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Has Laudan Killed the ?

Kirsten Walsh Bachelor of (Hons) (Melb)

Submitted in partial fulfilment of the requirements of the degree of Master of Arts (with Advanced Seminars component)

History and of The University of Melbourne Australia

October 2009

Produced on archival quality paper.

The „Demarcation Problem‟ is to mark the boundary between things that are scientific and things that are not. Philosophers have worked on this problem for a long , and yet there is still no consensus solution. Should we continue to hope, or must we draw a more sceptical conclusion? In his paper, „The Demise of the Demarcation Problem‟, (1983) does the latter. In this thesis, I address the three he gives for this conclusion.

The Pessimistic Induction: From the of many specific past attempts at demarcation, Laudan infers that all attempts at demarcation fail. For his to be fully convincing, Laudan needs to show that each attempt has been a complete failure, and that these have never led to in the of demarcation. I argue that many past attempts at demarcation have only resulted in partial failure, and many of these failures have led to some cumulative progress. So I think we can draw a more optimistic conclusion: future attempts at demarcation may be even more successful than past attempts.

The Pseudo-Problem: Laudan argues that the demarcation problem presupposes an „epistemic invariant‟: something common to all and only the , which makes them epistemically special. But, says Laudan, this presumption is false – so, by definition, the issue is merely a pseudo-problem. I find Laudan‟s argument unconvincing. I present for thinking that the demarcation problem does not, in , presuppose an extremely simple epistemic invariant. Furthermore, there may still be a satisfactory, moderately complex epistemic invariant to be found. So I do not think any false assumption is presupposed.

The New Problem: Laudan argues that we should replace the original demarcation problem with a new demarcation problem. I take this to be the problem of demarcating between well-confirmed and ill-confirmed . I argue that scientific status is relevant to the confirmation of theories, so the two problems are closely related. I also argue that science has other purposes; so scientific status indicates other besides well- confirmedness. Thus we do want to know which theories and activities are scientific, because this will help us to decide which theories and activities to pursue. So this new demarcation problem is not a suitable replacement for the original problem.

My central question is „Has Laudan killed the demarcation problem?‟, and my answer is „No!‟.

1 Declaration

This is to certify that

(i) The thesis comprises only my original work except where indicated in the Preface;

(ii) Due acknowledgement has been made in the text to all other material used;

(iii) The thesis is 20,000-22,000 words in , inclusive of footnotes but exclusive of tables, maps, appendices and bibliography.

Kirsten Walsh

2 Acknowledgements

This thesis is the culmination of my academic journey thus far. Starting out as a vague question, „Is the demarcation problem worth solving?‟, it evolved into its present form. I would like to thank a of people who have contributed to the final result in many different ways:

First and foremost, I sincerely thank my long-suffering supervisor Howard Sankey, who has ploughed through various drafts, making critical suggestions and posing challenging questions. He has motivated and encouraged me every step of the way – never reproaching me when the necessity of doing paid work got in the way of progress. For this, I am extremely grateful.

Special thanks go to Neil Thomason for inspiring me in the early stages, encouraging me to present my work at conferences, and for an entire draft of my thesis during a flight to the US! I also thank Jason Grossman for his kind comments and helpful criticisms of Chapter One.

This thesis evolved into its present form during a summer I spent at the University of Auckland under the supervision of Nola who went above and beyond the call of duty. I thank him not only for his helpful discussions and feedback, but also for showing me around Auckland and the surrounds. I also thank Jan Crosthwaite, Rosalind Hursthouse, and the University of Auckland for providing me with the Summer Scholarship that made this work possible.

For providing numerous distractions, as well as their unfailing support, I thank the and (HPS) and Philosophy postgrads with whom I have shared office , morning tea, and jugs of beer. In particular I‟d like to thank: Conrad Asmus for teaching me formal and reading a draft of my thesis; Alex Murphy for insisting that I use correct grammar and punctuation; Tama Coutts for interested in everything philosophical; David Condylis for insisting that I should never trust a philosopher named „Larry‟; Ned Taylor for telling me what do; Suzy Killmister for securing office space; and Kristian Camilleri and Steph Lavau for proving that theses can be finished. Special mention goes to the participants of „Help! My Progress has Stalled!‟ and „Operation Endless Victory‟: Aaron Retz, Bryan Cooke, Chris Soeterboek, Paul Carter, Sergio Mariscal, Steph Lavau, Steven Kambouris, and Vicki Macknight – I hope we are all victorious in the end!

3 A very special thank you to Erik Nyberg, my partner, colleague and friend. He always supported and encouraged me, stayed interested in the details of my interminable project, and made many helpful suggestions – especially by playing the devil‟s advocate during the editing of my final draft.

Finally, I thank my parents, Adrian and Meredith Walsh, who have offered their unconditional support and gentle counsel at every turn of the road. Their foresight and values paved the way for my privileged .

I dedicate this thesis to Larry Laudan, for all the time we‟ve spent together.1

1 I haven‟t met Professor Laudan yet, but I hope to meet him one day!

4 Table of Contents

Abstract ...... 1

Declaration ...... 2

Acknowledgements ...... 3

Table of Contents ...... 5

Table of Figures ...... 8

0 Introduction ...... 9

0.1 What is Demarcation? ...... 9

0.2 Laudan’s Three Sceptical Arguments ...... 11

1 The Pessimistic Induction ...... 13

1.1 Laudan’s Pessimistic Induction ...... 13

1.2 Inductive ...... 16

1.3 The Resemblance Assumption should be Rejected ...... 19

1.4 Progress in the Philosophy of Method ...... 20

1.5 Reply: Changes ...... 23

1.6 Reply: The New Tradition is not Epistemic ...... 27

1.7 Rejoinder: is Epistemic ...... 28

1.8 Rejoinder: The New Tradition is Progressive ...... 29

1.9 Conclusion ...... 32

2 The Pseudo-Problem ...... 34

2.1 Laudan’s Requirements and Pseudo-Problem...... 34

2.2 Requirement One: Accuracy ...... 36 2.2.1 Objection: Demarcations can be legislative ...... 36 2.2.2 Sub-conclusion: Reasonable accuracy is sufficient ...... 37

2.3 Requirement Two: Precision ...... 37 2.3.1 Objection: Precise enough for the specific purpose ...... 38 2.3.2 Objection: Ordinary could be replicated ...... 39 2.3.3 Sub-conclusion: Moderate precision is sufficient ...... 40

2.4 Requirement Three: Epistemic Superiority ...... 40 2.4.1 Objection: Only epistemic significance ...... 41 5 2.4.2 Objection: Indirect epistemic virtues ...... 42 2.4.3 Reply: Too many virtues ...... 43 2.4.4 Reply: Too unreliable ...... 44 2.4.5 Sub-conclusion: Indirect epistemic virtues are sufficient ...... 45

2.5 Requirement Four: Invariance ...... 45 2.5.1 Variance and complexity...... 45 2.5.2 Objection: An extremely complex invariant ...... 48 2.5.3 Reply: The demarcation criterion must be simple ...... 49 2.5.4 Rejoinder: Demarcation needn’t be extremely simple ...... 50 2.5.5 Sub-conclusion: Moderate invariance is sufficient ...... 52

2.6 The Moderate Demarcation Criterion ...... 53

2.7 The of the Epistemic Invariant ...... 53 2.7.1 Objection: Rules don’t vary that much! ...... 56 2.7.2 Objection: Ultimate goals might not vary ...... 57 2.7.3 Sub-conclusion: Deep epistemic homogeneity ...... 58

2.8 Conclusion ...... 58

3 The New Problem ...... 61

3.1 Laudan’s New Problem ...... 61

3.2 Accounts of Confirmation ...... 62 3.2.1 Laudan’s views ...... 62 3.2.2 A simple account ...... 63 3.2.3 Sophisticated accounts ...... 64 3.2.4 Overcoming ...... 65

3.3 Objection: Science is Relevant to Confirmation ...... 66 3.3.1 Science is more than confirmation ...... 66 3.3.2 Types of ...... 66 3.3.3 No exhaustive relevance ...... 67 3.3.4 Causal relevance ...... 68 3.3.5 Statistical relevance ...... 69 3.3.6 Logical relevance ...... 71 3.3.7 Sub-Conclusion: Scientific status is strongly relevant ...... 71

3.4 Objection: Science has Other Purposes and Other Virtues ...... 71 3.4.1 Improving our requires novelty ...... 72 3.4.2 Improving our living standards requires usefulness ...... 72 3.4.3 Achieving confirmation requires testability ...... 72 3.4.4 Pre-selection before confirmation ...... 73 3.4.5 Other virtues can outweigh confirmation ...... 74 3.4.6 Reply: Pursuit versus acceptance ...... 74

6 3.4.7 Rejoinder: Demarcation is still useful for pursuit ...... 75 3.4.8 Rejoinder: Other virtues may affect acceptance ...... 76 3.4.9 Reply: Science is all about confirmation ...... 76 3.4.10 Rejoinder: Then science is relevant to confirmation! ...... 77 3.4.11 Sub-conclusion: Being ‘scientific’ indicates other virtues ...... 77

3.5 Conclusion ...... 78

4 Conclusion...... 79

Appendix A Some Non- Definitions ...... 80

A.1 Ideal Definitions ...... 80

A.2 Non-Ideal Definitions ...... 81

A.3 Gold ...... 82

A.4 Science ...... 83

A.5 Diamonds ...... 84

Appendix B Some Complex Demarcations ...... 86

B.1 Thagard ...... 86

B.2 Lugg ...... 87

B.3 Derksen ...... 88

Appendix C Other Virtues versus Confirmation ...... 90

C.1 Wonten versus Newton ...... 90

Bibliography ...... 93

7 Table of Figures

Figure 1.1: Three possible views of demarcation...... 20

Figure 1.2: Candidate rules in plausible levels of generality...... 22

Figure 1.3: Two inadequacies of the „eschew‟ proposal...... 22

Figure 1.4: A hierarchy of goals and methods...... 26

Figure 1.5: Laudan‟s Old and New Traditions of demarcation...... 28

Figure 1.6: Some methodological progress since Popper...... 31

Figure 2.1: Two epistemic virtues in a positive feedback loop...... 45

Figure 2.2: My alternative requirements for a demarcation criterion...... 53

Figure 2.3: The implications of heterogeneity for the invariant...... 56

Figure 2.4: Some accepted rules and their opposites...... 57

Figure 2.5: My alternative interpretation and claim...... 59

Figure 2.6: Two interpretations and my corresponding values...... 59

Figure B.1 Thagard‟s two conceptual profiles...... 86

Figure C.1 Two describing the behaviour of apples...... 90

8 0 Introduction

In 1983, Larry Laudan published a paper that he called „The Demise of the Demarcation Problem‟ (Laudan, 1983), in which he argued that the problem is unsolvable. This is my reply to Laudan‟s paper.

0.1 What is Demarcation?

To demarcate is, literally, to mark a boundary (Sykes, 1989). So a demarcation tries to sort things into two mutually exclusive groups: things inside the boundary, and things outside the boundary. If the boundary is not perfectly precise, then there may also be some „borderline‟ things that are not clearly „in‟ or „out‟.

In the philosophy of science, „the problem of demarcation‟ is to mark the boundary between things that are scientific and things that are not.2 Several different terms are commonly used to describe the contrasting things that are not scientific, with slightly different connotations: „non-scientific‟, „pseudo-scientific‟, and „unscientific‟. I do not think the differences here are important to Laudan‟s argument or my reply.

Science has many aspects, including (but not limited to): (a) basic elements such as theories, , , and results; (b) technical refinements such as mathematical models and formulae, tools, and statistical analyses; (c) general virtues of theories such as confirmation, novelty, , explanatory breadth and usefulness; and (d) social arrangements such as qualified experts, published journals, , large institutions, and competition for funding. We can try to demarcate between what is scientific and what is not with respect to areas of , or with respect to any of these particular aspects. I take it that all these demarcations should be closely related, e.g. once we have decided that an area of knowledge is a science, we would probably describe most of its theories, methods, instruments, and experts as scientific.

2 It is possible to propose logically weaker demarcation criteria that are only one-way: they either include things as scientific, or they exclude things as non-scientific, but not both. One might also define other weaker types of criterion. But traditionally philosophers have been concerned with strong criteria that are two-way, applicable in all areas, and so on. Laudan clearly states that he is concerned with strong criteria of this kind (Laudan, 1983: 119), and strong criteria will also be my concern. Note that if a strong criterion were discovered, then this could also function as a logically weaker criterion (e.g. exclude non-sciences). So if my defence of the possibility of demarcation is successful, then it holds for strong and weak criteria alike.

9 We can sort things adequately for a specific purpose and context, but this sorting may not be adequate for another purpose or context. What purposes could a demarcation of science serve? Let us suppose that the successful demarcation is based on fundamental features that make science epistemically superior, which gives us both a deeper theoretical understanding of science and the practical ability to tell whether something is scientific. Then it could be useful in several ways. Firstly, it could be of theoretical interest to philosophers, e.g. to help them explain why science is epistemically superior. Secondly, it could be of practical interest to non-, e.g. to help them decide what research to fund or who to trust. Thirdly, it could even be of practical interest to scientists themselves, e.g. to help them improve their practices.3

Demarcation is described as a „problem‟ because it has proved to be very difficult – and perhaps impossible – to achieve. Philosophers of science have worked on it for many years. While many solutions have been suggested, and many philosophers think they have solved it, no solution has been accepted by all or most philosophers of science. Therefore, I shall assume, as Laudan does, that the demarcation problem is „unsolved‟.

In claiming that the demarcation problem cannot be solved, Laudan is rejecting all forms of the problem. He is not distinguishing between demarcations that contrast non- scientific, pseudo-scientific, or unscientific. He is not distinguishing between demarcations that apply specifically to areas, theories, methods, people, etc. He is not distinguishing between demarcations for one purpose or another. He is asserting that the solutions to all of these variations on the demarcation problem are either impossible or unimportant. Like Laudan, I will not focus exclusively on any of these particular variations on the problem. I take it that they are all related. However, this variety does make it harder for Laudan to establish that no useful version of the problem can be solved.

Is there any between a demarcation and a definition? A definition of what is scientific also tries to sort things into two groups. Things are scientific iff they fit the definition; things are not scientific iff they do not fit the definition. Again, there may be borderline cases. Definitions can apply to any aspect of science, and be designed for any purpose, whether theoretical or practical. I will not assume that the projects of „demarcating‟ and „defining‟ what is scientific are exactly the same, but they are clearly similar – I will argue that similar issues can arise.

3 For more detailed discussion of the purposes of demarcation see for example (Cioffi, 1970), (Gardner, 1957), (Kuhn, 1996), (Resnik, 2000), (Ruse, 1982), and even (Laudan, 1983: 111).

10 0.2 Laudan’s Three Sceptical Arguments

There are two possible reasons for the failure to solve the demarcation problem:

1. There is a solution out there – the we haven‟t found it (or accepted it) yet is because philosophers have not been imaginative (or perceptive) enough; or

2. There is no solution to the demarcation problem – that‟s the reason we haven‟t found one.

Laudan favours the second diagnosis, arguing that the demarcation problem hasn‟t been solved because it is unsolvable.

Laudan gives three main sceptical arguments against the original demarcation problem:

A Pessimistic Induction – None of the many and varied criteria offered so far have successfully demarcated science from non-science. Therefore, it is unlikely that there will be any future success.

A Pseudo-Problem – The demarcation problem presupposes an accurate, precise epistemic invariant in all and only science. There is no such feature. Therefore, the demarcation problem is a pseudo-problem.

A New Problem – A better alternative to the demarcation problem is to identify theories that are well-confirmed. We can (and should) evaluate confirmation without considering scientific status.

Laudan concludes that terms such as „pseudo-science‟ and „non-science‟ do nothing but rhetorical work. He recommends that philosophers and scientists trade-in their jargon and for sound argument and strong . If we are serious in our quest to identify superior theories, then we should evaluate theories solely on the basis of their empirical and conceptual credentials, and their scientific status should be irrelevant (Laudan, 1983: 125).

It is important to recognise that Laudan does not claim that science doesn‟t exist. He agrees that the terms „science‟ and „non-science‟ identify a genuine distinction, but he argues that this distinction has no philosophical and epistemological significance (Laudan, 1983: 125).

In this thesis I reply to each of these three arguments in turn. My aim is modest: refuting Laudan‟s arguments, rather than putting forward my own criterion. Other critics of „Demise‟ have been more ambitious. They have attempted to refute Laudan‟s arguments by : offering their own criterion as a new solution to the problem of

11 demarcation (e.g. (Butts, 1993), (Lugg, 1987)). The problem is that unless such a criterion is accepted, the refutation also fails. In contrast to these critics, the strength of my refutation does not rely on any particular criterion being the final solution to the problem of demarcation.

My central question is: „Has Laudan killed the demarcation problem?‟

12 1 The Pessimistic Induction

1.1 Laudan’s Pessimistic Induction

Laudan wonders if a solution to the demarcation problem is still feasible, so he considers past attempts at demarcation in order to shed some on this question.

He initially considers the candidates proposed by . Laudan tells us that the task of identifying genuine knowledge had been attempted even earlier. But Aristotle‟s focus was on scientific knowledge, and the solution he proposed was extremely influential. Laudan says that Aristotle demarcated science from craft with his criterion of „knowledge of first causes‟. This distinguished between „know-how‟ (the kind of knowledge a craftsman has about how to build a boat that floats) and „know-why‟ (the kind of knowledge a has about why the boat floats). We can only arrive at scientific knowledge about an event or behaviour (know why it has occurred) by tracing its causes back to first principles (Laudan, 1983: 112-113).

According to Laudan, this candidate had a varied career. Initially, it served as grounds for dismissing certain fields of as unscientific – for example, early mathematical failed to qualify as a science because it didn‟t yield knowledge of first causes. Instead, astronomers offered hypothetical models, which they sought to test by comparing predictions made by their models with the observed positions of the planets. Laudan tells us that it wasn‟t until the beginning of the seventeenth century that scholars started to question this position on the scientific status of astronomy. Galileo, Huygens and Newton wanted to give scientific status to many systems of that laid no claim to understanding underlying principles or knowledge of first causes. Thus, „knowledge of first causes‟ failed to become the accepted solution to the demarcation problem (Laudan, 1983: 113-114).

Laudan tells us that Aristotle also identified a second, complementary demarcation criterion: he demarcated science from with his criterion of „apodictic ‟. He claimed that the product of scientific inquiry was demonstrably certain, i.e. infallible (Laudan, 1983: 112). Infallibility and knowledge of first causes worked together as a two- pronged demarcation: science can be distinguished from non-science both by the certainty of its knowledge and by the basis of this knowledge in first principles (Laudan, 1983: 113). But after the latter was rejected, infallibility became the sole criterion of demarcation. Laudan notes that, for a while, infallibility was a great success: despite their disagreement in

13 other areas, scholars of the seventeenth and eighteenth centuries widely agreed that scientific knowledge was apodictically certain (Laudan, 1983: 114).

Laudan tells us that this criterion was finally rejected when scholars noticed that existing theories were often amended or replaced by better theories – this could only occur if the existing theories were false. It now seemed that few (if any) scientific theories were infallible, and philosophers were forced to conclude that scientific knowledge was fallible after all. Thus, Aristotle‟s „apodictic certainty‟ also failed to solve the demarcation problem (Laudan, 1983: 114-115).

Laudan tells us that after the final defeat of Aristotle‟s twin criteria for demarcation, philosophers considered as a possible replacement. They aimed to show that „the scientific method‟, although fallible, was a better way of testing empirical claims than any other method. “And if it did make mistakes, it was sufficiently self-corrective that it would soon discover them and put them right” (Laudan, 1983: 115). Furthermore, this superior method was the thing that distinguished science from non-science, and made scientific knowledge epistemically superior (Laudan, 1983: 115).

According to Laudan, while many philosophers believed in the methodological criterion, they could not agree on the details of this method. The candidates for „the scientific method‟ were diverse: some thought that scientists reasoned by induction; others thought that scientists restricted their theories to what could be directly observed; and still others thought that scientists preferred theories that successfully predicted novel . Without agreement about the details of scientific methodology, philosophers were unable to argue persuasively that methodology is what demarcates science from non-science. Laudan also notes that most of the proposed methods failed to resemble the methods actually used by scientists. So, for these two reasons, this approach failed before it got very far. Firstly, philosophers were unable to identify the scientific method (that all and only scientists used). Secondly, philosophers were unable to establish the superior epistemic credentials of any of the methods considered (Laudan, 1983: 115-116).

According to Laudan, an entirely new approach to demarcation emerged early in the twentieth century. This new approach equated science with meaningfulness: scientific statements are those that have a determinate meaning. Philosophers argued that we can establish whether or not a statement has a determinate meaning by deciding whether or not the statement can be exhaustively verified. This approach became known as : a claim is scientific iff it can be exhaustively verified (or confirmed) by empirical testing (Laudan, 1983: 120). Another candidate from the same period is

14 Falsificationism: a claim is scientific iff it can be falsified (or refuted) by empirical testing (Laudan, 1983: 121).

Laudan tells us that these candidates failed in two ways. Firstly, it wasn‟t the case that all and only scientific statements were verifiable or falsifiable. Laudan remarks that (a) there are clear examples of scientific claims that are not exhaustively verifiable or falsifiable, (b) all non-sciences contain at least some claims that are verifiable (Laudan, 1983: 120), and (c) falsificationism appears to give scientific status to any theory, as long as it makes ascertainably false claims (Laudan, 1983: 120-121). Secondly, Laudan argues that the notion of testability fails to identify what is important about science: being testable-in- principle does not make a theory worthy of belief (Laudan, 1983: 122). Laudan argues that these two candidates mark a significant shift in the approach to demarcation. Where the earlier candidates attempted to identify an epistemological demarcation, these later candidates attempted to identify a syntactic or semantic demarcation (Laudan, 1983: 121- 122).

Finally, Laudan wonders whether there are any promising candidates “waiting in the wings” (Laudan, 1983: 122). He considers the following:4

. Scientific claims are well-tested;

. Scientific theories exhibit progress or growth;

. Scientific theories make surprising predictions that turn out to be true;

. Science is the only form of system building that proceeds cumulatively; and

. Science is the sole repository of useful and reliable knowledge.

He argues that none of these candidates is promising. Some scientific theories are highly speculative, and therefore untested and unreliable. Some established scientific theories do not progress rapidly or make lots of surprising, successful predictions. Finally, some scientific theories do not contain their predecessors as special cases, therefore not all scientific progress is cumulative. He concludes that none of these candidates identifies a feature that is always or only displayed by science (Laudan, 1983: 122-124).

4 Laudan does not mention his own demarcation attempt, in which he offers problem-solving effectiveness as the primary aim of science (Laudan, 1977). This is surprising, since he continues to develop this idea in later years (e.g. (Laudan, 1990b)).

15 After considering these past demarcation attempts, Laudan argues that all future attempts at demarcation will probably fail. This argument looks like a pessimistic induction,5 which contains at least this and conclusion:6

P1. All past attempts at demarcation have failed. C. All attempts at demarcation will fail.

1.2 Inductive Inference

Inductive inference can be represented in the following form: 7

P1. All observed xs are Q. C. All xs are Q.

Inductive arguments are not deductively valid, in that P1 does not entail C by the normal rules of deductive logic. Rather, the conclusion is ampliative: it expands on what is contained in the . Specifically, C has the same logical form as P1, but it has a broader range of application, because it also includes unobserved xs.8 Hence, unlike deductive , inductive inferences are fallible: P1 may be true while C is false.

Although all inductive inferences are fallible, some seem more likely to fail than others. Some inductions seem „good‟, and hence more convincing. Others seem „bad‟, and hence less convincing. It is difficult to give precise, general rules for what constitutes a good or bad inductive inference, but I take it that sometimes we can tell the difference.

5 This pessimistic induction about demarcation attempts should not be confused with Laudan‟s well-known pessimistic induction to the conclusion that all scientific theories are false (e.g. (Laudan, 1981a: 121-124), (Laudan, 1984: 121), (Psillos, 1999: 101)).

6 I have three reasons for interpreting this as a pessimistic induction. Firstly, Laudan does not make the logical structure of his argument entirely explicit and clear, so to some extent I must address arguments that are implicitly suggested by what he says. Secondly, Laudan does devote a large amount of space to describing this historical sequence of failed demarcation attempts, and does conclude that all future attempts to identify an “epistemic version of a demarcation criterion” will probably fail (Laudan, 1983: 124). This certainly suggests a pessimistic induction, which I am entitled to discuss as an explicit argument. Thirdly, Laudan has also advanced a similar argument in the past, namely his pessimistic induction about the truth of scientific theories, so it is not unreasonable to attribute this kind of argument to him.

7 One might call Laudan‟s pessimistic induction a „meta-induction‟, because it is about Philosophy rather than . However, I take it that meta-inductions, pessimistic inductions, and inductions about nature all have a similar form.

8 So, C does entail P1, and hence is logically stronger than P1.

16 Making this particular inductive inference is equivalent to assuming that unobserved xs will resemble observed xs, at least with respect to the property Q. I shall call this the „resemblance assumption‟ (a resemblance which is specific to each inductive argument). We can replace any application of a rule of inductive inference with an additional premise such as this, and thereby make the argument deductive. Of course, this does not make the inference any less fallible; we have merely replaced an unreliable rule with an unreliable premise.

There is some dispute over what additional premises, if any, should be present to make a good inductive inference. I have used only P1 and C because they capture the basic idea, on which everybody agrees: induction extrapolates from the observed to the unobserved. Some philosophers, e.g. (Chalmers, 1999: 45-49), claim that (generally speaking) good inductive inference requires this additional premise: „A large number of xs have been observed under a wide variety of conditions‟. Certainly, an inductive inference is unlikely to be good if we have observed only one x! If all of these many and varied xs (without exception) are Q, then one usually has good reason to assume resemblance.9

In some cases, it is clear that the resemblance assumption is inappropriate. Consider the following two scenarios:

Scenario 1: You and I are playing a game. There are four cups lined up on a table. You turn your back while I hide a coin under one of them. The object of the game is for you to guess which cup is hiding the coin. You point to the first cup and say, “Is it this cup?” and I say “No”. You point to the second cup and say, “Is it this cup?” and I say “No”. You point to the third cup and say, “Is it this cup?” and I say “No”. At this point, you throw your hands up in the air in frustration and say, “I’m never going to get it! Show me which cup it is.”

9 P1 demands that all of the xs are Q. But statistical inferences can be described as inductive, because they extrapolate from the observed sample to the unobserved population. In such inferences, the requirement that the observed xs are many and varied is replaced by an assumption that the observed xs are a random sample from the population. The requirement that all the observed xs are Q is usually replaced by a premise that some specific proportion of observed xs are Q. The conclusion then concerns the proportion of xs in the population. But I can leave aside all the complications involved in statistical inferences, because Laudan‟s induction seems to take the traditional, non-statistical form.

17 You have inferred that since all of your past attempts at identifying the correct cup have failed, all of your future attempts at identifying the correct cup will fail. This inference is a bad one! By a process of elimination, on your next guess you would have identified the correct cup.

Scenario 2: You are helping a child who is learning how to tie his shoelaces. On the first day, he tries and fails. On the second day, he tries and fails. On the third day, he tries and fails. At this point, you conclude that he’s always going to fail, so you go out and buy him shoes with Velcro fasteners instead.

You have inferred that since each of the child‟s past attempts to tie his shoelaces has failed, all of his future attempts to tie his shoelaces will fail. This inference is a bad one! When a child is learning to tie shoelaces, he will fail many before he succeeds. If he keeps trying, probably the child will eventually succeed.

While I do not have a general rule to tell me which inductions are good ones, I can still tell in each of these cases that the resemblance assumption is inappropriate. In both of the above scenarios, we have good reason to believe that the unobserved cases have a significant chance of being different to those already observed. In the first scenario, we know that one of the cups definitely has the coin in it. So, as each cup is eliminated, the probability that the next guess will be correct rises. Thus, each new unobserved case is more promising than the last, and the fourth guess is completely certain to be correct. In the second scenario, although the child fails to tie his shoes each morning, we hope that he is learning and improving each day. Indeed, it is reasonable to expect that over time the child will learn how to tie his shoes. One could even argue that the use of the term „failed‟ is inappropriate here. It is not nuanced enough to convey the notion that each day the child comes a bit closer to success: each day the failure is partial, not total. So, in both of the above cases, we have good reason to reject the resemblance assumption.

These examples seem to illustrate a general principle. If relevant progress is being made towards success, then there is often a good chance that success will eventually arrive, despite a sequence of failures. Hence, in such cases we have good reason to a pessimistic induction.

In summary, inductive inference is equivalent to the assumption that unobserved xs will resemble observed xs with respect to the property Q. There are appropriate and inappropriate resemblance assumptions – and sometimes we can tell the difference. Evidence of progress is one good reason to doubt a pessimistic resemblance assumption.

18 1.3 The Resemblance Assumption should be Rejected

Those who believe that the demarcation problem is solvable do not believe that all attempts at demarcation will succeed; rather, they believe that an attempt will succeed. The fact that all observed attempts at demarcation have failed is disappointing, but it does not contradict this optimistic belief in eventual success. In fact, the history of demarcation attempts may be encouraging if these failures were only partial, and there is some cumulative progress. Laudan, in contrast, is arguing that the demarcation problem is unsolvable and that all attempts at demarcation will fail. This resemblance assumption is dubious if we have good reason to believe that philosophers are making relevant progress. So, to make his pessimistic conclusion compelling, Laudan must assume that each attempt at demarcation is a complete failure and that these failures have never led to progress.

I shall argue in the following sections that Laudan‟s bleak assessment of philosophical progress is not correct. Many past attempts at demarcation have resulted in failures that are only partial, and many of these failures have led to some cumulative progress. In fact, one might make an optimistic induction: since many observed attempts have resulted in significant progress, then many future attempts will also result in significant progress. Since sufficient progress must eventually lead to success, one might then infer that philosophers will eventually succeed. This optimistic resemblance assumption leads to the opposite conclusion! However, I do not need to defend such an optimistic view in order to refute Laudan‟s pessimism. I only need to establish that some progress is evident. This leaves the future of the demarcation problem unclear (at worst), with a significant chance that philosophers will eventually succeed. This is sufficient to establish that Laudan‟s inductive argument is unconvincing.

I summarise these three possible views of demarcation in Figure 1.1. I argue that Laudan‟s pessimistic assumptions are untrue (so I place an F for false next to these items), and therefore it is not clear that we will never succeed (so I place a question mark here). I claim that the optimist‟s assumptions are true (so I place a T for true next to these items), but it is still not clear that we should accept the optimistic that we will eventually succeed (so I place another question mark here). I argue that a mixed assessment of the situation is warranted, and therefore we should conclude (at worst) that the outcome is uncertain (so I place a T next to all these items).

19 Assessment Local Result Global Result Future Prediction

Pessimistic Complete Failure F No Cumulative Progress F Never Succeed ?

Optimistic Significant Success T Cumulative Progress T Eventual Success ?

Mixed Mixed Results T Mixed Results T Outcome Uncertain T

Figure 1.1: Three possible views of demarcation.

1.4 Progress in the Philosophy of Method

Laudan tells us that prior to the nineteenth century, Aristotle‟s „Apodictic Certainty‟ was considered to be the definition of science. However, the replacement of several well- developed scientific theories by new theories left philosophers with no choice but to conclude that scientific knowledge was fallible after all. From my point of view, this failure of Apodictic Certainty was progressive: it led directly (by elimination) to a correct belief about science.

In any case, nineteenth-century philosophers turned to „the scientific method‟ to do the job. They thought that the method used by scientists was fallible, but nonetheless superior to methods used by non-scientists; and hence was an adequate demarcation criterion. Laudan says that for this approach to succeed, philosophers needed to complete two tasks (Laudan, 1983: 115):

1. Identify a method that all and only scientists follow; and

2. Justify this method by appealing to its superior epistemic status.

According to Laudan, philosophers could never deliver on either of these tasks because of their lack of agreement about the basic tenets of the scientific method (Laudan, 1983: 115- 116). Without such agreement, philosophers were unable to argue persuasively that superior method is what demarcates science from non-science. Furthermore, proposed methodological rules were either incomprehensible or too complicated to follow: Laudan identifies a rule instructing one to “eschew theoretical entities” (Laudan, 1983: 116) as typical of this era. Finally, Laudan tells us that „the scientific method‟ was never adequately

20 justified because philosophers had no good reason to prefer one proposed „scientific method‟ over another – or to any „unscientific method‟.

Laudan is surely correct in saying that the rule „one ought to eschew theoretical entities‟ is a failure. However, he seems to assume that this kind of rule is symptomatic of the failure of the entire approach. Moreover, he appears to think that the of scientific methodology as an approach to demarcation („methodology‟ for short) was a dead-end, and that the astute philosopher should have realised this in advance (Laudan, 1983: 115). He notes that several of his contemporaries, who he regards as “respectable” philosophers (Laudan, 1983: 115), approach demarcation in this way; but he doesn‟t discuss any of their developments. Presumably, he doesn‟t see them as progressive. I claim that Laudan‟s dismissal of methodology is premature. In fact, recent developments in methodology have shed some light on the failures of nineteenth-century attempts to demarcate between science and non-science.

One might wonder which less-than-astute philosophers continued to work on methodology. In fact, one of them was Laudan. One year after „Demise‟, Laudan published a book called Science and Values (Laudan, 1984) in which he proposed a theory of scientific methodology. He developed his methodology in subsequent papers (e.g. (Laudan, 1987)). He argued convincingly that scientific method is „goal-directed‟. Laudan claimed that methodological rules make no sense as isolated statements of the form: One ought to do x. Rather, they should be regarded as conditional statements of the form: If one’s goal is y, then one ought to do x. So, particular methods can be justified by a rule such as this: As long as one’s goal is y, and one that doing x is more likely than any alternative method to produce y, then one is justified in doing x (Laudan, 1987: 203).

These points have been accepted by many philosophers of science (e.g. (Sankey, 2000), (Worrall, 1988)), and may be regarded as developments in the field. Even if philosophers didn‟t know what was wrong with the „eschew‟ rule at the time, we can now identify at least one problem: it‟s not explicitly related to a goal, and it‟s not clear what kind of goal would really justify this rule.

Another development in methodology was made by Rosenberg (1985), who argued that there are levels of method: some rules are more general than others. We can distinguish between, say, rules that give general advice about how to proceed, and rules that specify particular actions that ought to be taken. For example, in Figure 1.2 I have out some candidate rules arranged in plausible levels of generality. This shows us something

21 else that was wrong with the „eschew‟ rule: it is not clear what particular actions should follow from this general rule, i.e. how we could possibly implement it.

Avoid forming inaccurate conclusions

Avoid making Avoid Avoid incorrectly inaccurate experimenter and rejecting the null effects

Always calibrate a Always perform Always select a pH-meter against double-blind tests low p-value distilled water

Figure 1.2: Candidate rules in plausible levels of generality.

Laudan attributes the failure of the „eschew‟ rule to the fact that it “involved complex conceptions which neither scientists nor philosophers of the period were willing to explicate” (Laudan, 1983: 116). This is a good enough reason to reject the rule: if a methodological rule is such that we cannot tell when it is being followed and when it is being flouted, then it is of little use to philosophers or scientists. However, this third criticism (while valid) only tells us that good rules need to be spelled out more clearly. Compared to the previous two criticisms, it doesn‟t provide us with much new about what methodological rules should look like.

Taken on its own, the failure of the „eschew‟ rule is scarcely progressive. It eliminates one candidate rule, but this still leaves many other possibilities. There does not seem to be any „good bit‟ that we can take from it! However, philosophers have now learned some general lessons that would avoid such rules. These serve to identify at least two reasons why the „eschew‟ rule was a failure (as depicted in Figure 1.3):

1. It‟s not clear what kind of goal would justify this rule; and

2. It‟s not clear what particular actions should follow from this rule.

? One ought to eschew ? GOAL ACTIONS theoretical entities.

Figure 1.3: Two inadequacies of the ‘eschew’ proposal.

22 Presumably, Laudan selected the „eschew‟ rule because it is one of the worst rules that methodology has ever offered. Its flaws are supposed to illustrate the failure of the entire nineteenth-century methodological approach. But, as I have demonstrated, even this rule can be used to illustrate my point: since the nineteenth century, progress has been made in methodology.

1.5 Reply: Scientific Method Changes

I have presented the history of change in „methodology‟ as a positive – i.e. philosophical accounts of „the scientific method‟ are progressing towards a better understanding of it. However, one implication of the philosophical developments I have discussed is that scientific methods themselves may well change over time. This should occur when scientists develop better techniques for achieving their goals, and does occur frequently with statistical and experimental techniques.

Laudan sees this kind of methodological change as a negative. He argues that if everything about science changes, then we cannot give an enduring definition of science (Laudan, 1987: 214).10 If the change is completely pervasive (so that not even the nature of the changes is constant), then this is surely correct. So, if we hope to demarcate between science and non-science, then we would like our account of science to:

a) Account for the variation (over time, between disciplines, etc); and

b) Explain the common aspects (over time, between disciplines, etc).

In (b), we would be identifying some characteristic general things that don’t change.

A plausible response to Laudan‟s objection is that the most general things don‟t change, even if the details do. Specifically, one might argue that we can identify an appropriate overall goal (or set of goals) for science. I shall call these „ultimate goals‟. If the ultimate goals don‟t change over time and across disciplines, then this might be sufficient. These goals would need to be very general in both their applicability and attractiveness, since they would need to be goals to which all scientists are at least superficially committed, despite the disparity of their particular practices. There is one obvious candidate: a complete, true theory of the world (and preferably, one that is easy to understand!). The goal of truth is applicable to all areas of science, because regardless of which aspect of the world is under investigation, scientists can develop theories attempting

10 This is not an argument he makes in „Demise‟. However, because it is relevant to his pessimistic comments on methodology, I shall digress briefly to examine this argument.

23 to describe this aspect (which are then true or false). Truth should also be very attractive in any area, for two reasons. Firstly, a true theory offers genuine insight and understanding of the world, which is often desired for its own sake. Secondly, a true theory allows practical predictions and manipulations of the world for our benefit. It should be accurate not only about phenomena we have already observed, but also about any other phenomena described by the theory (which we have never observed). For the moment, let us suppose that truth is the enduring ultimate goal of science.

As previously discussed, Laudan agrees that goals can constrain and regulate method. To this end, he identifies a „naturalist justification‟ of method (Laudan, 1987: 206-207):

If actions of a particular sort, m, have consistently promoted certain cognitive ends, e, in the past, and rival actions, n, have failed to do so, then assume that future actions following the rule „if your aim is e, you ought to do m‟ are more likely to promote those ends than actions based on the rule „if your aim is e, you ought to do n‟.

This justification requires that we can tell when e is achieved. But Laudan argues that some goals are such that we cannot tell when they have been achieved. He describes these goals as „transcendent‟,11 and he argues that truth is one such goal (Laudan, 1984: 50-55). If e is transcendent, then we cannot know which actions can promote or achieve it. Therefore, a complete, true theory of the world cannot be the e in Laudan‟s rule, and cannot be the enduring, ultimate goal that motivates scientific methodology.

One natural response to Laudan‟s objection is to replace truth with attractive lower- level goals that are not transcendent. For example, scientists may aim for theories that are simple relative to their explanatory breadth, and/or make successful novel predictions. These goals are more realistic in that (properly explicated) we can tell when they have been achieved. I shall call these „applied goals‟. Applied goals can guide or constrain the methodological rules scientists follow according to the naturalist rule for justifying method.

Replacing ultimate goals with applied goals is a move that won‟t appeal to everyone. Most philosophers and scientists would agree that goals such as simplicity, explanatory breadth, and successful novel predictions should be preferred to their respective opposites: complexity, explanatory narrowness, and unsuccessful or unsurprising predictions. However, many philosophers would be dissatisfied if there were no reason for this

11 Laudan actually uses the term „transcendental‟. But Kant introduced a distinction between „transcendental‟ and „transcendent‟ which I will follow here. Goals are „transcendent‟ if they are beyond knowledge, and this is what Laudan wishes to say about truth.

24 preference. For example, it would be nice if we could justify simplicity on the grounds that nature tends to be simple, and hence simpler theories are more likely to be true. Alternatively, it would be nice if we could justify simplicity on pragmatic grounds, by showing that simpler theories are easier to use. If either truth or utility is the ultimate goal, then this would provide a good justification, because truth and utility both seem obviously desirable. But otherwise, why should scientists prefer simplicity? It is not so obvious that simplicity is desirable as an end in itself. It seems somewhat arbitrary as a goal for science, and not one that is likely to unite scientists always and everywhere. Similar comments could be made about other applied goals. Without a very desirable ultimate goal, they seem inadequate to motivate a universal scientific method.

We could extend this argument even to the goal of confirmation, or „belief worthiness‟. If a scientist aims for truth, then she has good reason to make sure her claims are adequately justified or strongly supported by . If she does not hope to advance empirical knowledge, then she needn‟t be concerned with justification or support at all. In fact, she may prefer to make claims that are unjustified or contradicted by empirical evidence, if only to make them more interesting! Without an ultimate goal such as truth, it seems that scientists may have any applied goals whatsoever, and may change goals whenever they wish. Scientific method remains unfixed and appears to lack all epistemic credentials.

The universal-goal proposal faces an impasse: the achievement of proposed scientific goals is either not adequately verifiable or not adequately attractive. However, there seem to be (at least) two moderate paths we can take to avoid this difficulty.

Firstly, one could argue that empirical accuracy with respect to available is the most we can ever know we have achieved. If this is true, then current empirical accuracy does seem to be the second-best ultimate goal. It does not offer a deep understanding of the world. But an accurate description of observed phenomena would be all the truth we can be certain we have obtained. Moreover, it should still allow us to successfully predict and manipulate the world. We can reasonably expect that a well-tested theory would be accurate (at least) about the kinds of phenomena we have already observed, even if we have less reason to feel confident about predicting kinds of phenomena we have never observed. Therefore, this kind of empirical accuracy seems appropriate as an ultimate goal. Presumably, empirical accuracy (relative to currently available empirical results) is such that we can recognise it when we see it. In this case, we may empirically test the correlation

25 between this ultimate goal and the applied goals, and justify them using Laudan‟s naturalist justification.12

Secondly, one could argue that truth is not always transcendent. I think I can tell when some everyday claims are true. Perhaps we can also tell when some general theories are true. For example, consider Harvey‟s that the function of the heart is to pump blood around the body (Schultz, 2002). This is now accepted as definitely true, and it‟s hard to imagine how Laudan could motivate about this claim. Admittedly, it is not clear that all our theories will eventually be so certain. For example, consider the best general theories in . Can we ever be certain that they are true, and not just predictively accurate? Perhaps this is the kind of example that Laudan had in . Nevertheless, if we can sometimes tell what‟s true, then perhaps through we may learn to recognise „truth indicators‟. These might be things like consistency, simplicity, explanatory breadth, predictive ability, empirical support, and so on. In the absence of certainty, we could use these indicators as a guide to which theories are more likely to be true. On this proposal, the ultimate goal is truth – but wherever truth is transcendent, we have good reason to prefer the applied goals of simplicity, explanatory breadth, predictive success, and so on, to their opposites.

On either of these moderate proposals, the applied goals may change as we learn more about science and about the world, but the ultimate goal of science stays the same.

kjlk Ultimate Goals

Applied Goals

General Methods

Particular Methods

Figure 1.4: A hierarchy of goals and methods.

12 Similarly, one might argue that Laudan‟s (1977 & 1990b) problem-solving effectiveness is not transcendent. Therefore, as the primary aim of science, it may succeed where truth fails.

26 The ideas I have discussed may be represented by the structure of scientific practice depicted in Figure 1.4. In this structure, only the ultimate goals of science remain fixed over time and shared by all scientists. The applied goals and methodological rules can change over time and vary between disciplines (and even within a discipline).

Laudan views methodological variation as bad news for demarcation. If the goals and rules at all levels of scientific practice vary, then we cannot give an enduring definition of science. I have argued that even superficial allegiance to certain „ultimate goals‟ is enough to „fix‟ science by constraining and guiding the kinds of methods that scientists use. This doesn‟t sound like such bad news to me. It leaves open the possibility of an enduring definition of science, and in particular, it leaves open the possibility of a demarcation criterion that refers to methods and goals.

1.6 Reply: The New Tradition is not Epistemic

So far I have argued that the failure of nineteenth-century methodology was only partial; and that subsequent work on methodology can be regarded as progressive. But Laudan has another reason for making the resemblance assumption. He expects that future attempts at demarcation will be even less successful than past attempts, because philosophers no longer seek an epistemically significant distinction. According to this argument, demarcation attempts that do not make the distinction epistemically significant are complete and unprogressive failures.

According to Laudan‟s summary, originally the demarcation problem was firmly grounded in . It was concerned with, for example, knowledge vs. , vs. appearance, and truth vs. error (Laudan, 1983: 112). Eventually, this problem became related to the nature of scientific knowledge, and science has continued to be the focus of the demarcation problem. Laudan distinguishes between the „Old Demarcationist Tradition‟, characterised by Aristotle and the nineteenth-century methodologists, and the „New Demarcationist Tradition‟, characterised by the Verificationists and Popper (Laudan, 1983: 112-121). Laudan argues that the shift from the Old Tradition to the New Tradition might be viewed simply as a shift from epistemic to semantic strategies: the Old Tradition was concerned with the epistemic merit of claims; the New Tradition was concerned with the semantic structure of statements. But, Laudan argues, the shift is far more significant. Philosophers from the Old Tradition were concerned with actual evidential support, and they proposed criteria that were supposed to provide retrospective judgement of the scientific status of theories. Philosophers from the New Tradition were concerned with possible evidential support, and criteria were proposed that were supposed to judge the

27 scientific status of theories in advance. The Old Tradition equated science with belief- worthiness, and hence it was concerned with actual epistemic warrant. However Laudan argues that the New Tradition did neither of these things. Rather, the New Tradition saw science only as knowledge claims that are testable in principle, and hence it was concerned only with potential epistemic scrutability. Since „testable‟ claims are not necessarily worthy of belief, any demarcation criterion proposed in the New Tradition, says Laudan, will fail to make the demarcation between science and non-science epistemically significant. Laudan‟s dichotomy is summarised in Figure 1.5.

Old Tradition New Tradition

Epistemic Semantic

Judges after empirical tests Judges before empirical tests

Seeks well-tested claims, Seeks testable claims, i.e. actual empirical support i.e. potential empirical scrutability

e.g. Aristotle, 19th Century Methodology e.g. Verificationism, Falsificationism

Figure 1.5: Laudan’s Old and New Traditions of demarcation.

Laudan regards the New Tradition as having lost its integrity – it has downgraded science to the point where scientific status has nothing to do with epistemic warrant, and any „crank‟ theory can achieve scientific status. If this is true, then it is a strong argument for the complete and unprogressive failure of methodology.

1.7 Rejoinder: Testability is Epistemic

Laudan contrasts „well-tested‟ with „testable‟. While well-testedness suggests strong empirical support, and hence epistemic merit and belief-worthiness, he says “testability is a semantic rather than an epistemic notion, which entails nothing whatever about belief- worthiness” (Laudan, 1983: 121). I agree that testability is a semantic notion, since it describes a property that statements have in of their meaning. However, I claim that testability has epistemic implications as well. A theory that is untestable could never be tested, and hence, could never become well-tested. Testability is a necessary condition for well-testedness. However, as Laudan says, being well-tested is closely correlated to the of „belief-worthiness‟. Thus, being testable has the epistemic implication that a theory could become more „belief-worthy‟ through surviving tests. Indeed, it is plausible that being testable is a necessary condition for a theory to be both empirical and „belief-worthy‟.

28 Unlike Laudan, Popper certainly believed that testability had epistemic implications. He made testability central to his theory of Falsificationism. Laudan dismisses Falsificationism, yet Popper gave a persuasive account of how testability is relevant to epistemic virtue. We can identify three distinct principles in Falsificationism, which are evident throughout Popper‟s work (e.g. (Popper, 1959), (Popper, 1963)):13

1. Logical : A theory must make predictions that could be proven false;

2. Methodological falsifiability: Proponents of a logically falsifiable theory must be willing to test those predictions, and reject the theory if they prove to be false; and

3. Corroboration: A theory that has survived many attempts at falsification is „corroborated‟, and it is rational to prefer corroborated theories to theories that are uncorroborated.

Laudan focuses only on (1), and seems not to recognise that (2) and (3) are just as crucial to Falsificationism. So he dismisses Falsificationism as if it only consisted of (1). However, as Popper explains, (1) is just the first step towards (3). Corroboration, (3), must be seen as an account of well-testedness, empirical support, and an attempt to explain how some theories “exhibit a surer epistemic warrant or evidential ground” (Laudan, 1983: 118) than other theories.14 It should be clear, then, that Falsificationism is at least partly epistemic in approach. Laudan‟s dismissal of Falsificationism is based on a superficial and distorted characterisation of Popper‟s views.

The fact that Laudan‟s dichotomy between the so-called Old and New Traditions breaks down in the case of Falsificationism undermines the of his distinction. At best, it must be fuzzy; at worst, there is no important distinction to be made.

1.8 Rejoinder: The New Tradition is Progressive

Laudan‟s history of past attempts at demarcation seems to finish with Falsificationism, which he takes to be a complete and unprogressive failure. However, I now argue that Falsificationism did not completely fail, because aspects of Falsificationism have been

13 Popper doesn‟t properly distinguish between (1) and (2). However, he does seem to advocate both at different times. So I think it is safe to assume that he believes that both are necessary conditions to be a falsifiable (and hence scientific) theory.

14 Despite Popper‟s scepticism about the idea of support, the notion of corroboration can be seen as quite similar to the notion of confirmation. Viewed in this way, (3) is very similar to the thesis Laudan puts forward at the end of his paper: that confirmation is what matters. I shall discuss this in Chapter 3.

29 retained in later methodological accounts. Moreover, the theory of demarcation has seen much progress since Popper.15

Falsificationism did not completely fail. Rather, it partially failed – and so it failed to be a completely satisfying account. Nevertheless, it should still be regarded as partially correct. Many philosophers of science have retained at least some of Popper‟s claims in their own accounts of methodology. For example, Cioffi (1970) sees falsifiability as a crucial part of the „scientific attitude‟. Genuine scientists are those who are willing to test their theories; pseudo-scientists are unwilling to falsifying their theories. Thus, for Cioffi, falsifiability is a unique methodological and psychological property of science. Similarly, Godfrey-Smith (2003) argues that it is a mistake to say that theories like and Freudianism are themselves unscientific. He says we should distinguish between scientific and unscientific “ways of handling ideas” (Godfrey-Smith, 2003: 71). Science, he says, exposes ideas to the risk of falsification. Sober (1999) says a theory can be untestable for many reasons: some theories are logically untestable; some theories are untestable because of current technological limitations; some theories are untestable because the necessary evidence no longer exists (e.g. some prehistoric evidence about dinosaurs); and so on. It is the scientist‟s job to identify those theories that are testable right now: these are the ones they should work on. Sober argues that selecting for current empirical testability is unique to science. I am not suggesting that all these ideas are correct, or that any of these ideas is, in itself, an adequate solution to the demarcation problem. My point is simply that parts of Falsificationism have been employed in later accounts of science, and many philosophers still think that these parts have significant merit.16 Therefore, it must at least be plausible that Falsificationism has contributed to the progress of demarcation and was not a complete failure – and Laudan certainly hasn‟t established the opposite.

I now give a very brief sketch of some significant methodological developments since Popper, and the way in which earlier theories contributed to later ones in a progressive

15 I am not referring to the “candidates waiting in the wings” that Laudan mentions briefly and then rejects (Laudan, 1983: 122-123). The accounts of science that I am referring to are sophisticated, developed, and promising.

16 Even the „merely semantic‟ issue of falsifiability has been considered worth emphasising. Butts (1993) sees texts as the appropriate unit of demarcation (rather than, say, theories, claims or methods). He argues that scientific texts have only one permissible interpretation, and this seems to open them to criticism and refutation, whereas pseudo-scientific texts have many permissible interpretations, which protects them from criticism and refutation.

30 way. Figure 1.6 shows the connections between theories, where each arrow represents a transmission of „good bits‟ from the earlier theory to the later theory.17

Past

Falsificationism

Lakatos’ Methodology

Bayesianism

Present Experimentalism

Figure 1.6: Some methodological progress since Popper.

1. My historical diagram begins with Inductivism, a very influential view that preceded Falsificationism. It had many adherents, including J.S. Mill and R. Carnap.

2. There is a tentative (dashed) arrow between Inductivism and Falsificationism. Popper developed Falsificationism in reaction to the earlier view. He objected to the idea that we could regard scientific theories as probably true, based on inductive inference. He took the opposite position: scientific theories always have a probability of zero (Popper, 1959: vii). So, Popper presumably wouldn‟t have thought that he had taken any „good bits‟ from Inductivism. However, the long- running debate between Popper and Carnap suggests that there was enough common ground between the two views to make the differences worth arguing about.18 At the very least, Popper would have agreed that he had learnt from the mistakes of Carnap, and so progressed in the opposite direction!

17 I will not give extensive evidence for these connections. Firstly, I must assume some familiarity with the on methodology. Secondly, I‟m only claiming that my sketch is plausible, not that it is definitely correct in every detail.

18 Earlier, I pointed out that corroboration is not as different from confirmation as Popper liked to think.

31 3. Lakatos, in turn, saw his Methodology as building on Falsificationism (Chalmers, 1999: 130). Despite the differences between these two theories, it is easy to recognise several „good bits‟ of Falsificationism retained in Lakatos‟ Methodology. Principally, there is the same emphasis on making and testing novel predictions, and looking favourably on a theory if these predictions are successful.

4. Bayesianism has been around a lot longer than my diagram suggests. However, as an account of scientific reasoning, it is relatively recent. Dorling (1979) argued that the „good bits‟ of Lakatos‟ Methodology are found in Bayesianism. In fact, as Hacking argued, it seems the „good bits‟ of Falsificationism and Inductivism are found in Bayesianism too (Hacking, 2001: 256-260).

5. Many philosophers of science (e.g. (Dorling, 1979), (Horwich, 1993), and (Howson and Urbach, 1989)) believe Bayesianism shows a great deal of promise. However it is not necessarily the end of the road. Some philosophers (e.g. (Mayo, 1996)) argue that Bayesianism is just a general rule of that says nothing about many important aspects of science, such as experiments. So, I have tentatively added Experimentalism to the chain of theories that represent progress in methodology. Perhaps it can give an account of experiments in science, while making use of the rules of rationality identified by Bayesianism.19

My objection to Laudan‟s pessimistic induction does not rely on this sketch being accurate in every detail. In particular, it isn‟t supposed to be a complete of all major theories: it omits, for example, the demarcation criteria of Kuhn (1996), Explanationism (e.g. (Lipton, 2004)), and Laudan himself (1977). Nor does it rely on Bayesianism or Experimentalism being the final solution to the problem of demarcation. I claim only that something like this sketch is highly plausible, so that Bayesianism and/or Experimentalism can be viewed as significant progress in methodology. If it is plausible that significant progress is still being made, then we should not accept Laudan‟s pessimistic induction as convincing.

1.9 Conclusion

To summarise, Laudan considers five attempts at demarcation, beginning with Aristotle and ending (mysteriously) with Popper. From this extraordinarily brief history of demarcation, Laudan makes a pessimistic induction: all past attempts at demarcation have

19 Some philosophers might disagree with my characterising Experimentalism as an improvement on Bayesianism, but to discuss this further would be to go beyond the scope of this thesis and my expertise.

32 failed; therefore all attempts at demarcation will probably fail. To support this pessimistic conclusion, Laudan assumes that each of these attempts has been a complete failure, and that these failures have never led to progress in the theory of demarcation.

I argued that Laudan‟s assumptions are not correct. Many past attempts at demarcation have only resulted in partial failure, and many of these failures have led to some cumulative progress. I argued that (at worst) we should draw a more balanced conclusion: the outcome of the demarcation debate is still uncertain. Firstly, I demonstrated that recent accounts of scientific method are more sophisticated than nineteenth century accounts, which suggests that methodology has progressed. Secondly, I demonstrated that Falsificationism has some „good bits‟ that had been retained, and is not merely semantic. So Laudan‟s distinction between the Old and New Demarcationist Tradition is dubious, and this argument does not support his pessimism either. Finally, I demonstrated that Laudan‟s history of demarcation attempts ends prematurely, since a lot of progress has been made post Popper.

Therefore, I find Laudan‟s pessimistic induction unconvincing.

33 2 The Pseudo-Problem

2.1 Laudan’s Requirements and Pseudo-Problem

After his pessimistic induction, Laudan discusses what a solution to the demarcation problem should look like.

Firstly, he says there are clear cases of science and clear cases of non-science, and a demarcation criterion must agree with these. I shall call this requirement „accuracy‟, i.e. the criterion accurately classifies the clear cases. Laudan notes that this requirement reflects a difference between the current demarcation problem and, say, Aristotle‟s demarcation problem (Laudan, 1983: 117). Aristotle had no cases of established science to consider, so his demarcation criterion could be extremely legislative. He could specify criteria that were displayed by no actual cases, i.e. his category „science‟ could be empty. In contrast, we have many cases of established science (as well as non-science) to consider. We should only use criteria that are displayed by these clear cases, and our demarcation criterion must be accurate. Any criterion that does not accurately classify established science as „science‟ (and established non-science as „non-science‟) will not be considered an adequate solution. For example, if I were to propose a solution that classifies Quantum and as pseudo-science, or and as science, then my solution would probably be considered a failure. I would have seriously misinterpreted some of our most important paradigmatic cases. As Laudan says, “A failure to do to these implicit sortings would be a grave drawback for any demarcation criterion” (Laudan, 1983: 118).

Secondly, Laudan says there are a number of unclear or difficult cases, and a demarcation criterion should also classify these as science or non-science (Laudan, 1983: 118). I shall call this requirement „precision‟, i.e. the criterion is so precise that it can classify even unclear cases. Laudan claims that we need to be able to say in every case whether or not something is science. A criterion that cannot perform this function is “no better than no criterion at all” (Laudan, 1983: 118).

Thirdly, Laudan says that a demarcation criterion should make science epistemically superior to non-science (Laudan, 1983: 118). I shall call this requirement „epistemic superiority‟. There are various ways in which science might differ from non-science. For example, scientists might make more money or know more than non- scientists. However these distinctions are epistemically unimportant. We want to know what (if anything) is special and superior about scientific knowledge. Any adequate 34 demarcation must explicate the general belief that science is superior in some way, e.g. science has superior methods, stronger evidential grounds, or more reliable theories. This requirement is not merely supported by a desire to vindicate normal judgements about science (as in the requirement of accuracy). Laudan also argues that if it turns out that science is not epistemically superior, then the demarcation between science and non- science has no philosophical significance (Laudan, 1983: 118). Therefore, spending time and effort trying to solve the demarcation problem presupposes (pragmatically) that science is epistemically superior.

Finally, Laudan asserts that exactly the same demarcation criterion should demarcate all cases, so that it identifies an “invariant” property (Laudan, 1983: 124). I shall call this requirement „invariance‟. An invariant is, literally, something that does not change, and in this context it is a property displayed by all and only cases of science. Laudan claims that the demarcation problem presupposes that there is such an invariant property (Laudan, 1983: 124).

To summarise, Laudan has specified four requirements for an adequate demarcation criterion:

1. Accuracy;

2. Precision;

3. Epistemic superiority; and

4. Invariance.

However, Laudan argues that there is no such criterion. We now know enough about what passes for science, he says, to see that it is “not all cut from the same epistemic cloth” (Laudan, 1983: 124). Some scientific theories are well-tested, but some are not. Some scientific theories are making cognitive progress, but some are not – and so on. In fact, he argues, there is nothing epistemically homogeneous about the variety of theories we currently call „scientific‟. Therefore, no adequate criterion for demarcation exists (Laudan, 1983: 124).

35 The trouble is that the demarcation problem presupposes that such a criterion exists (Laudan, 1983: 124). Since the problem presupposes a false assumption, it is a pseudo- problem, and we ought to abandon it. This argument can be represented deductively:

P1. The demarcation problem presupposes that there is a particular epistemic invariant that occurs in all and only science. (claim)

P2. The , that there is a particular epistemic invariant that occurs in all and only science, is false. (claim)

P3. If a problem makes a false presupposition, then it is a pseudo-problem. (by definition20)

C. The demarcation problem is a pseudo-problem. (from P1, P2 and P3)

2.2 Requirement One: Accuracy

Laudan says that we have a number of clear cases of established science and non-science to consider. This is both a blessing and a burden. It is a blessing because we can derive a demarcation criterion a posteriori from these cases – we don‟t have to solve the problem a priori from first principles. It is a burden because our demarcation criterion must accurately classify these clear cases (Laudan, 1983: 117-118). He says (1983: 117):

It is inconceivable that we would find a demarcation criterion satisfactory which relegated to unscientific status a large number of the activities we consider scientific or which admitted as sciences activities which seem to us decidedly unscientific.

2.2.1 Objection: Demarcations can be legislative Deriving a demarcation criterion a posteriori from established cases seems to be a good way to proceed. Generally, we have stronger about paradigmatic particular cases than we have of abstract concepts or general principles. Moreover, the demarcation criterion cannot disagree with too many clear cases, because then it would demarcate something other than science.

However, once identified, this demarcation criterion may take on a „ of its own‟ – it could become the accepted demarcation of science, and so provide a legislative function.21

20 Presupposing a false assumption is not supposed to be a necessary condition to be a pseudo-problem, only a sufficient one. There may be other sufficient conditions to be a pseudo-problem. In fact, Laudan argues that the demarcation problem satisfies two such conditions: it presupposes a false assumption; and also, there is a better problem that should replace it.

36 So, if it turns out that it disagrees with a small proportion of previously clear cases, we might keep it anyway. We might even revise our opinion of the conflicting cases, rather than revising the criterion, until we reach a stable, consistent view.22 The philosophical demarcation project does not presuppose that our naive intuitions will all be correct; it is quite possible that we will need to rethink our assumptions about science. Therefore, perfect accuracy is not required.

2.2.2 Sub-conclusion: Reasonable accuracy is sufficient Laudan says that an epistemic invariant should be accurate – i.e. present in the clear cases of science, and absent in the clear cases of non-science. Laudan does not appear to insist on perfect accuracy, so I take it that he would agree with my suggestion that an epistemic invariant should be reasonably accurate, but we can allow a little room for error.

2.3 Requirement Two: Precision

Laudan argues that we should be able to say in every case whether or not something is science, even in unclear or difficult cases. He says, “without conditions which are both necessary and sufficient, we are never in a position to say „this is scientific: but that is unscientific‟ ” (Laudan, 1983: 119), moreover “the criterion must have sufficient precision that we can tell whether various activities and beliefs whose status we are investigating do or do not satisfy it” (Laudan, 1983: 118). To satisfy this requirement: a) The epistemic invariant must be present in every case of science – including the unclear ones; b) The epistemic invariant must be absent in every case of non-science – including the unclear ones; and c) It must be evident in every case whether or not the epistemic invariant is present.

Laudan says the demarcation criterion needs to be more precise than our ordinary concept of science.

21 In fact, this is Laudan‟s (1982) concern when he considers the Arkansas Trial. He argues that Judge Overton‟s ruling rests on a demarcation that misrepresents what science is and how it works. He is concerned that this ruling will set a precedent for other trials of this kind – and there seem to be a lot of them in the USA – so he tells us that this ruling “may come back to haunt us” (Laudan, 1982: 48).

22 This is what Rawls (1999) called (in relation to moral theory) the process of reaching „‟.

37 2.3.1 Objection: Precise enough for the specific purpose To be precise enough for Laudan, an epistemic invariant will be present and discernible in every case of science. To illustrate why this requirement might be too strong, I shall consider an with the demarcation of diamond.23

The criterion „pure carbon crystallised in octahedrons‟ demarcates diamond from all imitations. But for some purposes, e.g. valuing jewellery, this demarcation won‟t do. Identifying gem stones on the basis of their chemical structure is costly and invasive – it is not something for which the valuer has the equipment or expertise. Instead, she must use a number of distinguishing properties of diamond such as colour, , impurities, hardness, sparkle, reflection and refraction. While these properties are typically displayed by diamonds, some of these properties are indiscernible in some diamonds (depending on the quality, cut and setting of the stone); conversely, some of these properties can be discerned in some imitations. So the valuer‟s identifications must be performed in a piecemeal fashion: she will continue to make until she is sure whether or not the stone is a diamond. The chemical structure of diamond identifies a „theoretical essential‟ that is present in every case of diamond, but it cannot be discerned in any case. The valuer‟s „mixed bag‟ of tests identifies „practical indicators‟ that are not individually present in every case of diamond, but some combination of these can be discerned in every case.

Similarly, it is conceivable that an epistemic invariant may be present but indiscernible in science. For argument‟s sake, suppose we define science as „an activity where the fundamental aim is to generate novel, empirical ‟.24 This criterion could be present in every case, and yet not be discernible in any case. Looking at a set of practices, how can we discern the fundamental aim? To identify particular cases of science, we might need to identify practical indicators such as methods and procedures. So to tell in every case whether or not something is science, we may need to employ a „mixed bag‟ of properties – practical indicators rather than a theoretical essential.

This kind of demarcation, in which science is like diamond, should be sufficient for both philosophical and practical purposes.25 For philosophical purposes, we might be most interested in the theoretical essential. This satisfies Laudan‟s requirements (a) and (b), but

23 For a more detailed discussion of this analogy, see Appendix A, section A.5.

24 I do not endorse this simplistic definition.

25 There are several possible purposes for the demarcation of science, as identified in section 0.1.

38 not his requirement (c): it does not tell us in every case whether something is a science. Nevertheless, it can provide some useful insight. For practical purposes, we might be most interested in the practical indicators. These may be useful, by telling us in some cases whether something is a science. But they do not seem to satisfy Laudan‟s requirements (a) and (b), since different indicators are present in different cases. Nor will they necessarily satisfy Laudan‟s requirement (c), since a mixed bag of indicators may not tell us in every case whether something is a science.

This shows us two things. Firstly, the same concept – such as science – may require different demarcations for different purposes.26 Secondly, an adequate demarcation criterion does not need to be perfectly precise and give us all the answers, in the manner that Laudan suggests. Demarcations only need to be precise enough to be helpful for the specific purpose.

2.3.2 Objection: Ordinary vagueness could be replicated A set of individually necessary and jointly sufficient conditions is usually considered to be a very neat and precise way of defining a concept.27 While this level of precision can be appropriate for defining geometrical and mathematical concepts, it often seems to be inappropriate for defining ordinary concepts.28 In particular, most ordinary concepts admit instances that have a vague or borderline status. Some philosophers argue (e.g. (Thagard, 1988)) that a set of individually necessary and jointly sufficient conditions cannot replicate this vagueness; instead, it will increase the precision of the concept by specifying a sharp line of demarcation. This will turn vague cases into either clear instances or clear non- instances. Such a definition will either be too broad or too narrow, or it will fail to match the degree of precision of the concept. If we just want to describe an ordinary concept, then it is better to replicate its ordinary vagueness.

Many ordinary concepts seem to be defined by : something is an x if it is similar enough to other things that are an x (Rey, 1998). For example, Wittgenstein famously claimed that game was defined by family resemblance (Wittgenstein, 1953: §66).

26 For a more detailed discussion of using different kinds of demarcation for different purposes (mostly demarcations that are not perfectly precise), see Appendix A, sections A.3 and A.4.

27 I noted in section 0.1 that demarcations and definitions are similar. In this section I shall discuss the logical structure of definitions, but I take it that similar issues arise with respect to the logical structure of demarcations.

28 For more discussion of ideal definitions, see Appendix A, sections A.1 and A.2.

39 To be similar enough to other xs, presumably it must share enough of the same properties (to a great enough extent). However there need not be any property that is common to all xs, which is necessary to be an x. Furthermore, judgements of overall similarity are usually not completely precise. Hence, there are often cases where we are not sure if something is similar enough to be counted as an x. So, many ordinary concepts are vague, and in particular, family resemblance concepts are very common and usually vague.

At least three kinds of definition could be used as a demarcation criterion:

1. A definition that specifies the ordinary meaning of the concept;

2. A definition that clarifies the concept; and

3. A definition that explicates the concept.

Since many ordinary concepts are vague, we should not expect the ordinary concept of science specified in (1) to be perfectly precise. So, we might prefer to use something more precise, and the form of (2) or (3). However, demarcating our ordinary concept of science is of philosophical interest in itself, and for this purpose only (1) is correct. Furthermore, (1) might still be helpful for some practical purposes, e.g. as a starting point for a legal argument about „‟. Therefore, for some purposes, we would settle for less than perfect precision.

2.3.3 Sub-conclusion: Moderate precision is sufficient Laudan says that a demarcation criterion must be very precise. To be precise enough, it must be present and discernible in every case of science. My argument against perfect precision is two-pronged. Firstly, I argued from the diamond example that it may not be possible to specify an epistemic invariant that is present and also practically discernible in every case. Nevertheless, imperfect precision can be helpful for specific purposes. Secondly, I argued that ordinary concepts are often vague, but it is possible to specify definitions that replicate this vagueness. We might want to describe the ordinary concept of science, and this is quite likely to be vague – so for this purpose we would accept a non- ideal, imprecise demarcation criterion. Thus, our minimal requirement for a demarcation criterion should only be moderate precision.

2.4 Requirement Three: Epistemic Superiority

Laudan observes that there are many ways in which science differs from non-science. For example, scientists often wear white lab coats, and tend to receive more public funding than non-scientists. But these distinctions are not “philosophically interesting”. Laudan

40 says “we want to know what, if anything, is special about the knowledge claims and modes of inquiry of the sciences” (Laudan, 1983: 118). So he says a demarcation criterion should make science epistemically superior to non-science – epistemic superiority is “philosophically interesting”. Moreover, he says, the demarcation problem presupposes that science is epistemically superior to non-science.

2.4.1 Objection: Only epistemic significance One might argue that working on the demarcation problem doesn‟t presuppose epistemic superiority, only epistemic significance. By significance I mean that there is some difference between science and non-science that is worth taking the trouble to identify. I can imagine a philosopher presupposing that science is epistemically distinct in a significant way, but not superior. They might advance the following argument:

To say that one activity is epistemically superior to another activity, one needs to be able to compare them in terms of their epistemic merits. But in some cases, this may not be possible. Suppose, for example, that we want to distinguish between History and the sciences. We might say that they differ in their subject matter. History seems to be concerned with the particular sequence of past events for which we have contemporary human records. In contrast, the sciences seem to be concerned with general patterns in events, which may well be replicated (in the past, present, or future). Consequently, the appropriate methodology also differs. History tends to involve the collection and interpretation of human records. The sciences tend to involve experiments to replicate patterns of interest. If we say that the sciences are epistemically superior to History, then this suggests that the more History was like a science, the better it would be. Yet if History became too much like a science, then it would no longer be History! To avoid this, perhaps we should say that the sciences are not epistemically superior to History, only different.29

I do not need to evaluate whether this particular argument is convincing or the conclusion is correct. My point here is that even if it is correct, this line of argument is difficult to sustain for all other knowledge claims. While there are some activities that we may be reluctant, or unable, to say are epistemically inferior to science – for example,

29 This demarcation between History and the sciences need not be completely sharp for this argument to succeed. For instance, it might be conceded that scientific methods can sometimes be used in historical enquiry, and that History would be better if this happened more often! Nevertheless, it might be argued that History could never become entirely scientific.

41 Philosophy and History – most philosophers do not want to say that science is no better or worse than anything. This is epistemic , a view that is very difficult to defend (and is usually held by academics who are particularly unscientific!). Some philosophers may be willing to declare, in the abstract, that science is not epistemically superior to other activities. But when it comes to particular cases, most philosophers of science want to be able to claim, for example, that Western is epistemically superior to Homeopathic medicine, and that Evolutionary theory is epistemically superior to Creationism. Moreover, it looks as though the distinction between Western medicine and Homeopathic medicine, and the distinction between Evolutionary theory and Creationism, rests on more than the simple fact that one is true or well-confirmed and the other is false or ill-confirmed. The difference appears to run deeper: it is a distinction between two fundamentally different ways of investigating phenomena or acquiring knowledge, and one of them seems to be better than the other.

In practice, most demarcation attempts seem to presuppose superiority, not just significance. The reputation held by science leads many philosophers to suppose that if science is in any way distinct, it is also superior. Since I am sympathetic to that view myself, and view epistemic relativism as very difficult to defend, I won‟t dispute Laudan‟s requirement of epistemic superiority.

2.4.2 Objection: Indirect epistemic virtues If science is epistemically superior to non-science, then presumably some of the distinctive properties we need to identify are epistemic virtues. Laudan never explicitly says what kind of property he would call an epistemic virtue. But from his paper, we can get a pretty good idea:

1. Truth is an epistemic virtue. 2. Things are also epistemic virtues if they tend to produce truth in a very direct way. 3. No other things are epistemic virtues.

For example, on Laudan‟s conception, „being well-tested‟ is an epistemic virtue. If a belief is well-tested, then presumably this makes the belief more likely to be true. So it qualifies according to (2). I shall call all the things identified by this definition „direct epistemic virtues‟.

A sceptic might object that being well-tested does not necessarily produce truth. For example, a well-tested belief may not be true if a contrary belief has just been proposed that is extremely plausible. Nevertheless, being well-tested tends to help. Hence, in this context, it is the kind of „epistemic virtue‟ we need. If a demarcation criterion specified that 42 scientific beliefs have the virtue of being well-tested, then this would help to explain why science is epistemically superior.

However, I claim that this notion of epistemic virtue is too narrow. It excludes some properties that are actually very important to making science epistemically superior. For example, selective funding, a large number and high quality of participants, vigorous competition, systematic criticism and peer review are widely regarded as important features of science. I suggest that these properties also tend to produce truth. But they do so more indirectly, and may require appropriate circumstances to be effective (such as the presence of other virtues). I shall call them „indirect epistemic virtues‟. Some might dismiss them as merely social properties. But, in this context, they are the kind of „epistemic virtues‟ we need. If a demarcation criterion specified that sciences have these social arrangements, then this would help to explain why science is epistemically superior. So, when we take a broader view of epistemic virtue, it is easier for a demarcation criterion to satisfy this third requirement.

I have used to argue that Laudan‟s concept of epistemic virtue is too narrow. I do not claim to have a perfect definition of this concept, nor do I need to provide one for my argument. But a better formulation would be:

1. Truth is an epistemic virtue.

2*. Things are also epistemic virtues if they tend to produce truth, directly or indirectly, in appropriate circumstances.

3. No other things are epistemic virtues.

(2*) is rather vague: we need to use our judgement to decide what circumstances are appropriate. But it is an improvement on (2), because it is much broader. (2*) can include all the virtues identified by (2), and can also include other important factors like the social arrangements I have specified.

2.4.3 Reply: Too many virtues Laudan might reply that (2*) would count too many things as virtues: either a huge number of things, or things that have only a very remote and weak tendency to produce truth.

My rejoinder is that we can include as many factors as we like, provided that we don‟t regard them all as equally important for demarcation. We can identify some important, distinctive virtues that tend to produce, in science, significantly more truth than occurs in non-science. In contrast, we can dismiss many virtues as relatively unimportant, because they are not distinctive to science and/or the comparative benefit they produce is small 43 (e.g. caffeine to combat drowsiness, or white lab coats for protection against chemical spillage). Thus, we can focus on a manageable list of important virtues.30

2.4.4 Reply: Too unreliable Laudan might reply that these „indirect epistemic virtues‟ shouldn‟t be counted as virtues at all, because they aren‟t connected reliably enough to truth or justified belief.

Features such as white lab coats, textbooks and fume cupboards are all part of the scientific tradition, but are relatively incidental to its epistemic virtue. They can easily be imitated by pseudo-scientists, without making pseudo-science any more virtuous. Even the social arrangements that I have mentioned could be imitated in this way, without bringing any benefit. For example, if pseudo-scientific results are not judged by their accuracy, novelty and usefulness, then selective funding might help pseudo-scientists to get more results – but these results might not be of any real value.

I agree that compared to the direct virtues, it is easier for the indirect virtues to be present without actually promoting truth or justified belief. To be effective, they may need appropriate circumstances – including, perhaps, the presence of other virtues. Yet I maintain that something like selective funding can still be counted as a virtue, because it is effective in promoting good results when it is combined with the other scientific virtues under normal circumstances. Under these circumstances, selective funding is positively correlated to better performance.

Laudan might concede that in appropriate circumstances, the indirect virtues are positively correlated to epistemic success. But he might say that they are merely „indicators‟ of epistemic virtue, not virtues in themselves.

My rejoinder is that the term „indicator‟ is too weak. Properties such as selective funding do not merely indicate epistemic virtue; they generate epistemic virtue. Moreover, their effectiveness often involves a positive feedback loop: every time they generate epistemic virtue, it becomes more likely that they will generate epistemic virtue again. For example, selective funding is not bestowed on everyone who asks for it. Researchers must apply for grants. They must say what they plan to do with the money; how long it will take

30 Those philosophers who do not think it is realistic to aim for truth might think that this revised definition would be even better if we substituted something else for „truth‟, for instance, „epistemic warrant‟. For my purposes, I do not need to take sides on this issue. My point here is simply that, whatever the ultimate virtue may be, we should take a broader view of what counts as an epistemic virtue (by including more things that tend to produce the ultimate virtue).

44 to get results; how likely it is that they will get results; how useful their results will be; and so on. If they are fortunate enough to receive funding, then they need to generate the promised results. If they manage to generate the results, then they probably will get more funding. If they don‟t generate the results, then they probably won‟t get more funding. This virtuous (or vicious) cycle is depicted in Figure 2.1.

Figure 2:

Selective Getting Funding Results

Figure 2.1: Two epistemic virtues in a positive feedback loop.

Of course, this system is fallible. Success of grant applications depends a lot on the quality of the application, who is applying, who is assessing the applications, and the nature of the proposed work. Some proposals that would yield epistemic success are rejected in favour of proposals that won‟t. But in the long run, I suggest that this system tends to generate epistemic success. It is fallible, but nonetheless reasonably effective.

2.4.5 Sub-conclusion: Indirect epistemic virtues are sufficient Laudan seems to assume that the only epistemic virtues in science are the direct epistemic virtues. I have argued that there are important indirect epistemic virtues as well. So, we should interpret „epistemic virtue‟ more broadly than Laudan. The „epistemic invariant‟ may include direct epistemic virtues, indirect epistemic virtues, or some complicated combination of the two kinds of virtue. This gives us more latitude to find a suitable demarcation criterion.

2.5 Requirement Four: Invariance

Laudan thinks that the same demarcation criterion should demarcate all cases, and do so by picking out an „invariant‟ property. In this context, an invariant property is one that is displayed by all and only cases of science. Laudan argues that the demarcation problem presupposes that there is such an invariant property (Laudan, 1983: 124).

2.5.1 Variance and complexity In order to determine whether or not the demarcation problem presupposes an invariant, first we need to understand the possible logical structures of an invariant property. I take it

45 that Laudan is imagining something like a property P, which corresponds to the property of being science, S, as follows:

(x)(Sx↔Px)

So, property P is displayed by all cases of science and no cases of non-science. Now, if a property was completely invariant, it would be displayed (invariably) by all cases. But note that P is only displayed by some cases; i.e. cases of science. Technically, P is variant, not invariant. The invariant here is really the meta-property Q, where:

(x)Qx (x)(Qx ↔ (Sx↔Px))

According to logical principles, variance can be converted to invariance simply by „going up‟ a level. I do not present this as an objection to Laudan, only as a technical clarification of what he means. Presumably, when Laudan talks about an „invariant‟ he is really talking about P: something that is invariably present in cases of science. i.e.: a) (x)(Sx↔Px)

Equivalently, we can specify a rough definition of a suitable invariant, beginning with:

i. Invariant means „a property that is displayed by all and only cases of science‟.

One may well object that i is too narrow, since often a demarcation criterion tries to specify a set of properties that are individually necessary and jointly sufficient. For example:

b) (x)(Sx↔(P1x & P2x & P3x))

This would surely be sufficiently invariant for philosophical or practical purposes, but my current formal definition of a suitable invariant i does not seem to reflect this.

There are two ways that I could correct my definition to respond to this objection. I could say that an invariant is „either a single property that is displayed by all and only cases of science, or else a conjunction of properties that are jointly displayed by all and only cases of science‟. This would complicate my definition. The alternative is to say that the word „property‟ is meant to be a broad term that includes both individual properties and conjunctions of properties. According to logical principles, the technique we used to convert variance to invariance can be used to describe a set of properties as a new single property:

46 (x)(Sx↔Px)

(x)(Px ↔ (P1x & P2x & P3x))

In this sense (b) is just as invariant as (a), if we can count conjunctions of properties as new properties. This would then preserve my simple definition of an invariant i, since (b) becomes an example of i instead of a counter-example. So I will choose to clarify i with:

P. A property may be an individual property or a conjunction of other properties.

But, again, one might object that i is too narrow – since a reasonable demarcation criterion could specify two properties that are individually sufficient, but not individually necessary: only one or the other is necessary, but not both:

c) (x)(Sx↔(P1x  P2x))

This disjunctive form seems to be sufficiently invariant to provide a decent definition of science. But again, i does not seem to reflect this.

Similarly, a reasonable demarcation criterion could specify the inclusion of one property and the exclusion of another. For example, Falsificationism tells us that all and only cases of science are falsifiable but not yet falsified. If P1 is the property of being falsifiable and P2 is the property of having been falsified, then we have:

d) (x)(Sx↔(P1x & ~P2x))

This certainly seems to fit our notion of a suitable invariant just as well as both of the previous criteria, but i does not seem to reflect this, since i does not include negation.

Once again, there are two possible replies to these objections. I could complicate my definition by saying that an invariant can be either a property or a conjunction of properties or a disjunction of properties or the negation of a property. Or I could keep my definition i and just say:

P*. A property may be an individual property, a conjunction of other properties, a disjunction of other properties, or the negation of another property.

If we may combine properties to form complex properties, then presumably we may combine complex properties to form even more complex properties. Certainly, my definition P* allows this, because it can be applied repeatedly. So we can obtain:

47 (x)(Sx↔Px) (x)(Px ↔ (Qx Rx  Tx))

(x)(Qx ↔ (Q1x & Q2x & Q3x))

(x)(Rx ↔ (R1x & ~R2x))

(x)(Tx ↔ (T1x  T2x))

So in this case,

(x)(Px ↔ ((Q1x & Q2x & Q3x)  (R1x & ~R2x)  (T1x  T2x)))

One property can really stand for any number of other properties, in various logical combinations. So any set of properties, no matter how complex, can be re-described as „a complex property‟.

I have shown that through some principles of logic, we can „cover up‟ all manner of variance and complexity:

1. Variance at one level can be converted to invariance at a higher level; and

2. Complexity at one level can be converted to simplicity at a higher level.

It looks as though Laudan‟s invariance requirement is easily achieved just by manipulating the level of description. In this case, an epistemic invariant may be extremely complex at one level, and yet extremely simple at another.

2.5.2 Objection: An extremely complex invariant If we allow an unlimited degree of logical complexity, then one might argue that there must be some complex property that is an invariant property of science. But if this were spelled out in terms of simpler, recognisable properties, then what would such a complex property look like?

It could be a complex property that contains as many properties as there are cases of science. For example, if A is being Physics, B is being , and C is being :

(x)(Sx↔Px) (x)(Px ↔ (Ax  Bx  Cx))

We could add as many disciplines as necessary, or identify what is scientific at the level of sub-disciplines instead.

48 In this way, P could „pick out‟ all the cases of science – but Laudan wouldn‟t accept this as an epistemic invariant, because A, B and C are names, not epistemic properties. To get around this objection, we might be able to make P even more complex:

(x)(Sx↔Px)

(x)(Px ↔ ((A1x & A2x … Anx)  (B1x & B2x … Bnx)  (C1x & C2x … Cnx)))

Where A1 to An are the epistemic properties of Physics, B1 to Bn are the epistemic properties of Chemistry, and C1 to Cn are the epistemic properties of Biology.

Presumably, these sets of properties would include methodological rules at various levels of description. Since many non-sciences appear to employ „scientific‟ methods and procedures, it would be challenging to specify enough properties to distinguish Physics, Chemistry and Biology from these non-sciences. But we need not be restricted to such methods: the subject matter might be used as one of the key distinguishing features.

2.5.3 Reply: The demarcation criterion must be simple Laudan doesn‟t deny that it might be possible to specify all the methods and goals of the various cases of science. But large and complex sets of properties would not satisfy his notion of an epistemic invariant, even if they are re-defined as a single new property. His idea of an epistemic invariant is a property that is genuinely extremely simple, before any special logical trickery.31

Laudan would surely argue that proposing a complex property like this one as a demarcation criterion would beg the question. The property is designed in an way, just to pick out all the cases of science. It appears that the only thing that makes the selected cases count as science is really just the fact that we already think they are cases of science. The complex property might include only epistemic properties, but there is no prima facie reason for including those epistemic properties and excluding others. A demarcation needs to give a deeper reason for its classification. The problem with a complex property is that it is not clear why that particular set of properties should have any special status, or even why they should be grouped together at all. A demarcation criterion must do more than artificially „pick out‟ all the cases of science: it must unify them. An extremely simple property (if there is one), will presumably identify a genuine commonality.

31 I take it that we can count some properties as genuinely more complicated than others. These can usually be broken down into simpler properties, which in turn might also be broken down into even simpler properties. It is difficult to say where this might end.

49 This argument is a strong one, and I accept that this kind of arbitrarily complex, ad hoc property will not suffice. But must we except extreme simplicity?

2.5.4 Rejoinder: Demarcation needn’t be extremely simple I imagine that Laudan would argue for the opposite extreme: the demarcation problem presupposes an extremely simple property. Yet this view cannot be correct either.

If the demarcation problem presupposes an extremely simple property, then when we examine the recent history of the demarcation problem, we would expect to see two things:

1. Most demarcation attempts specify extremely simple properties or demarcation criteria; and

2. Any non-simple solutions have been criticised because they are not extremely simple.

Accounts of science can be roughly divided into monist accounts, i.e. one property unites all cases of science, and pluralist accounts, i.e. there is no property that unites all cases of science.32 On monist accounts, a demarcation criterion can be extremely simple; and Laudan appears to assume that only monist accounts of science support demarcation. As an empirical fact, it does seem that pluralism is correlated to epistemic relativism about science.33 However, there are at least a few philosophers who support pluralist accounts of science, and yet think we can demarcate science in a way that is epistemically significant. I shall consider solutions proposed by three pluralists: Thagard, Lugg and Derksen.34 The demarcation criteria they propose are more complex than those proposed by monists.

Thagard (1988) thinks that the various scientific disciplines, fields, programs and theories display a „family resemblance‟. He specifies a „conceptual profile‟ for science and a contrasting conceptual profile for pseudo-science (Thagard, 1988: 170), but he expects that most cases of science will display some properties from both profiles. I consider Thagard‟s demarcation relatively complex, for three reasons. Firstly, he uses more properties than, say, Falsificationism. Secondly, some of these properties seem inherently quite complex. Thirdly, the logical function required to capture „family resemblance‟ is considerably more complex than, say, a conjunctive definition. Thagard thinks this complexity is unavoidable

32 This is a useful dichotomy, but not intended to be a sharp one. Sankey (2000) notes that various hybrid theories of scientific methodology have been proposed.

33 Sometimes this relativism is intentional, e.g. (Feyerabend, 1975). Sometimes it seems to be unintentional, e.g. (Kuhn, 1996).

34 For more detail of these complex solutions, see Appendix B.

50 – science is complex, so the demarcation criterion must be complex too. His conceptual profiles have been criticised by many and accepted by few. I have not introduced them here to add either my criticism or my support. Rather, I am interested to note the kinds of criticisms that have been made. This solution has been criticised because it is not accurate with respect to the clear cases of science and clear cases of pseudo-science (Derksen, 1993). Thagard‟s solution has not been criticised because of its complex structure.

Lugg (1987) argues that pseudo-sciences can be identified as such because they are structurally flawed, and each can be flawed in a different way. His demarcation criterion could become extremely complicated: he might specify as many kinds of flaws as there are cases of pseudo-science. Once again, this demarcation criterion has not been accepted by the majority of philosophers. It has been criticised for its circularity and scientific prejudice (Derksen,1993). But the point I wish to stress is that Lugg‟s solution has not been criticised for its complexity.

Derksen (1993) argues that the key to distinguishing between science and pseudo- science is to realise that pseudo-scientists are pretending or trying to be scientists. By identifying the characteristic pretensions of the pseudo-scientist, i.e. “the seven sins of pseudo-science”, we can distinguish between science and pseudo-science. Like Thagard, he says these are not individually necessary conditions for pseudo-science; but they are jointly sufficient. Derksen‟s seven-sin solution has not been accepted by the majority of philosophers, and has been criticised for being just as circular and prejudiced as Lugg‟s solution (Lugg, 1995). Again, the point I wish to emphasise is that Derksen‟s solution is just as complex as those of Lugg and Thagard – yet no one has criticised it for its complexity.

Philosophers are not always astute, but they are seldom stupid. If the demarcation problem presupposes an extremely simple epistemic property, then we should expect to see few, if any, complex solutions proposed. Yet I have outlined three complex solutions to the demarcation problem that have been proposed – and there are several others.35 We should expect to find that these complex solutions have been criticised for their complexity. Yet the three complex solutions I have considered have not been criticised for their complexity. They have been criticised because they are inaccurate, non-epistemic and prejudiced – not because they are complex.

35 For example, (Resnik, 2000), (Reisch, 1998), (Kuhn, 1996).

51 One can certainly argue that the complexity of these criteria makes it more difficult to apply them. For example, to assess Thagard‟s family resemblance we presumably need to weigh up his multiple factors in an unspecified way, in order to decide if an area of knowledge is scientific or unscientific. However, while this task may be difficult, it is not obvious that a family resemblance criterion of this kind could never be a viable solution. Therefore, Laudan has not established that such a solution is too complex to be viable.

Furthermore, there are many other possible solutions of this (or greater) complexity, that Laudan has not managed to rule out. For example, suppose we developed some way of measuring the degree to which various factors were present in an area of knowledge, and some mathematical rule for how to combine them, which seemed to satisfy our intuitions. Then we might have succeeded in developing a criterion that is more complex than Thagard‟s, yet more precise and with a better-specified method of application. Laudan has not established that all such criteria must be too complex to be viable solutions.

2.5.5 Sub-conclusion: Moderate invariance is sufficient To summarise, Laudan argues that the demarcation criterion must be an invariant property. I have argued that properties can be more or less variable, and that they range from extremely simple to extremely complex. I have shown that any set of properties, no matter how complex, can be described as a single property – and indeed, an invariant one.

So, one could argue that it is possible to specify an extremely complex set of epistemic properties that functions as an epistemic invariant. However, this would probably not be satisfactory. A demarcation criterion should identify some genuine „commonalities‟, and not just an artificial, extremely complex invariant property.

I imagined that Laudan would argue for the other extreme view: the epistemic invariant should be extremely simple. However I have argued that the demarcation problem doesn‟t presuppose such a simple property. For philosophical and practical purposes, there is no reason why a moderately complex property should not suffice. Philosophers implicitly seem to agree with this assessment: many such criteria have been proposed, and yet these criteria have not been criticised for their complexity.

So I claim that a demarcation property can between the two extremes: it mustn‟t be too variant (or equivalently, complex), but it can be moderately variant (or complex).

52 2.6 The Moderate Demarcation Criterion

Laudan specifies four requirements for a solution, which I have considered in turn. I am now in a position to provide my own contrasting list of minimum requirements for a solution, as shown in Figure 2.2.

Laudan Walsh

Accuracy Reasonable Reasonable

Precision Perfect Moderate

Epistemic Superiority Direct Indirect

Invariance Extreme Moderate

Figure 2.2: My alternative requirements for a demarcation criterion.

I have argued that, with the exception of accuracy, the requirements for a demarcation criterion should be more moderate than Laudan has demanded. The demarcation problem does presuppose an epistemic invariant, but it only needs to be reasonably accurate, moderately precise, indirectly epistemic and moderately invariant. Now I shall move on to Laudan‟s argument for P2, and ask „Is there a suitable epistemic invariant?‟

2.7 The Existence of the Epistemic Invariant

Laudan claims that there isn‟t an epistemic invariant in science. He declares emphatically, “The evident epistemic heterogeneity of the activities and beliefs customarily regarded as scientific should alert us to the probable futility of seeking an epistemic version of a demarcation criterion” (Laudan, 1983: 124 – Laudan‟s italics). This argument can be reconstructed as follows:

1. Science is epistemically heterogeneous. 2. If science is epistemically heterogeneous, then probably there is no epistemic invariant in science. 3. Probably there is no epistemic invariant in science.

Laudan‟s argument seems plausible, yet is less convincing after closer examination.

The presence of a great deal of heterogeneity is quite consistent with the presence of a moderately complex invariant. One reason for this is that there are many aspects of science. Therefore, many aspects can vary while a few aspects remain the same. Similarly, cricket balls, British telephone boxes, clown noses and poppies have very little in common apart from the fact that they are red. Red things are heterogeneous in almost every way; 53 they are only homogeneous in one respect. Another reason is that a moderately complex logical function, such as a family resemblance between the sciences, allows all simple properties to vary while maintaining a more complex invariant. Similarly, there are considerable differences between the faces of my family members, yet strangers can detect a distinct family resemblance.

Nobody expects all cases of science to be the same in every way: we already know that there will be many differences in subject matter and investigative techniques, and possibly deeper differences as well. But this is quite consistent with the presence of an invariant – particularly a deep property like my ultimate goals, or a moderately complex property like Thagard‟s conceptual profiles. So it simply does not follow that discovering an epistemic invariant is improbable (i.e. has a probability significantly less than 50%) just because we see a lot of heterogeneity.36

Furthermore, we should not confuse lowering a probability with causing a low probability. Laudan‟s second premise would be much more plausible if it were:

2'. If we discover more epistemic heterogeneity in science, then this lowers the probability that there is an epistemic invariant in science.

This must be true, if discovering the additional heterogeneity eliminates some of the plausible epistemic invariants that we thought might exist in science. But it does not follow that lowering the probability that an invariant exists (say, by 5%) will reduce the probability below 50%.

To make such a calculation, we would need to take into account numerous other factors, which Laudan does not quantify. What was the prior probability that a suitable invariant exists? When philosophers began their search, their hopes were no doubt very high, given that the various sciences were already intuitively placed in the same class (science), and they do appear to share many similarities. So the initial probability of success might have been as high as 90%. What other information have philosophers discovered, that might have caused them to revise this probability? I argued that there has been significant philosophical progress, which should be a factor that significantly increased the estimated probability of success. If we take all the positive and negative factors into account (including initial optimism, much heterogeneity, many similarities, numerous failures, significant progress, and many remaining possibilities that are only moderately

36 We may need to see extreme or maximal heterogeneity before the probability of any invariant is low. This is a possibility I will discuss shortly.

54 complex) then it is not at all clear that the probability of an epistemic invariant must be less than 50%, as Laudan claims.

In fact, Laudan needs to do more than establish that the conclusion is probable. Suppose that the remaining probability of finding an invariant is 40%. Solving the demarcation problem has seemed important enough to attract a great deal of philosophical and labour. Indeed, after years of failure, success would be even more glorious. Therefore, at those odds, we should expect that many philosophers would be willing to continue to work on demarcation and keep the problem alive. It would be premature to declare either that demarcation is dead, or that a solution will not be found, when there is still a 40% chance of success. Indeed, many philosophers would still hold out hope at 20%! So, to kill off all hope, Laudan needs to establish that failure is extremely probable, e.g. 90%. Only this kind of conviction could justify „pulling the plug‟ on demarcation.

Indeed, elsewhere in „Demise‟, Laudan writes as though he is almost certain that there is no epistemic invariant (Laudan, 1983: 124). How could he achieve this near-certainty?37 One way for Laudan to achieve certainty would be to show that science is maximally heterogeneous. He might make the following argument:

1*. Science is maximally epistemically heterogeneous. 2*. If science is maximally epistemically heterogeneous, then there is no epistemic invariant in science. 3*. There is no epistemic invariant in science.

If Laudan could show that science is maximally epistemically heterogeneous, then his conclusion would follow. At either extreme of heterogeneity, we can achieve certainty (as depicted in Figure 2.3). But in the more plausible middle ground, there is .

37 A very bad argument would be to reverse the conditional, so that the consequent becomes a certainty: „If there is no epistemic invariant in science, then science is epistemically heterogeneous.‟ Then the logical error of „affirming the consequent‟ would generate the desired conclusion. But in the passage I quoted, Laudan clearly isn‟t doing this.

55 Heterogeneity Homogeneity Epistemic Invariant

Maximum Minimum No

Minimum Maximum Yes

Moderate Moderate ?

Figure 2.3: The implications of heterogeneity for the invariant.

2.7.1 Objection: Rules don’t vary that much! Laudan‟s claim that science is epistemically heterogeneous is true, but almost trivially so. Scientists in different disciplines study different phenomena, so it should come as no surprise that their experimental techniques also differ. Scientists use different experimental techniques and equipment, and have different background knowledge to scientists who worked in the same discipline twenty years ago. Even scientists working side-by-side in the may conduct different experiments to test the same theory. Scientists often disagree about what constitutes good evidence, or sufficient data, or a significant result.

In fact, some divergence and disagreement, even at the methodological level, seems to be a good thing for science. One could argue that divergence leads to the production of various and diverse theories and approaches, and disagreement leads to the testing and replacement of old theories and approaches with new ones, so that science can improve over time. Some heterogeneity in science seems to be important to explain its long-term success. None of this should come as a surprise to philosophers of science.38

If science is maximally heterogeneous, then all variants should appear in science. For example, for every methodological rule that is followed by some group of scientists, we should see the opposite rule followed by some other group of scientists. Consider the rules and their opposites listed in Figure 2.4. 39

38 Indeed, Kuhn made this point about scientific assessments of theories (Kuhn, 1977: 112). If everyone thought the same theory was the most promising and worked on it, then no alternatives would receive any attention, which would be bad for science.

39 This is not by any means an exhaustive list: there are many other rules like these, and there is disagreement about the precise wording and relative importance of such rules. However, I take it that rules of this sort tend to be considered acceptable by scientists.

56 Prefer true theories. Prefer false theories.

Prefer theories for which there Prefer theories for which there is strong empirical support. is strong empirical opposition.

Prefer theories that are as Prefer theories that are as simple as possible. complicated as possible.

Prefer theories with a broad Prefer theories with a narrow scope of application. scope of application.

Prefer theories that make Prefer theories that make successful predictions. unsuccessful predictions.

Prefer theories that have Prefer theories that have passed rigorous empirical failed rigorous empirical tests. tests.

Prefer theories that give a low Prefer theories that give a probability to false claims. high probability to false claims.

Figure 2.4: Some accepted rules and their opposites.

The rules in the left-hand column are endorsed and followed by many scientists. The rules in the right-hand column are not endorsed or followed by any scientists (as far as I know!). In fact, such rules would surely be regarded as distinctly unscientific. The fact that there are some rules that are regarded as unscientific – even intuitively or implicitly – tells us that scientific methodology does not vary to an unlimited extent. Science is not maximally heterogeneous.

2.7.2 Objection: Ultimate goals might not vary One could argue that to sustain his argument, Laudan only needs to show that science is heterogeneous in crucial ways. We have already established that methods vary, along with techniques, experiments, evidence, facts and so on. Is this enough variance to support Laudan‟s conclusion?

Some philosophers find it surprising that such divergence and disagreement over techniques, experiments, evidence and even facts should eventually give way to convergence and agreement in these same areas. Yet, this is what happens repeatedly in science. As I demonstrated with my objection from limited heterogeneity, only certain methodological rules are considered „scientific‟. This suggests that science is somehow 57 constrained. If there were no overarching epistemological, methodological or axiological constraints, then we would see more variation in methodology among the numerous and various cases of science. Consider the two sets of rules listed in figure 2.5. The rules in the left-hand column are certainly diverse – yet they all seem to share common goals: such as adequacy in light of current empirical results and the advancement of useful empirical knowledge. This contrasts with the rules in the right-hand column. Following those rules would lead to inadequacy in light of current empirical results and degeneration of useful empirical knowledge. So the methodology we see in science appears to be constrained by general epistemic and empirical goals on which scientists agree. This suggests that in science, heterogeneity at the level of method gives way to homogeneity at the level of cognitive goals and values. I think we can reasonably conclude that such shared goals are probable (i.e. have a probability greater than 50%), and these goals are a crucial epistemic feature of science.

If my argument is plausible, then Laudan is very far away from establishing conclusively (with, say, a 90% probability) that no epistemic invariant exists.

2.7.3 Sub-conclusion: Deep epistemic homogeneity Laudan argues that the epistemic heterogeneity of science makes it highly improbable that there is an epistemic invariant in science. Scientific theories are different to each other in many ways, so Laudan that they are similar to each other in any way. I have argued that, although the methods of science vary, this variation is limited. It seems to be limited to methods that promote specific kinds of goals: the ultimate goals that appeal to all scientists. So there is probably a deep epistemic homogeneity, or invariant, in science.

2.8 Conclusion

To summarise, Laudan argues that the demarcation problem presupposes a false assumption. Therefore, it is a pseudo-problem and should be abandoned. I disagree.

Firstly, Laudan argues that the demarcation problem presupposes a particular kind of property: one that satisfies the four requirements of accuracy, precision, epistemic superiority, and invariance. He suggests that, with the exception of accuracy, these requirements must be satisfied perfectly or extremely. This means he has a very strict interpretation of an „epistemic invariant‟. I have argued that these requirements only need to be satisfied moderately. This leads me to a more moderate interpretation of an „epistemic invariant‟.

58 Secondly, Laudan argues that there is no suitable epistemic invariant in science, on the grounds that science is epistemically heterogeneous. I have argued that if science is maximally epistemically heterogeneous, then we would expect to see all possible variants displayed by science. I have shown that there are some possible methodological rules that are never displayed by science. This illustrates that, in fact, we only see limited epistemic heterogeneity in science, and cries out for some kind of . The methodological rules followed by scientists can all be plausibly explained by common goals and aims. This suggests that heterogeneity at the level of method gives way to homogeneity at the level of ultimate epistemic goals.

Laudan Walsh

Interpretation of ‘Epistemic Invariant’ Strict Moderate

Claim about Epistemic Heterogeneity Extreme Moderate

Figure 2.5: My alternative interpretation and claim.

The difference between our views is summarised in Figure 2.5. These differences lead us to different conclusions. To show exactly how, I now return to the formal version of Laudan‟s Pseudo-Problem Argument, to assess the truth value of each premise and the conclusion. Where do I say it goes wrong? My answer depends on how we interpret the premises, as well as my view of the facts about science. So in Figure 2.6, I set out the argument and give two truth values for each statement.

Interpretation of ‘epistemic invariant’: Laudan’s Walsh’s

P1. The demarcation problem presupposes that there is an epistemic invariant in science. F T

P2. The presupposition, that there is an epistemic invariant in science, is false. T ?

P3. If a problem makes a false presupposition, then it is a pseudo-problem. T T C. The demarcation problem is a pseudo-problem. ? ?

Figure 2.6: Two interpretations and my corresponding truth values.

In the left-hand column of truth values, I deal with the premises if they are interpreted as Laudan would have it. Demarcation doesn‟t presuppose a strict invariant, so P1 is false. There is no such strict invariant, so P2 is true. The false premise P1 leaves the truth value of C unclear.

59 In the right-hand column, I deal with the premises if they are interpreted as I would have it. P1 is now true, because working on the problem does presuppose at least a moderate invariant. But P2 is now unclear, because (despite my goals-and-rules argument) I leave open the possibility that no demarcation criterion will ever be completely satisfactory and generally accepted. The dubious premise P2 leaves the truth value of C unclear.

On either interpretation, I find Laudan‟s pseudo-problem argument unconvincing, because it doesn‟t establish that C is true.

60 3 The New Problem

Laudan argues that the problem of demarcation between science and non-science should be replaced with a new demarcation problem. I take this to be the problem of demarcating between well-confirmed and ill-confirmed theories. I argue that it should not replace the original problem.

3.1 Laudan’s New Problem

Laudan argues that we should replace the original demarcation problem with a new one. He is not entirely consistent in the terminology he uses to describe his new problem. He talks about „well-founded beliefs‟ and „reliable knowledge‟, as well as asking questions such as „When is a claim well-confirmed?‟ and „When can we regard a theory as well-tested?‟. However, the general idea is clear. According to Laudan, the legitimate purpose of the original demarcation problem was to choose which theories to believe, and this purpose was evident in the Old Demarcationist Tradition (Laudan, 1983: 122).40 He also says we should only believe things for which we have substantial evidence (Laudan, 1983: 125). Indeed, throughout his paper, Laudan makes it clear that he sees strong empirical support as the highest form of epistemic merit that a theory or claim can achieve.41 Thus, Laudan argues that confirmation is the key criterion by which we should evaluate a theory‟s epistemic merit. For this reason, I take his new demarcation problem to be a problem of demarcation between well-confirmed and ill-confirmed theories.42

40 I discussed the alleged transition from the Old Tradition to the New Tradition in section 1.5.

41 This is a view that Laudan continues to hold in later papers. For example, Laudan (1984) distinguishes between permissible and impermissible beliefs. He says “A belief is permissible precisely when, among the various alternatives to it under active consideration, none has a higher degree of empirical support than it does” (Laudan, 1984: 28-29). However, in earlier papers Laudan does not emphasise confirmation to such a high degree. For example, he says “In appraising the merits of theories, it is more important to ask whether they constitute adequate solutions to significant problems than it is to ask whether they are „true‟, „corroborated‟, „well-confirmed‟ or otherwise justifiable within the framework of contemporary epistemology” (Laudan, 1977: 14).

42 Laudan refers to various things that need to be confirmed in order to be credible: „beliefs‟, „knowledge‟, „claims‟, and „theories‟. We might add to this list „hypotheses‟, „statements‟, and so on. I take it that all these things are slightly different, but these differences are not important to Laudan‟s argument. So, like Laudan, I will vary these terms freely to fit my examples.

61 Laudan‟s argument can be formalised as follows:

P1. We only want to know which theories we should believe. (claim)

P2. We should believe all well-confirmed theories and we shouldn‟t believe any theories that are not well-confirmed. (claim)

P3. We only want to know which theories are well-confirmed. (from P1 and P2)

P4. Not all scientific theories are well-confirmed and some non-scientific theories are well-confirmed. (claim)

P5. Scientific status is completely irrelevant to well-confirmedness. (supported by P4)

P6. We don‟t want to know which theories are scientific. (from P3 and P5)

C. We only want to know which theories are well-confirmed; we don‟t want to know which theories are scientific. (from P3 and P6)

Laudan‟s argument is two-pronged: the arguments form two sub-conclusions, P3 and P6, before the main conclusion, C. I shall consider each of these sub-arguments separately. In Section 3.2, I will begin by considering some definitions of confirmation and whether confirmation alone could plausibly dictate our beliefs. In Section 3.3, I will argue that scientific status is relevant to confirmation (contrary to P5, and hence to P6). In Section 3.4, I will argue that scientific status also indicates other virtues that we want to know about, for other purposes besides belief (contrary to P1 and P3).

3.2 Accounts of Confirmation

3.2.1 Laudan’s views Confirmation is a relationship in which the available facts are supposed to offer some degree of support for a candidate theory.43 The term „confirmation‟ is commonly used in two distinct senses:

1. In an absolute sense, facts can confirm a theory if they provide conclusive support for the theory; and

43 The term „confirmation‟ is sometimes applied to the predictions of a theory. For example, physicists say that has been confirmed to 10-20(Coleman and Glashow, 1997). But this refers only to the discrepancy between the predicted measurement and the actual measurement. It does not measure the degree of support for any deeper theoretical claims. So this is a slightly different sense of „confirmation‟ to standard philosophical usage.

62 2. In an incremental sense, facts can confirm a theory if they provide some further degree of support for the theory.

If we talk about theories being confirmed in the first sense, then theories are either confirmed or unconfirmed. It makes no sense to say that one theory is well-confirmed while another is ill-confirmed – either it is confirmed, or it isn‟t. If we talk about theories being confirmed in the second sense, then one theory may be confirmed to a higher degree than another theory. Thus, we can talk (as Laudan does) about theories being well- confirmed and ill-confirmed. So, to formulate Laudan‟s new demarcation problem in a way that is consistent with his remarks, confirmation should be understood in an incremental sense.

Laudan thinks that all judgements of evidential support are comparative

(Psillos, 1999: 171 & 175). A given fact F confirms a theory T1 better than it confirms an alternative theory T2 when F gives a higher degree of support to T1 than to T2. So, I take it that confirmation should be understood in a comparative sense.

Laudan also notes that there are various possible positive relationships between a fact and a theory. A theory may (Laudan, 1990a: 329):

a) Be logically compatible with a fact;

b) Logically entail a fact;

c) Explain a fact; or

d) Be empirically supported by a fact.

He argues that these relationships are not reducible to each other. For example, a theory that satisfies (a) or (b) does not necessarily satisfy (c) or (d). This complicates any definition or assessment of confirmation.

3.2.2 A simple account One could give a „simple‟ account of confirmation which specifies a purely deductive relationship between an accepted fact F and a theory T. On such accounts, only Laudan‟s relationships (a) and (b) are relevant. There are three logical possibilities: (i) if T entails F, then F confirms T; (ii) if T entails ~F, then F disconfirms T; and (iii) if T does not entail either F or ~F, then F neither confirms nor disconfirms T. For example, the hypothesis „all crows are black‟ would be confirmed by a black crow, disconfirmed by a non-black crow, and neither confirmed nor disconfirmed by a black cat. A set of accepted facts can confirm T1 to a higher degree than T2 either because fewer of these facts are denied by T1

63 than T2, or because more of these facts are entailed by T1 than T2. At times, Popper seems to hold this simple view of confirmation (e.g. (Popper, 1963)): either a fact falsifies a theory, or it could have falsified it but now corroborates it, or could never have falsified the theory and was always irrelevant. Proponents of such a simple view need to weigh up the relative importance of confirming and disconfirming facts. Popper clearly regarded disconfirming facts as much more important than confirming ones.

3.2.3 Sophisticated accounts There are more sophisticated accounts of confirmation in which each individual fact can give a greater or lesser degree of support to a theory. This includes probabilistic theories of confirmation, such as those of Carnap (e.g. (Carnap, 1950)) and many Bayesians (e.g. (Dorling, 1979), (Earman, 1992), (Howson and Urbach, 1989)). All of Laudan‟s relationships (a), (b), (c) and (d) are relevant to these accounts.

For example, Bayesian confirmation theory tells us that facts confirm theories by increasing the probability that the theory is true, in accordance with Bayes‟ :

P(F|T).P(T) P(T|F) = P(F)

According to Bayesian confirmation theory, we begin with some prior probability P(T) for the theory T before the fact F is taken into account. Subsequently, some facts will support T more than others. The amount of support that a fact can give to a particular theory depends, firstly, on how likely the fact is if we assume that the theory is correct, i.e. P(F|T). If T entails F, then P(F|T) will be 1. If T entails ~F, then P(F|T) will be 0. If T is merely consistent with F, then P(F|T) will be more than 0 and less than 1. F will give more support to T if, say, P(F|T) = 0.9 than if P(F|T) = 0.5. Secondly, the amount of support that a fact can give to a particular theory depends on how likely the fact is if we don‟t assume the theory is true, i.e. P(F). This depends on all the alternative theories and our background knowledge (Chalmers, 1999: 175-176). If P(F) is very low, but P(F|T) is very high, then discovering F will significantly increase the probability of T. If P(F|T) is very high but P(F) is equally high, then the probability of T will not change. The probability of T could even decrease if P(F|T) is high but not as high as P(F).

The degree to which a fact supports a theory can be reformulated in a comparative way, consistent with Laudan‟s views. Bayes Theorem implies that:

P(T |F) P(F|T ).P(T ) 1 = 1 1 P(T2|F) P(F|T2).P(T2)

64

That is, the ratio of a posteriori probabilities is equal to the ratio of the likelihoods times the ratio of a priori probabilities. So the „likelihood ratio‟ measures the effect of evidence on the „prior ratio‟, i.e. it measures the comparative degree of confirmation of one theory over another provided by the evidence (Good, 1983: 38).

Bayesian confirmation theory can distinguish between smaller degrees of confirmation than a purely logical account. This increases the chance that, for any pair of competing theories T1 and T2, P(T1|F) will be different to P(T2|F) – i.e. one theory will end up more credible than the other.

3.2.4 Overcoming underdetermination For Laudan to sustain his argument, he must argue that confirmation is sufficient to dictate what we should believe. One objection to this argument arises from the alleged „underdetermination‟ of by facts. Roughly, the idea is that even the most well-confirmed theory must always have contraries which are equally well-confirmed but incompatible with it.44 If confirmation is our only criterion for choosing which theory to believe, then we can never have good reason to believe any one theory rather than one of these contraries.

One version of this objection is that theory choice is underdetermined by the observed facts. This leaves open the possibility that future discoveries will confirm one theory more than the others, and resolve any particular underdetermination dilemma. Alternatively, one might seek to rule out the contraries immediately on other grounds. For example, one might appeal to an epistemic virtue such as simplicity in order to show that one theory is better than the others. Thus, we can hope to make a rational decision to prefer one theory over another despite underdetermination. Another option is to accept the conclusion that we should never believe any one theory more than its contraries, and try to keep a perpetually open mind (although this seems bizarre, since we often come to believe particular theories).

A slightly different version of the objection from underdetermination is that there will be alternative theories that are empirically equivalent with respect to all possible observations. In that case, we could never hope to distinguish between them on the basis

44 There are various arguments for this claim. See, for example, „Chapter 3: The Duhem-Quine Thesis and Underdetermination‟ in Curd & Cover (1998).

65 of future discoveries. However, we could argue that such empirically equivalent theories are not contraries at all; rather, they are different formulations of the same theory.45

Yet these replies seem to concede too much to the objection from underdetermination. They accept the claim that theories are underdetermined by the facts, but it is not clear that we should concede this. Some philosophers (e.g. (Psillos, 1999) and (Laudan, 1990a)) have argued that a sophisticated account of confirmation can avoid underdetermination. They demonstrate that even if we happen to generate two rival theories that „fit‟ all and only the same facts, the theories may not be equally well- confirmed, because with a sophisticated account of confirmation the same facts can confirm each theory to a different degree. For example, on a Bayesian view the a posteriori probabilities of the theories can still be different. So a sophisticated account of confirmation could more easily avoid underdetermination, and could plausibly dictate beliefs, as Laudan‟s argument requires.

3.3 Objection: Science is Relevant to Confirmation

Laudan argues that we should only be interested in assessing the degree of confirmation of knowledge claims. He says “the „scientific‟ status of those claims is altogether irrelevant” (Laudan, 1983: 125). Here I argue that scientific status is, in fact, relevant to confirmation.

3.3.1 Science is more than confirmation As I noted in my Introduction, there are many aspects to science, including (but not limited to) : (a) basic elements such as theories, predictions, experiments, and results; (b) technical refinements such as mathematical models and formulae, measurement tools, and statistical analyses; (c) general virtues of theories such as confirmation, novelty, simplicity, explanatory breadth, and usefulness; and (d) social arrangements such as qualified experts, published journals, peer review, large institutions, and competition for funding. Laudan has commented at various times on many of these aspects of science (e.g. (Laudan, 1977), (Laudan, 1981a), (Laudan, 1984), (Laudan, 1990b)), so he would agree that science has other aspects besides assessing the degree of confirmation of theories. In Demise his claim is simply that all these other aspects are irrelevant to epistemic merit.

3.3.2 Types of relevance To evaluate Laudan‟s argument, I need to clarify what is meant here by relevance.

45 The theories would have identical Ramsey sentences, which on some accounts makes them identical theories (c.f. (Mellor, 1998)).

66 Firstly, relevance comes in degrees. In particular, we can distinguish between the following cases:

(i) Scientific status would have no relevance when it gives no indication at all of the degree of confirmation.

(ii) Scientific status would have some relevance when it gives some indication of the degree of confirmation. This could be weak, moderate, or strong relevance.

(iii) Scientific status would have exhaustive relevance when it completely determines the degree of confirmation, so that nothing else is relevant.

Secondly, there are at least three possible ways in which scientific status can be relevant to confirmation: a) Scientific status would be logically relevant to confirmation if their definitions were connected, e.g. one term appears in the definition of the other; b) Scientific status would be statistically relevant to confirmation (in some specified circumstances) if the occurrence of one made the occurrence of the other more or less likely; and c) Scientific status would be causally relevant to confirmation if scientific status had some causal influence on confirmation or vice versa, i.e. they were causally connected.

If Laudan could show that the notions of science and confirmation were completely irrelevant in all respects, then this would certainly be adequate for his argument. But partial relevance in some respects may be a problem. If there is a strong logical, statistical or causal connection between science and confirmation, then demarcating science could be useful for demarcating well-confirmed theories.

3.3.3 No exhaustive relevance Laudan claims that not all scientific theories are well-confirmed. Indeed, he says, “many, perhaps most, parts of science are highly speculative” (Laudan, 1983: 123).46 I agree. There are some parts of science, particularly new, competing hypotheses, that are not well-

46 He even employs his other pessimistic induction against the truth of scientific theories to support this claim. He asks, given that the historical record indicates that most scientific theories are false, “how plausible can be the claim that science is the repository of all and only reliable or well-confirmed theories?” (Laudan, 1983: 123).

67 confirmed.47 For example, it is not clear whether the universe will continue to expand forever, or whether it will eventually contract in a Big Crunch. Both possibilities are current and valid scientific hypotheses (Hinshaw, 2008), but we cannot regard both of them as well-confirmed! Conversely, Laudan claims that some well-confirmed theories are not scientific. I agree. There are some parts of non-science, particularly well-established specific hypotheses, that are well-confirmed. For example, in History, it is well-confirmed that Lincoln was assassinated by John Wilkes Booth; and in professional tennis strategy, it is well-confirmed that serving is an advantage.48

But Laudan‟s argument really only shows that scientific status is not exhaustively relevant to confirmation or vice versa, either logically, statistically or causally. If some scientific theories are not well-confirmed, and some well-confirmed theories are not scientific, it follows that: a) Logically, there must be more to the definition of confirmation than scientific status (and vice versa). b) Statistically, being scientific does not make it certain that a theory will be well- confirmed (or vice versa). c) Causally, being scientific does not always make a theory well-confirmed (or vice versa).

However, this conclusion is weak: it leaves open the possibility of non-exhaustive but strong relevance.

3.3.4 Causal relevance I claim that there is a strong causal connection between scientific status and confirmation. Philosophers of science do not agree on all aspects of the scientific process, but most philosophers who accept the idea of confirmation would agree with the following general remarks:

47 Derksen (1993) points out that their scientific status may just be due to the „happy mistake‟ that such theories have been generated from the scientific tradition. If such speculative and untested theories had been generated outside science, then Derksen tells us that they wouldn‟t be considered „scientific‟ so easily. Nonetheless, it is clear that when such theories have been generated within science, then they are regarded as scientific.

48 It could be argued that non- is only well-confirmed because it has undergone some kind of quasi-scientific process. However, this is stretching the definition of „scientific‟ in a controversial way. I don‟t need to take this line in order to defeat Laudan‟s argument.

68 . When scientific theories are first proposed, they are often highly speculative. However they are considered „scientific‟ before they become well-established or well-confirmed. By well-established I mean „well-established after considerable investigation‟.

. An important aspect of science is gathering evidence relevant to these speculative theories, e.g. through experiments that test their predictions.

. Not all speculative theories survive to become well-established scientific theories. Many are disconfirmed and/or not confirmed by the evidence, and are eventually rejected.

. Some theories survive to become well-established. By this stage they are generally well-confirmed, due to accumulated favourable evidence, e.g. favourable experimental results.

On this account, science is (among other things) a causal process that achieves confirmation. Indeed, it is probably our best available process for confirming novel theories. So being scientific is not irrelevant: it indicates that a theory is either undergoing the process of confirmation, or has survived that process and has probably become well- confirmed.

3.3.5 Statistical relevance Statistically, Laudan‟s argument that science is irrelevant to confirmation is very weak. This can be illustrated by considering other arguments with the same logical form. For example:

Some short people are successful at professional basketball, and some tall people are not successful at professional basketball. So being tall is altogether irrelevant to being successful at professional basketball.

Although the evidence cited shows that being tall is not exhaustively relevant to being successful at professional basketball (because there are other relevant factors), we know that in practice the two things are relevant to each other. A player is much more likely to be successful at professional basketball if he is tall. (Of course, this statistical fact has a causal explanation: being tall makes it easier for him to score points.) Therefore, if we are interested in knowing which players are more likely to be successful professional players, we should be interested in their height. A method of measuring their height would be very relevant!

Similarly, the causal process I described in section 3.3.4 leads to a statistical connection between scientific status and being well-confirmed. When we look specifically at well- 69 established theories, I think we can see that being scientific makes it more likely that a theory is also well-confirmed. Unfortunately, it is beyond my present resources to support this claim with a large randomised sample of well-established theories, and independent expert assessments of their scientific status and degrees of confirmation to prove that the two features are correlated. But perhaps I can support my claim with a thought , which appeals to our intuitions:

Theory A is a well-established scientific theory: firmly believed by scientists and taught as a fact in science classes. Theory B is a well-established pseudo-scientific theory: firmly believed by pseudo-scientists and taught as a fact in pseudo-scientific classes. Which theory is more likely to be well-confirmed?

I think most people would agree with me that theory A is much more likely to be the genuinely well-confirmed theory, although theory B may give the deceptive appearance of being well-confirmed. If this about the probability of confirmation is correct, then scientific status is strongly statistically relevant to confirmation (in the specific circumstances of examining well-established theories).

Where a theory is not well-established, I do not claim that being scientific increases the probability that it is well-confirmed. In outlining the causal process, I noted that many new scientific theories are speculative. By definition, these are not well-confirmed. Of course, speculative new pseudo-scientific hypotheses would not be well-confirmed either. However if theory A and theory B were both speculative new hypotheses, it is not clear that the scientific one would be any more likely to be well-confirmed.

I have argued that (for well-established theories), scientific theories are more likely to be well-confirmed than unscientific ones. A fortiori, I now argue that our most well- confirmed theories are more likely to be scientific than unscientific. For example, the biological theories that „organisms inherit many physical characteristics via the replication of DNA‟ and that „the function of the heart is to pump blood around the body‟ are among the most well-confirmed theories we have (e.g. (Alberts, et al., 2002: 235) and (Schultz, 2002)). There are many other examples of extremely well-confirmed theories in various scientific disciplines. In contrast, few non-scientific or pseudo-scientific theories are confirmed to an equally high degree. In cases where „folk theories‟ have been extremely

70 well confirmed, this is usually by gathering and converting them to scientific theories!49

3.3.6 Logical relevance It is clear that „scientific‟ and „well-confirmed‟ do not mean the same thing. Nevertheless, it might turn out that on some accounts the two terms are logically relevant. For example, some people might accept a definition of the form „Science is a process of confirming theories, in which...‟ Alternatively, some people might accept a definition of the form „Confirmation is a property achieved by the scientific process, when...‟ Even if the two terms are not as closely related as this, one term might appear somewhere in a detailed explication of the other. The main problem for assessing logical relevance is that we have no clear agreement on a definition of science (and perhaps not of confirmation either). So all we can say is that scientific status might be logically relevant to confirmation, or it might not. This does not decisively refute Laudan‟s claim that scientific status is logically irrelevant, but it does cast some doubt on his claim, and hence on his argument.

3.3.7 Sub-Conclusion: Scientific status is strongly relevant I have conceded that the scientific status of a theory has some logical, statistical and causal independence from its degree of confirmation. Nevertheless, I have argued that scientific status may be logically relevant (although this is hard to assess). What seems certain is that scientific status is strongly statistically and causally relevant to confirmation, contrary to Laudan‟s claim. If we are interested in identifying which theories are likely to be well- confirmed, then we should be interested in identifying which theories are scientific.

3.4 Objection: Science has Other Purposes and Other Virtues

Laudan argues that we should only be interested in finding out which theories are well- confirmed, and hence, belief-worthy (Laudan, 1983: 125). If so, then it seems that the only purpose of science is to seek well-confirmed theories, and confirmation is the only real virtue. I shall now argue that there are other well-recognized and legitimate purposes in science besides seeking well-confirmed theories, and other well-recognized and legitimate

49 For example, folk medicine says that specific herbs are valuable for particular ailments. Scientific investigation has shown that some of these claims are correct, and some of these claims are mistaken (e.g. (Morgan, 1999) and (Pirotta, et al., 2004)). The correct claims are now well-confirmed scientific claims, not just folk claims. It seems that none of these folk claims were actually well-confirmed before scientific investigation, but afterwards the correct ones became well-confirmed scientific claims.

71 virtues besides confirmation. These alternative purposes help to explain why these alternative virtues are genuinely valuable. We should be interested in identifying what is scientific, because this indicates the presence of such virtues.

3.4.1 Improving our understanding requires novelty Science is generally expected to improve our understanding of the world. Science has served this important purpose admirably in the past, so it is quite reasonable to expect it to continue to do so, and to regard this as a legitimate purpose of science.

To improve our understanding of the world, scientists cannot rest content with cautious or well-confirmed theories that tell us things we already know. They must continue to put forward new theories, even if (initially) they are speculative and not well- confirmed. This explains why we should value theories that are progressive, or fruitful, or make novel predictions. These things are often cited as virtues by both scientists and philosophers (e.g. (Lakatos, 1977), (Laudan, 1977), (Kuhn, 1977)), and yet they are clearly different to confirmation. In fact, there seems to be a tension between these values, since pursuing novelty will often lead to different choices than pursuing confirmation alone (e.g. (Kuhn, 1977)).

3.4.2 Improving our living standards requires usefulness Science is also expected to improve our living standards. Again, it has served this important purpose admirably in the past, and it is quite reasonable to regard this as a legitimate purpose. Science can achieve this through a better understanding of how to manipulate the world to achieve our ends, which includes the development of new and useful .

To improve our living standards, scientists must pursue novelty. But in particular, they should prefer theories with potential useful applications, and develop these applications. This explains why scientists are often interested in practical problems and developing technology, rather than merely confirming theories of abstract intellectual interest. It could also help to explain the attraction of simplicity and explanatory breadth in a pragmatic way. Simplicity helps us to understand and use a scientific theory, and explanatory breadth means that a theory can be used in a wider variety of situations. These virtues are clearly different from confirmation.

3.4.3 Achieving confirmation requires testability Even if we focus on the scientific purpose of confirming theories, there are other virtues that are useful for this process, which are not in themselves confirmatory. As I argued in 72 section 1.6, testability is a necessary condition for well-tested. As Popper observed, some pseudo-scientific theories are not testable, and for this reason they should not be classed as scientific. So even if confirmation is the ultimate goal and the primary epistemic virtue, testability must still be a significant indirect virtue (in my terminology of section 2.4.2).

Yet testability is not, in itself, confirmatory. It isn‟t an indication that the theory is well-confirmed, only that it might eventually become well-confirmed. It follows that scientific theories have a genuine virtue that is not identical to confirmation. So, when a new, speculative theory is scientific, we can expect it to have the virtue of being testable. We cannot be so confident that a new, speculative pseudo-scientific theory will have this virtue.

3.4.4 Pre-selection before confirmation The nature of the scientific process also suggests that other virtues must be employed besides confirmation. In section 3.2 I conceded that, at least in principle, a sophisticated account of confirmation could overcome the underdetermination objection to dictate which theories to believe, without resorting to any other epistemic virtues. In practice, however, evaluating theories solely on the basis of confirmation is inefficient.

As I have argued, many novel and promising theories are initially speculative. In fact, as the underdetermination objection suggested, scientists can potentially generate a huge number of speculative theories. Such theories can only be considered „well-confirmed‟ after rigorous testing. The problem is that there are too many speculative theories: more than we could ever possibly test! Confirmation cannot be a strong criterion for deciding which theories to test, since none of the options will be confirmed to any significant degree.

For the sake of efficiency, we need to use other values to pre-select theories for testing. Presumably, these are the familiar virtues of novelty, explanatory breadth, simplicity, etc. These other virtues do not require data or testing, so they can be used for pre-selection even if no relevant data is yet available. So, identifying a new speculative theory as „scientific‟ is useful because it is an indication that the theory has adequate virtues to be worth investigating – even before it is (or could be) well-confirmed. Fuller considers this process of pre-selection “a necessary (albeit fallible) condition for granting epistemic warrant” (Fuller, 1985: 331). However, he says, this point is lost on Laudan (1983), who only notices the diversity of the different cases of science – Laudan can‟t see the forest for the trees. 73 In support of my argument, some speculative theories do seem to be worth costly testing, while others do not even seem to be worth considering. For example, some physicists have proposed alternative theories of gravitation, in order to account for observed cosmological anomalies (e.g. (Bekenstein, 2004) and (Moffat, 1995)). I could propose my own theory: is exactly as the current theory dictates, except that smoked eels will weigh 50% less at the South Pole. Physicists are looking for ways to test their alternative theories, and yet I doubt that they would be interested in testing my proposal! I think they would be right to ignore it: all these proposals make novel predictions, but theirs have other significant virtues such as explanatory breadth and prior probability, whereas my proposal does not.

3.4.5 Other virtues can outweigh confirmation Other virtues are particularly useful for pre-selection, but they also seem to be significant later in the process. In some comparisons between theories, differences in other virtues seem to outweigh small differences in confirmation.

Suppose there is an alternative to Newton‟s theory of gravity, which is logically weaker. It has a narrower explanatory scope, because it is restricted to some things that we can easily observe, and excludes many things that are difficult to observe. It is also non- quantitative, because it only says that these objects are attracted to each other, and does not specify by how much. It would seem that this alternative must always be better confirmed than Newton‟s theory, no matter how much evidence we accumulate. Overall, the things it predicts will be better observed. Also, on a Bayesian , the posterior probability of the alternative must be at least as high as Newton‟s theory, because whenever Newton‟s theory is true then the alternative must be true (but not vice versa). Yet Newton‟s theory would still seem to be preferable, due to its broader explanatory scope and more specific quantitative predictions.50

3.4.6 Reply: Pursuit versus acceptance In earlier work, Laudan (1977: 111) identifies two kinds of theory preference: preferring a theory to pursue, and preferring a theory to accept. Accepting a theory requires us to believe the theory (i.e. to believe that the theory is true or empirically adequate). So, when we prefer to accept theory A instead of theory B (typically after extensive testing), then we are deciding to believe theory A instead of theory B. In contrast, pursuing a theory does not

50 For a more detailed discussion of this case, see Appendix C.

74 require us to believe the theory. So, when we prefer to pursue theory A instead of theory B (possibly after no testing at all), then we are not deciding to believe theory A, and may prefer theory A for reasons other than .

Laudan argues that it can be rational to pursue a theory even if one does not believe it. For example, it might be rational to pursue a theory if one thinks that, should the theory turn out to be true, then there would be an enormous pay-off. Moreover, it might still be rational to pursue a theory if one thinks it probably isn’t true but it is an interesting and important theory that one is best equipped to explore.51 In contrast, it is never rational to accept a theory that one does not believe. So the kind of preference we exercise in the pre- selection stage may not be the same kind of preference we exercise at the established- theory stage. Laudan would surely argue that the preferences I have discussed are for pursuit rather than for acceptance.

Laudan thinks we should only believe things for which we have “substantial evidence” (Laudan, 1983: 125) – this suggests that, when it comes to acceptance, empirical support overrides other virtues. Even if we decide to pursue theories with virtues such as novelty, simplicity, and potential usefulness, when we are dealing with established theories, we always prefer the better-confirmed ones.

3.4.7 Rejoinder: Demarcation is still useful for pursuit Laudan‟s argument that these other virtues are „pursuit‟ virtues rather than „belief‟ virtues, if correct, would be a strong argument that features such as testability and novelty are not primary epistemic virtues – they only have secondary epistemic value. This offers some support for P2 in Laudan‟s main argument: that belief should depend only on confirmation.

But the distinction between pursuit and belief does not provide a strong argument against the project of demarcation between science and non-science. It does not support P1 in Laudan‟s main argument: that we are only interested in belief. I have argued that there are other purposes in science besides providing well-confirmed theories, and these other virtues help to achieve these other purposes. So demarcation can still be useful for identifying theories with the pursuit virtues, which are the theories we should prefer to pursue. In fact, the „pursuit without belief‟ reply begs the question, since my argument in section 3.4 is an attack on P1 rather than on P2.

51 For Laudan (1977) the decision to pursue a theory is closely related to the rate of progress in problem- solving that the theory seems to offer.

75 3.4.8 Rejoinder: Other virtues may affect acceptance Laudan would argue that these other virtues are only „pursuit‟ virtues, but it is not clear that this is correct. I will not debate this point in detail, but I will list three possible arguments against it.

Firstly, in some cases it seems reasonable to prefer theories that display virtues other than well-confirmedness, even after extensive testing – which suggests that these virtues are affecting acceptance. For example, even after extensive testing we would still prefer Newton‟s theory of gravity to the logically weaker version mentioned earlier. The alternative would still be better confirmed, but Newton‟s theory would still have its other virtues, which seem to outweigh the difference in confirmation.

Secondly, Psillos argues that some epistemic virtues might be said to “capture the of a theory” (Psillos, 1999: 171). He then draws on a combination of arguments made by Boyd and Salmon to argue that explanatory power is potentially confirmatory because it raises the prior probability of a theory (Psillos, 1999: 171-172). If so, these epistemic virtues would affect acceptance.

Thirdly, in section 1.4 I suggested that it might be possible to demonstrate that some epistemic virtues are fallibly related to the truth (or empirical accuracy with respect to the available data) of theories. If we could establish this connection, then these virtues would be valuable as „truth indicators‟ and affect acceptance. However, it is difficult to establish whether other virtues (e.g. simplicity) are truth indicators, and it raises issues that are beyond the scope of this thesis.

If any of these arguments could be sustained, showing that other virtues are relevant to acceptance, then Laudan cannot dismiss the other virtues as merely for „pursuit‟. They are valuable properties that scientific theories tend to have, which also make them worthy of belief. This would cast some doubt on P2: the premise that belief should be governed only by confirmation.

3.4.9 Reply: Science is all about confirmation In response to my argument that being scientific signifies other virtues besides confirmation, Laudan might revise his position. He could make almost the opposite reply. Instead of dismissing other virtues as not important at all, or at least not important to acceptance, he could concede that they are important, but only for acceptance. The idea would be that the other virtues are important precisely because they lead to confirmation. But once we have achieved and assessed the degree of confirmation, these virtues would

76 have no further or additional importance. This makes the other scientific virtues relevant for achieving and assessing confirmation, but irrelevant for belief once confirmation has been assessed. This is a revision to Laudan‟s blunt claim in Demise that scientific status is “altogether irrelevant” (Laudan, 1983: 125).

What Laudan would dismiss is my claim that other virtues are important for other purposes besides confirmation. The purposes of improving understanding and living standards might be explained away as reasons for wanting specific kinds of confirmed theories (i.e. just variations on wanting confirmation). Laudan could try to explain every feature of science by making some kind of connection to confirmation, whether this is logical, statistical or causal. On this view, science is actually „all about‟ confirmation. In this way, Laudan could try to defend P1: the premise that we are only interested in confirmation.

This revised view might also allow Laudan to defend P2, the premise that belief should be determined by confirmation. It concedes that other virtues could play a limited role in acceptance, but only in support of confirmation.

3.4.10 Rejoinder: Then science is relevant to confirmation! I concede that this revised position, if defensible, might save P2 and rebut my „other virtues‟ argument against P1 in section 3.4. However, this revised position doesn‟t save P1, because it concedes the main point I argued for in section 3.3! If science is „all about‟ confirmation, then it is relevant to confirmation. Understanding what makes an area of knowledge a science should help us to understand what makes something well-confirmed. Identifying things as scientific would help to „pick out‟ things with features that are relevant to assessing confirmation. Thus, demarcating science could help to demarcate confirmation.

3.4.11 Sub-conclusion: Being ‘scientific’ indicates other virtues Laudan (1983) appears to think that the only legitimate purpose of science is to seek well- confirmed theories. I have argued that there are other important purposes in science, such as providing theories that increase our understanding of the world and improve our living standards. Hence, there are other important virtues in science besides confirmation, such as novelty and usefulness. These virtues are particularly useful for deciding which theories to pursue, rather than wasting time and money investigating all possible theories. So being „scientific‟ indicates an appropriate mixture of other virtues, not just being well-confirmed. It is a more general indicator of epistemic merit, similar to the useful summary assessment that a theory is „epistemically good‟ (although a bit more specific).

77 It is possible to argue that these other virtues of section 3.4 are only valuable because they lead to confirmation. But this concedes that my conclusion of section 3.3 is correct: being scientific is relevant to confirmation. So it doesn‟t really matter to my argument in Chapter 3 whether these other virtues are valuable because of their relationship to confirmation, or valuable for other reasons. Either way, demarcating what is scientific seems to be a valuable project.

3.5 Conclusion

I will now summarise exactly what is wrong (on my analysis) with Laudan‟s formalised argument of section 3.1.

In section 3.4, I disputed P1. I argued that there are other important purposes for science besides telling us what theories to believe. I also disagreed with P3. I argued that there are other important and well-recognized values in science besides confirmation, such as novelty, simplicity, explanatory breadth, and usefulness. These other values serve the other purposes, especially when deciding which theories to pursue. They also seem to play a role in deciding which theories to accept, which may conflict with P2.

In section 3.3, I did not challenge P4, but I demonstrated that it provides very little support for P5. I then disagreed with P5. I argued that (1) science might be logically relevant to confirmation; (2) science is strongly statistically relevant to confirmation; and (3) science is strongly causally relevant to confirmation.

Hence, I disagreed with P6 and C for two reasons. Contrary to P5, science is relevant to confirmation, so demarcating science could help us solve Laudan‟s new demarcation problem. Contrary to P3, being scientific indicates an appropriate mixture of other important epistemic virtues, so demarcating science could help us to identify things with such virtues.

I have not argued that we never want to know which theories are well-confirmed. It is important to answer the questions Laudan asks, such as: „When is a claim well-confirmed?‟, „When can we regard a theory as well-tested?‟ and „What makes a belief well-founded?‟. But to answer these questions properly, we need an adequate account of science – which is also useful for other reasons. Thus Laudan‟s new demarcation problem is worth solving, but we shouldn‟t reject the original demarcation problem.

78 4 Conclusion

Laudan argues that the original demarcation problem cannot be solved. He gives three main sceptical arguments against it:

A Pessimistic Induction – No past solution has succeeded, therefore future success is unlikely.

A Pseudo-Problem – The demarcation problem presupposes an epistemic invariant in science. This assumption is false.

A New Problem – We can (and should) evaluate confirmation without considering scientific status.

I replied to each of these three arguments in turn, asking „Has Laudan killed the demarcation problem?‟

Against Laudan‟s pessimistic induction, I demonstrated that the failure of many past attempts at demarcation has been partial, and the theory of demarcation continues to make cumulative progress. Therefore, I think we can take a more optimistic view.

Against Laudan‟s pseudo-problem argument, I demonstrated that the demarcation problem does not presuppose an extremely simple epistemic invariant. I also argued that a satisfactory, moderately complex epistemic invariant may exist. Therefore, I do not think any false assumption is presupposed.

Against Laudan‟s new demarcation problem, I argued that science is relevant to confirmation, so solving the original problem is relevant to solving the new problem. I also argued that there are other valuable aspects to science that are not identified by the new problem. Therefore, I do not think the new problem is a suitable replacement.

Some readers may be disappointed that my aim has been so modest: to defend the search for a solution, rather than to propose a brand-new solution to a very old problem. I am not convinced that a brand-new solution is required. I have identified several recent developments and current areas in the philosophy of science (for example, Bayesianism and Experimentalism) that I regard as particularly promising. I suggest that we look to the current research of other philosophers for improvements to the demarcation between science and non-science.

To return to my central question, and with apologies to Mark Twain: Laudan‟s report of the death of the demarcation problem is greatly exaggerated.

79 Appendix A Some Non-Ideal Definitions

A.1 Ideal Definitions

According to Swartz (1997), the classical theory of concepts emerges from the philosophical tradition of looking to geometry and mathematics for cases of reasoning and knowledge. Many philosophers considered these to be particularly clear and precise cases from which to develop rules for use in non-geometrical or non–mathematical arenas. Geometrical concepts such as square and triangle can be defined by a set of conditions that are individually necessary and jointly sufficient (e.g. „closed figure, has four straight sides, sides are all equal in length, interior angles each measure 90o, in a plane‟ for square; and „closed figure, has three straight sides, lies in a plane‟ for triangle). So, says Swartz, philosophers expected that ordinary concepts should also be defined in this way. But in recent times, philosophers have argued that geometrical concepts should not be considered exemplary or paradigmatic cases; rather, they should be considered special or exceptional cases. Most ordinary concepts are not as simple and precise as geometrical ones.

Traditionally, definitions have been judged by how well they satisfy the following ideal requirements:52

(i) Accuracy: A definition should be neither too broad nor too narrow. It should include all the cases that are instances of the concept, and exclude all cases that are not instances of the concept. For example, „unmarried adult male philosopher‟ is too narrow a definition for bachelor; but „unmarried adult‟ is too broad.

(ii) Precision: A definition should be neither too vague nor too precise. The definition should match the degree of precision of the concept being defined. For example, „adult female‟ for woman satisfies this requirement, but „female at least 18 years old‟ does not.

(iii) Non-circularity: A definition should not be circular. For example, if „desirable‟ defines good and „good‟ defines desirable, then these definitions are circular.

(iv) Conjunctive: A definition should consist of a set of criteria that are individually necessary and jointly sufficient. For example, „adult‟, „unmarried‟ and „male‟ are jointly sufficient criteria for bachelor. But none of these criteria is sufficient on its own; all three must be

52 This list of requirements is compiled from (Yagisawa, 1999) and (Swartz, 1997).

80 satisfied in order to call someone bachelor. So the logical structure of the definition is a conjunction of properties.

(v) Essentials: A definition should specify only genuinely essential properties of the concept, not accidental properties. For example, defining vertebrates as „things with both vertebrae and a liver‟ violates this requirement, for although all actual vertebrates have a liver, it is logically possible for a vertebrate to lack a liver. Vertebrae are an essential property of vertebrates; a liver is merely an accidental property.

These requirements stand together in an uneasy alliance as combined conditions for an ideal definition. Individually, they are often achieved; jointly, they are usually unachievable – one requirement usually infringes on another requirement.

A.2 Non-Ideal Definitions

There are many examples of non-ideal definitions that do not satisfy one of the criteria (i)- (v).

I have already discussed family resemblance definitions for ordinary terms such as game. On this view, an activity qualifies as a game iff it is similar enough to other accepted games. Presumably, it must share enough of the same properties (to a sufficient extent). But none of these properties need to be necessary ones: an activity could lack any particular property but possess all the others, and be judged similar enough to qualify as a game. A definition like this is not a conjunction of individually necessary and jointly sufficient conditions, i.e. it does not satisfy requirement (iv).

A definition that specifies essential properties of a concept may turn out to be circular. This depends on how we interpret the notion of essential properties. On the one hand, one might take essential properties to be unique, individual properties. For example, one might argue that the essential property of apple is „appleness‟. However, this is open to the charge of circularity, for appleness must then be defined as „the property displayed by all and only apples‟. Therefore, satisfying requirement (v) may violate requirement (iii). On the other hand, one might take essential properties to be more general properties. For example, one might argue that the essential properties of apple are „rounded, firm, juicy, edible fruit‟. This definition appears to avoid circularity, but it is too broad to define apple. „Rounded, firm, juicy, edible fruit‟ is also satisfied by orange, nectarine, and grape. So, trying to satisfy requirements (iii) and (v) may cause one to violate requirement (i).

Moreover, to give a definition for a particular purpose, one may need to violate one or more requirements. For example, sometimes the purpose of the definition is to clarify the

81 concept in order to reduce its vagueness. In such cases, (ii) is superseded by a new requirement (ii*):

(ii*) The definition should be more precise than the concept being defined.

Satisfying this new requirement may come at the expense of violating (i): increasing precision may lead to a broader or narrower definition of the concept.

Sometimes the purpose of the definition is to explicate the concept, that is, to provide an analysis of the meaning of the concept. Often an explication brings to the surface a new meaning – or the „real‟ meaning, or a lesser-known meaning – of the term. In such cases, requirements (i), (ii) and (v) may be violated: explication may result in a slightly different set of instances, more or less precision, and it may identify something that was not originally an essential property.

Sometimes a definition is ostensive rather than verbal. A definition is ostensive when a concept is defined by showing in some way, e.g. by example, , or by pointing (e.g. saying “red is that colour” while pointing to something that is red). A definition is verbal when a concept is defined by an explicit description of the relevant properties. It isn‟t clear that any of the requirements (i) to (v) can be satisfied by an ostensive definition.

In general, many ordinary definitions can‟t meet all of these ideal criteria, so maybe it is unrealistic to expect the demarcation of science to meet all of Laudan‟s stringent requirements (several of which are the same).

A.3 Gold

Often the same concept can be defined in several different ways – sometimes satisfying the requirements, sometimes violating them. For example, consider the following definitions of gold:

„Element with atomic number 79‟: This definition satisfies all of the requirements, and yet it may be considered unsatisfactory for some purposes. To scientists, „element with atomic number 79‟ is very informative: it tells them about the atomic structure of gold, which in turn tells them about the properties of gold, and hence a lot about the concept gold. For many people, however, this definition does not seem to be very different to the definition „element identified by the symbol Au‟; atomic mean little to many non- scientists. So other definitions are often employed.

„Soft, bright, yellow transition metal‟: This definition violates requirement (ii) because it is less precise than the concept, and requirement (iv) because the specified conditions are individually necessary but not jointly sufficient, and yet it is satisfactory for some purposes. 82 To the non-scientist, this definition says much more about gold than the former definition. It characterises gold in terms of general properties, and guides the application of the term „gold‟ in everyday situations.

„Alloy with at least 9 karats of pure gold‟: This definition violates most of the requirements, and even seems to violate requirement (iii), by defining gold using „gold‟ – and yet this definition is satisfactory for some purposes.53 In particular, this definition stipulates a standard for what should be called „gold‟. Pure gold is too soft to use for jewellery, so it is combined with other metals to make it harder. The resulting alloy is referred to as „gold‟, and displays a karat54 marking – this tells the buyer the purity of the gold. Authorities in many countries only allow alloys above a certain level of purity to be marketed as „gold‟. In Australia, the lower limit is 9 karats; in the USA, it‟s 10 karats. This level is arbitrary, but once set it provides a necessary condition for calling something „gold‟. This definition of gold doesn‟t tell us very much about the nature of gold; but it precisely demarcates things that can be marketed as „gold‟ from things that cannot.55

The gold example shows that it can be appropriate to define ordinary concepts differently for different purposes, even if this results in violating some of the ideal criteria. This principle can clearly be extended to definitions of science. So it is plausible that a demarcation criterion could be sufficient for philosophical and/or practical purposes, adequately motivating the philosophical search for it, even though it does not meet one or more of Laudan‟s stringent requirements.

A.4 Science

Science has also been defined in different ways, depending on the purpose of the definition. Consider the following definitions of various types:

53 One could argue that circularity is avoided by defining pure gold in some other way. Even so, the karat definition of gold is distinct from the others, and serves a specific practical purpose better than the atomic number definition.

54 A „karat‟ is a measure of the purity of gold, where pure gold is 24 karats.

55 The concept gold may also be explicated in terms of its use as a figure of speech. For example, when we say „good as gold‟ we are not concerned with the element and its atomic structure, we are talking about a shiny metal which symbolises and . When we talk about the proverbial „pot of gold‟ at the end of the rainbow, we are not concerned with its karat marking – we are talking about something more elusive. When we say „that‟s gold‟ in response to a statement, idea or plan, we are not necessarily being ostensive, we may be saying that we like the plan.

83 a) A lexical definition: 56 „Systematic and formulated knowledge‟ (Sykes, 1989: 939). b) A definition by genus and species: 57 „Empirical knowledge‟ (LaDuke, 2008). c) An ostensive definition: „Science is that kind of activity‟ – where „that‟ identifies, say, , a chemistry experiment, or a copy of the Journal of Applied Physics. d) A persuasive definition: 58 „The department that gets all the funding‟ (Gieryn, 1983); or „our most reliable source of knowledge‟ (Hansson, 2008). e) A recursive definition:59 „Physics is a science; any activity that uses similar methods, accepts similar theories or publishes in similar journals to Physics is also a science; nothing else is a science.‟

I take it that none of these definitions, on closer examination, constitutes a demarcation criterion that would satisfy most philosophers. They are very far from ideal. Nonetheless, they are not completely without merit, and they may be helpful for their intended purposes. Similarly, although solving the demarcation problem requires something better, it may well be something imperfect, and it might be a criterion tailored to a specific purpose (e.g. to theoretical insight rather than to practical applications).

A.5 Diamonds

The demarcation of diamond provides a very clear illustration of the difference between demarcations for theoretical and practical purposes.

There is a well-established distinction between diamonds and imitations: diamond is pure carbon crystallised in octahedrons; imitations are not. Therefore, one could argue that the criterion „pure carbon crystallised in octahedrons‟ is sufficient to demarcate diamond. If the purpose of the demarcation is merely to say what the difference between diamonds and

56 This is a definition designed to specify the conventional meaning of a term. This is the kind of definition normally found in a dictionary.

57 This is a definition that (i) specifies a type to which all the entities must belong (the „genus‟), and then (ii) specifies a subtype to which only the relevant entities belong (the „species‟).

58 This is a definition designed to affect or appeal to the psychological states of the party to whom the definition is given, so that a claim will appear more plausible to the party than it should.

59 This is a definition in three clauses, in which (1) the expression being defined is said to apply to certain particular items (the base clause); (2) a rule is given for reaching further items to which the expression applies (the recursive, or inductive clause); and (3) it is stated that the expression applies to nothing else (the closure clause).

84 non-diamonds is, then this criterion may be adequate. However a demarcation may have another purpose, and this criterion may not be useful. Consider, for example, a jewellery valuer trying to decide whether a particular gem stone is a genuine diamond or an imitation. There are (at least) two very good imitations on the market: cubic zirconia, made from zirconium oxide; and moissanite, made from silicon carbide. Unfortunately, identifying gem stones on the basis of their chemical structure is costly and invasive – it is not something for which the valuer has the equipment or expertise. Chemical structure might be essential to the concept diamond, yet it has no practical application for the valuer. I shall call this kind of property a „theoretical essential‟.

The valuer must consider properties that are observable to the trained person, such as colour, weight, impurities, hardness, sparkle, reflection and refraction. Good imitations, like cubic zirconia and moissanite, display many of the properties of diamond, so the valuer‟s identifications may be performed in a piecemeal fashion. For example, if she examines the back of the stone with a magnifying glass, and sees two sets of facet lines, then she will know that she is seeing a strong double refraction, and can conclude that she is looking at moissanite, not diamond. However if she sees a single set of facet lines, then she is either looking at diamond or cubic zirconia – she doesn‟t know which. So she must make another . For example, she can see how much light leaks out the back of the stone. If there is a lot of leakage, then the stone is cubic zirconia; if there is very little leakage, then the stone is diamond. This observation, though very reliable, can only be made if the stone is cut in a certain way, so it cannot always be used. Alternatively, the valuer may examine the stone for impurities. If she sees impurities, then she will know that the stone is diamond – albeit, a poor quality diamond. But if she can‟t see any impurities, the stone might be an extremely high quality diamond, or it might be cubic zirconia. She will continue making observations – she has an extensive selection of tests of this sort to choose from – until she is able to tell whether or not the stone is a diamond. I shall call the properties identified by these sorts of tests „practical indicators‟.

The theoretical essential – the chemical structure of diamond – is present in every case of diamond, but it cannot be identified in any case. The practical indicators – the valuer‟s „mixed bag‟ of tests – are not individually present in every case of diamond, but some revealing combination of these features can be identified in every case. The demarcation of science may turn out to be similar. So even if philosophers agree that they have solved the demarcation problem to their satisfaction, this may not satisfy everyone else!

85 Appendix B Some Complex Demarcations

There are several demarcations of science in the recent philosophical literature that are moderately complex, but none of them have been criticised for their complexity.

B.1 Thagard

Thagard (1988) thinks that the various scientific disciplines, fields, programs and theories display a „family resemblance‟. He specifies a handful of properties that are typical of science, and which can be contrasted with another handful of properties that are typical of pseudo-science. Thus, he specifies a „conceptual profile‟ for science and a contrasting conceptual profile for pseudo-science (Thagard, 1988: 170), as shown in Figure B.1.

Science

Uses correlation thinking Uses resemblance thinking

Seeks empirical confirmations and Neglects empirical matters disconfirmations

Practitioners care about evaluating Practitioners oblivious to alternative theories in relation to alternative theories theories

Uses highly consilient and simple Nonsimple theories: many ad hoc theories hypotheses

Progresses over time: develops new Stagnant in doctrine and applications. theories that explain new facts

Figure B.1 Thagard’s two conceptual profiles.

Thagard expects that instances of science will display more features from the profile of science than from the contrasting profile of pseudo-science. Very few (if any) of the sciences will display all of the characteristic features of science, and many of them will display one or two features of pseudo-science. Any instance of science will resemble other instances of science, though they may not have identical profiles. Any instance of science will also resemble some instances of pseudo-science in some ways.

Thagard specifies only a handful of properties of science, but (as I explained in section 2.5.4) I consider this a relatively complex demarcation, for three reasons. Firstly, he uses more features than, say, Inductivism or Falsificationism. Secondly, some of these properties seem quite complex in themselves. For example, it might take a lengthy description to spell out in simple terms what counts as confirmation and disconfirmation, or what counts as and simplicity. Thirdly, the logical function required to

86 capture „family resemblance‟ is considerably more complex than, say, a conjunctive definition. A conjunctive definition just says that all its properties must be present for something to count as a science. But a family resemblance definition needs to capture how many properties must be present and in what proportions. Thagard thinks this complexity is unavoidable – science is complex, so the demarcation criterion must be complex too.

Thagard‟s conceptual profiles have been criticised by many and accepted by few. What is relevant for my purposes is not the quality of his solution, but the kinds of criticisms that have been made. His solution has been criticised because it is not accurate with respect to the clear cases of science and clear cases of pseudo-science. For example, Derksen (1993) argues that Freud never confused resemblance with cause, he paid attention to empirical matters, and his theory was not stagnant in its doctrine and applications. – widely regarded as one of the clearest cases of pseudo-science – fails to fit Thagard‟s profile of a pseudo-science (Derksen, 1993: 18). Thagard‟s solution has not been criticised because of its complex structure.

B.2 Lugg

Lugg (1987) argues that we should distinguish between science and pseudo-science in much the same way that we distinguish between valid and invalid arguments. There is no single way that an argument can be invalid; rather, an argument can commit a number of that render it invalid. This does not lead us to conclude that there is no real distinction between valid and invalid arguments. Nor do we conclude that the only distinction is between true and false conclusions – an invalid argument might happen to have a true conclusion, and a valid argument might happen to have a false conclusion. Similarly, Lugg argues that pseudo-sciences can be structurally flawed in many different ways; but we shouldn‟t conclude that there is no genuine, useful distinction between science and pseudo-science. We have a number of clear cases of pseudo-science and analysis of these will provide us with a basis for identifying new cases of pseudo-science. Some new cases will contain flaws that we have identified in the clear cases, which will allow us to identify these new cases as pseudo-science. However, it is possible to identify a new kind of flaw, so the list provided by the clear cases of pseudo-science is revisable.

Lugg‟s demarcation criterion could become extremely complicated: he might specify as many kinds of flaws as there are cases of pseudo-science. Some cases are flawed before they are subjected to empirical testing; others are flawed in the way they deal with failures or refutations. For example, he says:

87 1. Action theory “merely repeats what everyone knows in a misleading and confusing way”;

2. Psychoanalysis incorporates a set of auxiliary hypotheses that shield it from refutation;

3. involves the “suspect strategy” of shuffling forward new cases when the old ones are shown to be problematic; and

4. fails to conform to the canons of good experimentation and sound statistical analysis.

Once again, this demarcation criterion has not been accepted by the majority of philosophers. It has been criticised for its circularity and scientific prejudice. For example, Derksen (1993) criticises Lugg for taking a „good structure‟ to be that which is displayed by clear cases of science and „bad structure‟ to be that which is displayed by clear cases of pseudo-science. This distinction, he says, amounts to the presupposition that science is good and pseudo-science is bad, because Lugg doesn‟t justify it on any other grounds. But the point I wish to stress is that Lugg‟s solution has not been criticised for its complexity.

B.3 Derksen

Derksen himself uses an analysis of pseudo-science to suggest an alternative solution (Derksen, 1993). He argues that the key to distinguishing between science and pseudo- science is to realise that pseudo-scientists are pretending or trying to be scientists. Hence, there will be a considerable amount of resemblance between science and pseudo-science. He says that scientists are committed to reliable knowledge and recognise human fallibility, so the pseudo-scientist must show commitment to these things as well. But by identifying the characteristic pretensions of the pseudo-scientist, we can distinguish between science and pseudo-science.

Derksen identifies “the seven sins of pseudo-science”. These are:

1. The dearth of decent evidence;

2. Unfounded immunisations;

3. The ur-temptation of spectacular coincidences;

4. The method;

5. The insight of the initiate;

6. The all-explaining theory; and

88 7. Uncritical and excessive pretension.

He says that these are all characteristic failings of the pseudo-scientist. But (like Thagard) he says they are not individually sufficient conditions for pseudo-science. This is because scientists can make mistakes, and we shouldn‟t dismiss them as pseudo-scientists on the basis of one or two sins.60 Nor are the seven sins jointly necessary: a pseudo-science may not display all of them. But Derksen does argue that the seven sins are jointly sufficient for dismissing something as pseudo-science.

Most of the criticism of Derksen has come from Lugg (1995), who has returned fire by arguing that Derksen has failed to avoid his own criticism of Lugg‟s (1987) view. Derksen‟s „sins‟, he says, really amount to „structural flaws‟, because while Derksen says he is talking about pretensions, he is really talking about practice. Derksen‟s sins may be more refined than Lugg‟s own „structural flaws‟, but they are still open to the same charge of scientific prejudice that Derksen levelled at Lugg. However, while Lugg criticises Derksen for his inconsistency, Lugg doesn‟t actually see this charge of scientific prejudice as warranted. He thinks that the standards of good scientific practice are situated in some sort of epistemological twilight zone. We can challenge these standards. But in the absence of convincing justification or refutation, they ought to be followed nonetheless – anyone who doesn‟t follow them “is asking for trouble” (Lugg, 1995: 324). Again, the point I wish to emphasise is that Derksen‟s seven-sin solution is just as complex as those of Lugg and Thagard – yet no one has criticised it for its complexity.

60 He even says “Put paradoxically, the sins of pseudo-science lose their sinfulness within science” (Derksen, 1993: 38).

89 Appendix C Other Virtues versus Confirmation

Features such as novelty, simplicity, explanatory breadth and usefulness are often regarded as virtues of a scientific theory. But Laudan maintains that when we are deciding whether to believe a scientific theory, we should only consider the virtue of confirmation. He concedes that other virtues can be important for deciding which theories to pursue, but not for deciding which theories to accept.

I think that the following hypothetical example illustrates that other virtues are important. They are certainly important when deciding which theories to pursue. They seem to be important enough that a difference in these virtues can outweigh a small difference in confirmation. In fact, they seem to retain this importance even after a lot of data has been accumulated. This suggests that they might even be significant for deciding which theories to accept.

C.1 Wonten versus Newton

Suppose two chaps, Newton and Wonten, are sitting under a tree when two apples fall (knocking them on their heads). As they sit there (rubbing the bumps on their heads) they each generate a , as described in Figure C.1.

Wonten’s Law of Falling Apples Newton’s Law of Universal Gravitation When apples become detached from trees, they Every point attracts every other point mass fall down. by a force pointing along the line intersecting both points. The force is proportional to the product of the two and inversely proportional to the square of the distance between the point masses according to the formula: m m F = G 1 2 r2

Where, F is the magnitude of the gravitational force between two point masses; G is the gravitational constant;

m1 is the mass of the first point mass;

m2 is the mass of the second point mass; and r is the distance between the two point masses.

Figure C.1 Two Laws describing the behaviour of apples.

Even after extensive testing, it seems that Wonten‟s Law must be better-confirmed than Newton‟s Law, for two reasons. Firstly, it is easier to make the observations that are relevant to Wonten‟s Law. To confirm Wonten‟s Law, one only needs to observe apples.

90 To confirm Newton‟s Law to the same extent, one needs to observe apples, atoms, planets and countless other things – many of which are (arguably) unobservable! Secondly, Wonten‟s Law is entailed by Newton‟s Law.61 So Wonten‟s Law must be at least as probable as Newton‟s Law62, and according to Bayesian confirmation theory that means it is at least as credible.

So, if we always prefer well-confirmedness to the exclusion of all other epistemic virtues, then we should prefer Wonten‟s Law. Yet we don‟t – we prefer Newton‟s Law. Newton‟s Law has epistemic virtues that Wonten‟s Law doesn‟t have, which seem to make it preferable to Wonten‟s Law. For example, it gives a broader explanation than Wonten‟s theory,63 it makes some interesting novel predictions,64 and it is quantitative rather than simply qualitative.65 If we accept Newton‟s Law for these reasons even after extensive testing, then in Laudan‟s terminology, it looks like these virtues are guiding acceptance rather than just pursuit.

This hypothetical example supports an alternative view of the other virtues. Well- confirmedness is one of a number of virtues that indicate epistemic merit. When faced with a choice between two rival theories, we don‟t always prefer the better-confirmed theory. We sometimes prefer the theory which displays other virtues, such as novelty, simplicity, explanatory breadth, or usefulness. If we focus on well-confirmedness to the exclusion of all other epistemic virtues, then we will miss many other indicators of epistemic merit. , scientists prefer well-confirmed theories. But usually ceteris

61 Newton‟s Law only entails that detached apples will fall when it is combined with some background assumptions about the approximate masses of objects such as the and apples. But these assumptions are unproblematic, and even if they were challenged, this would be more of a problem for Newton‟s Law than for Wonten‟s.

62 This can be demonstrated as follows. Let N = „Newton‟s theory is true‟ and W = „Wonten‟s theory is true‟. The entailment NW is equivalent to ~(N&~W) which entails that P(N&~W) = 0. From basic probability theory, we know that P(N) = P(N&W) + P(N&~W), so here P(N) = P(N&W). Similarly, P(W) = P(N&W) + P(~N&W). But P(N&W)  P(N&W) + P(~N&W), so P(N)  P(W).

63 It is interesting that the thing that makes Newton‟s Law less well-confirmed makes it preferable to Wonten‟s Law!

64 Wonten‟s Law makes novel predictions too, but these are neither interesting nor risky.

65 Even if we are only interested in apples, we might prefer Newton‟s Law to Wonten‟s Law. Wonten‟s Law tells us that apples fall, whereas Newton‟s Law tells us how fast they will fall.

91 non paribus. If a theory displays other important virtues, then they might prefer it to a theory that is better-confirmed.

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: WALSH, KIRSTEN

Title: Has Laudan killed the demarcation problem?

Date: 2009

Citation: Walsh, K. (2009). Has Laudan killed the demarcation problem? Masters Research thesis, Arts - School of Philosophy, and Social Inquiry, The University of Melbourne.

Publication Status: Unpublished

Persistent Link: http://hdl.handle.net/11343/35372

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