then the conclusion must be true as well. Here’s a simple example:

Premise 1:	All humans are mortal.
Premise 2:	Stephen Hawking is human.

Conclusion: Thus, Stephen Hawking is mortal.

Clearly, if premises 1 and 2 are both true, then the conclusion is guaranteed to be true. Thus, the two premises together are conclusive reasons for believing that Hawking is mortal. But this sort of reasoning is not often helpful for advancing our scientific understanding of the world. Let’s see why.

Notice that in a deductive argument, what’s really happening is that information that is already contained implicitly in the premises, is made explicit in the conclusion. In other words, the conclusion does not reveal any new information. It restates the information that’s already contained in the premises. That’s why such inferences are safe: truth in – truth out.

Deductive reasoning (i.e., reasoning that proceeds by providing conclusive reasons) is mostly confined to two major disciplines: mathematics and logic. Yes, sometimes we use deductive reasoning in the empirical sciences, such as in cases in which we deduce observational consequences from a theory in order to test it (which can include refuting the theory):

Premise 1 (Theory 1)	All birds can fly.
Premise 2 (Theory 2)	Penguins are birds.
Premise 3 (Deduced Consequence)	Penguins can fly.
Premise 4 (Observation):	Penguins can’t fly.
Conclusion:	Not all birds can fly.

However, a lot of scientific reasoning is nondeductive. Why? Because typically, in scientific reasoning, we want to infer something about the world at large on the basis of a limited number of observations. Such inferences are inherently risky because their conclusions convey information that goes beyond the information contained in the descriptions of the actual, limited observations that have been made.

For example, if I infer, on the basis of having observed the eating habits of 20 koalas, that all koalas eat eucalyptus leaves, I make such a risky inference. I assume, among other things, that the koalas I observed are typical of their species. This assumption could easily be wrong, as I might have come across a peculiar band of koalas that happen to consume eucalyptus. That such inferences are risky, however, doesn’t show that they are altogether unreasonable. The conditions under which they are reasonable are somewhat difficult to pin down, and we will tackle this challenge in the next section.

Now, given that reasoning nondeductively is risky, and that the conditions of its reasonableness are somewhat elusive, one might think that science should aim at just using deductive inferences, precisely because they are safe – even certain. But that would be a mistake. Remember: They are safe because in an important sense, they are uninformative. Since there is no new information in a deductive conclusion that was not already implicitly contained in the premises, deductive inferences won’t allow you to gain more information about the world by reasoning from your evidence. To accomplish this, we need to go beyond an obsession with certainty, which is provided by conclusive reasons and reasoning, and enlarge our toolbox. The tools we need, especially for the empirical sciences, are various forms of defeasible reasoning, and thus defeasible reasons.

1.1.2 Defeasible Reasons

The second, and much more common, type of epistemic reasons are called defeasible reasons. They are also sometimes called probable, or prima facie reasons. The main difference between these and conclusive reasons is that even true defeasible reasons don’t guarantee the truth of their target belief. Consider this example:

You are near the mouth of a cave looking at a rock formation just inside the cave. The formation looks red to you. This “red-looking” is a good (defeasible) reason for believing that the rocks are red. However, as we all know, lighting conditions vary in natural settings and can be deceptive. Thus, it could be the case that the rock formation isn’t really red; its red appearance could be produced by weird lighting filtering into the cave. Thus, although the red-appearance of the formation is a good defeasible reason for believing it to be red, the truth of this latter belief is not guaranteed.

What defeasible reasons do is to make the truth of the belief for which they are reasons probable. (That’s why they are also called probable reasons.) A red appearance of a rock formation makes it more probable that the formation is red than that it is not. Of course, you could acquire a further bit of information which defeats the strength of the reason (that’s why they are called defeasible reasons). For example, you could notice that there is a brilliant sunset outside the cave, which makes it likely that many even nonred things look red. Thus, the fact that the formation looks red to you is no longer a very good reason for believing that it is actually red, given that many nonred things will seem to be red in these lighting conditions.

There are actually two recognized kinds of defeating information, or defeaters: so-called rebutting defeaters and undercutting defeaters. In the example just given, noticing that there is a sunset is an undercutting defeater. It undercuts the evidential force of your original reason for believing that the formation is red. Given that ...

Table of contents

• Cover
• Series page
• Title page
• Copyright
• Table of Contents
• Preface
• Acknowledgments
• About the Companion Website
• 1 Pillars of Science: Reasons, Knowledge, and Truth
• 2 Evidence, Observation, and Measurement
• 3 Uses of Evidence
• 4 Evidence, Rationality, and Disagreement
• 5 The Nature of Probability
• 6 Do Not Be Misled: Confounds and Controls
• 7 Physical Experiments and Their Design
• 8 Experimental Methods That They Don’t Teach
• 9 Models: Useful Lies and Informative Fictions
• 10 Causation and Causal Inference
• 11 Strange Causation – Time Travel and Remote Action
• 12 But Is Any of It Real?
• 13 Explanation and Understanding
• 14 Explanation and Understanding
• 15 Scientific Progress
• Index
• End User License Agreement