He’s still got it

Darwin’s theory of evolution generates almost as much suspicion today as it did when it first appeared in the nineteenth century.

The theory has two main components, and there are two corresponding sorts of unease about it. The first component is natural selection, in which organisms are shaped by environmental pressures. The second is sexual selection, in which organisms are shaped by the choices of potential sexual partners.

The first component of Darwin’s theory undermines the assumption of a cosmic designer, so the first sort of unease tends to be felt by people who have traditional religious beliefs. Notice, though, that natural selection doesn’t really undermine the looser idea that living things are shaped in an appropriate way for living in their environments. In a metaphorical sense they are “designed”, although they are not literally designed by a conscious or intelligent designer with a plan. The “watchmaker” is “blind”, in Dawkins’ metaphor, but he is still a bit like a watchmaker. Examples of convergent evolution (think of similarities between marsupial moles and placental moles) illustrate how environmental niches shape the living things that inhabit them: similar niches can shape their inhabitants in strikingly similar ways.

The second component of Darwin’s theory is quite different. If natural selection is all about “fitting in with the environment”, sexual selection is all about “standing out from the crowd”. Far from working towards a smoother or more economical fit between organism and environment, sexual selection introduces capricious extravagance. If natural selection makes for traits that are “sensible and practical”, sexual selection makes for traits that are “crazy and impractical”.

With sexual selection comes ostentatious ornamentation, “runaway” emphasis on arbitrary traits, advertising, “handicapping” to subvert false advertising, prodigious waste, ritual, and romance, among other things. Ironically, as intelligence — or at least choice — is an essential part of sexual selection, it tends to introduce features that are “stupid” inasmuch as they are unsuited to the environment, and “irrational” inasmuch as they are harmful to the individuals who have them. (So much for the nearest thing nature has to “intelligent designl”!) Some specific traits (such as the Irish elk’s gigantic antlers) no doubt contribute to the extinction of the entire species.

Darwin used the word ‘man’ (meaning mankind) in the title of his main work on sexual selection, because he recognised its importance for understanding the evolution of our own species. The idiosyncrasies of human behaviour, culture and art are more complicated than those of bower birds, but they are similar in that their main engine is usually sexual selection. We too should recognise its importance, and the relevance of evolutionary theory for our self-understanding as humans.

In the nineteenth century, delicate sensibilities and Victorian piety were offended by Darwinism. In the present day, delicate sensibilities and twenty-first century piety are still offended. Our pieties are moral rather than religious, and take the form of strong distastes for beliefs that can be construed as misogynistic, sexist, racist, or homophobic. Such beliefs as that men and women have innately different intellectual strengths, say, or that rape can be explained from an evolutionary perspective, say, are frowned upon in our day as much as atheism was in Darwin’s day. The hierarchical institutions which discourage such thoughts are no longer those of the church, but of academia.

Darwin still has the power to offend. A widespread reaction is to suppose that Darwin’s theory doesn’t apply to humans at all. We say that “humans are no longer evolving”, or that “human culture overrides human nature”, or that “our minds are wholly the products of environment”, or even that “there is no such thing as human nature”. Recently, a neuroscientist claimed that “male and female brains only differ because of the relentless ‘drip, drip, drip’ of gender stereotyping”.

But that is all wrong. Rather than yielding to pressure to avoid offence, or promoting a dishonest political agenda, we should stop frowning upon “impious thoughts” and instead try to avoid immoral actions. Misogyny, sexism, homophobia and racism are best understood not as “having the wrong beliefs” but as willingness to behave in ways that disregard interests because of group-membership. They’re morally wrong, often extremely so, but not because of anything like impiety.

Bronowski on “absolute knowledge”

In this moving clip taken from the very end of his acclaimed TV series The Ascent of Man, Jacob Bronowski speaks of two great human evils.

The first is the idea that “the end justified the means” — or as I would put it: if a particular end is treated as supremely valuable, its pursuit can ride roughshod over the many other competing values that characterise human life.

The second is the idea that we can have “absolute knowledge”. What does Bronowski mean by “absolute knowledge”? To understand this, consider how he defends science against the charge that it dehumanises people. He is standing in front of a dark pond in the grounds of Auschwitz, where the ashes of millions of people were flushed. These people were not victims of science. They were not killed by gas, he says, but by “arrogance”, “dogma” and “ignorance”:

When people believe that they have absolute knowledge, with no test in reality, this [gesturing towards the pool of death] is how they behave. This is what men do when they aspire to the knowledge of gods. Science is a very human form of knowledge. We are always at the brink of the known — we always feel forward for what is to be hoped. Every judgement in science stands on the edge of error and is personal. Science is a tribute to what we can know although we are fallible.

Now Bronowski doesn’t embrace any sort of “postmodernist” nonsense along the lines of “truth is relative”. He uses the words ‘true’ and ‘false’ freely, and clearly thinks they mean the same for everyone. Rather, in denying that we have “absolute knowledge”, his focus is on the traditional “justification” condition on knowledge. (It was traditionally thought that when we know something, we believe it, it is true, and we have a rational assurance or “justification” in believing it.) Bronowski is saying that justification or assurance is never absolute. It isn’t simply that it isn’t total or 100% — we can’t even measure it in an objective way. We can never have an impersonal or numerical assurance of what we believe or ought to believe. Assurance always depends on what each individual already believes, and that always differs from one individual to the next.

Bronowski is a “fallibilist” with respect to knowledge. That is, we are often mistaken, but we can have knowledge despite the ever-present possibility of error. Knowledge is a matter of our beliefs actually being true of the world. It’s an aspiration, a project guided by hope — and it’s often a matter of sheer luck. When we have knowledge, it’s not because our assurance is “absolute”, but because as a matter of fact our hope has paid off, and we have stumbled upon theories that happen to be true. In science, we have to “feel forward” in a tentative, exploratory way by guessing and then testing our theories against reality. The result of such tests is not a numerical measure of “how likely our theories are to be true”, but various hints and suggestions that we are “on to something” — which are bound to strike different individuals in different subjective ways. That’s part of what Bronowski means when he says science is a very “human form of knowledge”.

Nowadays, hardly anyone thinks we can have absolute certainty. Even the Nazis didn’t think that. But there is another “level of assurance”, which Descartes called “moral certainty”. This is not “total assurance”, but “assurance enough” to act so as to achieve some end. If we think assurance is absolute, objective, measurable, or suchlike, then everyone is rationally obliged to act in the same way to achieve the same end. I think that is the Nazi poison that Bronowski has in mind.

I think we should take Bronowski’s warnings seriously, and beware of movements that put one overriding end above all the other human values. And beware of claims that assurance can be objective or numerically measured.

Why would anyone think such a thing? I think such thoughts have two ingredients. The first is ambiguity in words such as ‘likely’ and ‘probable’. In science and statistics these words refer exclusively to relative frequency — that is, to the numerical proportion of members of a class that have some property. Sometimes, when we know practically nothing about a repeated phenomenon, we have to make judgements guided by nothing better than relative frequency. For example, consider gambling with cards, or wondering about Earth-collisions by objects such as comets and asteroids. If the only thing we know about such phenomena is the relative frequency of various hands of poker or of near misses in the long run, that is all we have to guide our behaviour. That’s how casinos make a profit and how governments should make contingency plans for asteroid collisions — and allocate resources for floods. It’s better than nothing, and it’s “objective”, but it’s not a measure of how much assurance we can have in believing anything.

Yet words such as ‘likely’ and ‘probable’ are often used in everyday parlance to refer to a supposedly objective assurance — assurance in believing that an individual event will occur, or that a given theory is true. Talk of numerical relative frequency often slides imperceptibly into talk of assurance.

The second ingredient is a worship of “science” in general — not this or that theory or branch of science, but the entire enterprise as if it were one monolithic body of assured knowledge. With this worship comes uncritical respect for “scientists” — not as practitioners of this or that branch of science, but as miracle workers whose opinions it is downright immoral to disagree with. Nowadays, it’s common to hear people proudly announcing that they “believe the science” — and implicitly shaming those who “refuse” to “believe the science”. That is a terrible state of affairs — and it represents a backward slide of civilisation. A descent rather than ascent.

Science consists of theories about the world. Many of these theories are about very abstract entities that can’t be observed directly. But none of them are about how much assurance we have that any scientific theory is true. Science doesn’t pronounce upon its own belief-worthiness. Anyone who says it does is either a fool or a fraud. That is to treat science as miraculous, and scientists as shamanistic miracle-workers, the purveyors of “absolute knowledge”.

What is “denial”?

When we say someone is “in denial”, we mean that they reject something obvious — something so obvious that their rejection of it amounts to a sort of pathology. For example, in the movie Psycho, Norman Bates interacts with the skeletal remains of his obviously dead mother as if she were still alive. This is not a sign of good mental health.

Although “deniers” deny facts, they usually do so for “emotional” reasons. They want something to be true so much that they pretend that some other things are false. Dolly Parton uses this idea effectively in The Grass is Blue:

There’s snow in the tropics
There’s ice on the sun
It’s hot in the Arctic
And crying is fun
And I’m happy now
And I’m so glad we’re through
And the sky is all green
And the grass is all blue

It’s vital to see that denial is not the mere rejection of facts — it’s the rejection of obvious facts, things that almost everyone can see easily with their own eyes.

We might say that denial is rejection of “observational” facts rather than “theoretical” facts. Fine, but all observation is “theory laden” — in other words, observations have to be interpreted, there’s no such thing as “raw data”, there’s no sharp distinction between observation and theory, and so on.

There is a gradient here, between facts that can be directly checked by simply opening our eyes and looking, and facts that are more abstract — facts that leave more room for doubt, that can be interpreted in several different ways, that depend on theoretical commitments that are not universally shared.

Some facts lie near enough to the observational end of the gradient to be counted as “almost observational” themselves. For example, we can’t quite see directly that the Earth is round. But nowadays we’re familiar with photographs taken from space of a round Earth, and most of us have watched ships slowly disappearing over the horizon, and so on. When we fly long distances, we adjust our watches in the perfectly reliable expectation that we will land in a different sector on the surface of a rotating sphere. Nowadays, a person who insists the Earth is not round is denying something very close to “obvious”.

Words like ‘denial’ can serve a useful purpose. But they are abused when applied to the rejection of claims that are not obvious. In that situation, their use amounts to an appeal to authority rather than an appeal to observation. The theoreticians whose opinions are rejected are supposedly so authoritative that it takes a sort of mental pathology to disagree with them.

I can’t think of a less sceptical or less scientific attitude than one that demands obedience to the authorities by “taking their word for it”. Heretics were tortured and killed by people who justified their sadism by saying their victims were suffering from a sort of pathology — one whose “cure” need not involve the giving of reasons.

Sometimes I have to restrain myself from using words like ‘denial’ for Creationists who reject the theory of evolution. But then I remind myself that the theory of evolution isn’t obvious — if it were, it wouldn’t have taken someone of Darwin’s stature to provide a satisfactory account of it. People who reject evolutionary theory are sceptical about something I believe in, but they can’t reasonably be called “deniers”. This also applies to other types of scepticism.

Two paradigms of evidence

Many people take valid deductive arguments to be the guiding ideal or “paradigm” of evidence. There are two obvious reasons for this. The first is that in mathematics, the proof of a theorem is essentially a deductive argument, and mathematical proof is perhaps the closest thing we can have to certainty. The second is that when people try to persuade one another of something, they appeal to shared beliefs, which each hopes will imply something the other has no choice but to accept. This gives the shared beliefs the function of premises — and persuasion becomes the derivation of a conclusion from those premises.

Buoyed by the thought that proof and persuasion are achieved by arguments, we cast about in search of their equivalent in “empirical enquiry” — and inevitably arrive at induction. (By ‘induction’ I always mean enumerative induction: for example, the sighting of several white swans leads to the general claim that all swans are white.) An inductive “argument” with true “premises” doesn’t guarantee the truth of its “conclusion” as a valid deductive argument does, but it does lead to it with mechanical inevitability. It leaves no room for choice as to what its conclusion will be. No “guesswork” is involved — the “data” determine the resulting “theory”. The latter is “based on” the former in much the same way as the conclusion of a deductive argument is “based on” its premises.

The ubiquity of the thought that “evidence consists of arguments” is underlined by the widespread use of words like ‘basis’, ’grounds’, ‘foundations’, ‘support’, etc. — as if these words were synonymous with ‘evidence’.

There’s a remarkable fact about arguments, which can be loosely expressed as follows: “the conclusion doesn’t tell us anything genuinely new — it just rearranges information already contained the premises”. That’s a loose way of putting it, because obviously theorems in mathematics can be surprising. But they’re mostly surprising because we don’t expect them to be able to say what they do say, given that they were derived from such meagre “input” as is contained in the axioms.

Theorems never “reach out” beyond what can be derived from the axioms. And the conclusion of an inductive argument only reaches out beyond what is contained in its premises inasmuch as it merely generalises from them. It can’t come up with new concepts. If we were limited to deduction and induction, we might be able to do logic and mathematics, and to generalise about what we can observe directly. But we wouldn’t be able to talk about the sort of things science talks about. In that sense, both deduction and induction are “closed” with respect to their “raw material”. Everything mentioned in their conclusions is internal to the system of axioms or beliefs expressed by their premises.

If we assume that evidence consists of arguments, it amounts to “being implied by what you know already”. It’s analogous to what can be got from a library that contains nothing but books you have already read. It’s an internal guarantee or assurance, the sort of thing that invites adjectives like “strong”, or possibly “overwhelming”.

But that sort of evidence doesn’t play a big role in science. Science isn’t trying to give us an internal sense of assurance, but to give us an understanding of external reality. In other words, it’s not aimed at justification but at truth. Unlike the best that can be achieved by deduction and induction, science “reaches out” beyond any system of axioms or beliefs working as premises. To achieve that, science simply cannot avoid guesswork. In embracing guesswork, scientific theory is not fully constrained by observation. In other words, theory is underdetermined by “data”. Typically, several possible theories are consistent with any given set of “data”.

A scientific theory is a representation of its subject matter. It can represent it by literally being true of it, or by modelling it. Hypotheses are true or false — they consist of symbols, some of which stand for real things. Models mimic the behaviour of some aspect of reality in some relevant respect. Either way, evidence in science consists of mere indications, often sporadic and peripheral, that the representations in question do in fact represent their subject matter faithfully or accurately. A theory is related to its subject matter in somewhat the same way as a map is related to its terrain. The image of map and terrain is appropriate — and the old image of conclusion and premises of an argument is inappropriate. The main purpose of observation in science is not to gather “data” to work as premises in an argument, but to check here and there to see whether the “map” and the “terrain” do in fact seem to fit.

Understood in this way, evidence is no longer a matter of proof or persuasion — of leaving no alternative to accepting a “conclusion” — but of seeking new indications that a representation is accurate. The most obvious such indications are passing tests and providing explanations. A theory passes a test when it predicts something that can be observed, and new observation confirms the prediction. A theory explains successfully when it newly encompasses something formerly baffling. Both involve seeking new facts rather than mechanically deriving something from old facts.

Science is more a process of discovery rather than of justification, and scientific evidence is more like what an explorer can bring to light through travel than what a scholar can demonstrate in his study.

Why does science insist on replicability of test results?

Replication of exactly the same test is epistemically worthless. Only by varying the conditions in which a test is done do we set up more “hurdles” for a hypothesis to “fall” at or “make it over”. In effect, varying conditions is a way of doing more tests. But inasmuch as any individual test gives us an independent reason to think a hypothesis is true, it differs from all of the other tests the hypothesis passes, and so it isn’t an exact repeat performance or perfect replication of any other test.

Of course we insist that test results should be reliable and objective. So we insist that they be inter-subjectively checkable, that they can in principle be done by different people, in different places, at different times.

The point of replicability is to prevent fraud or reliance on mere testimony. It’s not to provide many instances for an inductive generalisation to be based on. Even if science relied on induction like that — and I would argue that no genuine science does — perfectly exact replication would be of no use. For example, consider the inductive generalisation “all swans are white”. That would have to be based on several sightings of several white swans rather than the same single white swan. So even here, each individual sighting would have to differ from all of the others, at least insofar as it is the sighting of a different swan.

Utilitarianism and the “golden rule”

According to JS Mill, “In the golden rule of Jesus of Nazareth, we read the complete spirit of the ethics of utility. To do as one would be done by, and to love one’s neighbour as oneself, constitute the ideal perfection of utilitarian morality.”

Mill was an avowed atheist. Why did he draw such a close connection between his own utilitarianism and the ethics of original Christianity?

Trivially, we all tend to maximise the satisfaction of our own preferences when we act. By choosing to do X rather than Y, we reveal that we prefer doing X to doing Y. To prefer X to Y is simply to “go for” X rather than Y.

Normally, we would like other people to maximise the satisfaction of our preferences when they act as well, because that would help us to achieve our goals, which we are already striving to achieve through our own actions. To have additional help in doing so — to have them throw their weight behind our own attempts to satisfy our own preferences as much as possible — is how we “would be done by” them.

So if we were to do to them as we would be done by them, we would try to satisfy their preferences as much as possible. And of course the same thing can be said symmetrically for them. So if the “golden rule” of doing as we would be done by were followed by everyone, everyone would be striving to satisfy preferences in general as much as possible.

If we understand interests as the satisfaction of preferences, as I think we should, it means that all interests count, no matter whose interests they may be. To be more precise: since preferences can be strong or weak, interests should be given due consideration — which means they should be respected for the actual strength of the preference they correspond to. When thinking about animals of the same species such as humans, “due” consideration nearly always means equal consideration.

If morality were the only motivating factor in human life, and everyone accepted preference utilitarianism, then everyone would respect preferences in general. This would necessarily involve a huge amount of compromise, as each individual’s preferences inevitably come into conflict other individuals’ preferences. But as an ideal, preferences would be respected as much as possible regardless of whose preferences they were.

Of course in reality the effects of our actions are limited. I can’t affect people who live a very long way away from me (although this changes as time passes). And our knowledge is limited: I cannot predict what effect my actions will have on people living in the distant future. But I can affect people who live reasonably close to me (in causal terms) yet who can’t be counted as either family or friends. At one time, these people would literally have been my “neighbours”. The preference utilitarian moral ideal is to respect their preferences as much as my loved ones’ preferences and my own preferences. The ideaI would be to “love my neighbour as myself”.

So much for what Mill called the “ideal perfection of utilitarian morality”. It’s an ideal because no one could hope to fully achieve it in action, and there are other important values that complete with moral value — such as beauty, truth, clarity, love, loyalty, eroticism, profit, and fun (which are just the first few I can think of).

Yet preference utilitarianism is not what is normally called “idealism” in the political sense. No perfect state needs to be achieved for utilitarian morality to “work”, as Marxism might need the total embrace of communism to “work”, or libertarianism might need a perfectly free market to “work”. In the imperfect world as it really is right now, utilitarians strive to behave morally by satisfying preferences as best they can. Where the strongest preferences are at stake, moral value can become the principal guiding light of action. Like the demands of original Christianity, the demands of utilitarianism can never be perfectly met — but we can strive to approximate meeting them, the closer the better. To my mind, that is a humane and realistic thing to strive for.

Demarcation and the magic of science

Is psychology a science? Are the methods used by climate scientists more pseudo-scientific than genuinely scientific? — When we ask questions like these, the issue is “demarcation”: How can we distinguish or demarcate genuine science from non-science?

The word ‘science’ has become such a warm — almost religious — term of approval that practically everyone nowadays is eager to call whatever they do “science”. So hearing the word ‘science’ in a description  — especially self-description — of what people do isn’t a reliable indicator that what they do actually is science. When you hear the word ‘science’, beware: there are impostors about.

Followers of Popper say that the mark of science is “falsifiability”: if there is no way a hypothesis can be shown to be false, then it’s not scientific. I hope it’s clear why. We all accept that hypotheses can’t be conclusively verified: a hypothesis is just a guess, and as a guess it can never be proved true the way theorems in mathematics can. But of course a good scientific hypothesis has more going for it than mere guesswork. It “sticks its neck out” by saying something about the real world. If it gets it wrong, the hope is that it will be exposed as false; and if it hasn’t been so exposed, at least not so far, that counts in its favour. But in order to have that count in its favour, it has to be able to stick its neck out. If it can’t do that, followers of Popper think, it can’t be regarded as a scientific hypothesis.

I think these followers of Popper are on to something important, but they’re not quite there yet. Why not? — Because of “holism”, hypotheses can never be decisively falsified either. Hypotheses are held by individual people, along with all the other stuff they believe, which are also hypotheses. Any particular hypothesis which is subject to testing — and therefore potential falsification — is tested alongside innumerable other hypotheses. Together, they imply something that can be observed, and that something either is observed or else it isn’t observed — it’s a “1 or 0 outcome”, if you like.

Followers of Popper want to say that if that something isn’t observed as predicted, then the hypothesis that yielded the prediction is falsified. So even though we have to reject it, it was a scientific hypothesis to begin with; if we had been lucky enough not to have to reject it, so much the better. But things are not as simple is this. Any of the hypotheses, plural, that went into generating the prediction might be singled out as the culprit. With a bit of judicious weeding and planting, we can tend our “garden” so as to keep what we want. For example, my belief in the steady-state theory of the universe yields the prediction that no red shift should be observed in distant astronomical objects. When it is in fact observed, contrary to my prediction, am I obliged to declare my hypothesis falsified? — No, I am not. I can simply make up a new hypothesis, to the effect that light gets “tired” when it travels for very long periods of time and loses energy. This loss of energy manifests itself as decreasing frequency.

In fact, any favoured hypothesis can be protected from the threat of unfavourable observations like that. So any hypothesis can be held in such a way as to make it practically unfalsifiable. So we can’t appeal to the falsifiability or otherwise of a hypothesis as the mark or standard of its being genuinely “scientific”.

It doesn’t follow that no such standard is possible. But instead of focusing on the falsifiability of hypotheses, we should consider instead the way hypotheses are held. If peripheral “excuses” are habitually made so that a hypothesis is held with such tenacity that it is in effect unfalsifiable, what is held is no longer a scientific hypothesis but an ideology. Hypotheses that are made up for the sole purpose of protecting another, favoured hypothesis are called ad hoc hypotheses. Anyone who cares about science should be constantly on the look-out for too great a willingness to make up ad hoc hypotheses. Eagerness of that sort is the mark of ideology rather than science. People in the grip of an ideology can believe almost anything they like, as long as they are prepared to bend over backwards far enough to accommodate their central hypothesis — the one they like.

Followers of Popper who emphasise falsifiability aren’t far wrong, though. What they get right, I think, is the importance of passing tests — by which is meant the honest prediction of otherwise unexpected results, followed by actual observation of the unexpected results. It emphatically does not mean the mere fitting of hypotheses or models to data that have already been gathered and don’t give rise to any sense of surprise.

For a hypothesis to be tested, it must have predictive power. The hypothesis purports to describe things that can’t be observed directly, but it implies things that can be observed, which wouldn’t been anticipated but for the hypothesis yielding its prediction, and so which without it would seem surprising.

Perhaps even more important than predictive power is explanatory power. A hypothesis that has great explanatory power enjoys a logically similar position to one that has great predictive power, with “bafflement” taking the place of “surprise”. In both cases, something that baffles us (in the case of explanation) or would otherwise surprise us (in the case of prediction) is “newly encompassed”. In both cases, what is newly encompassed is implied by a description of some hidden aspect of reality. Such “drawing back of the curtain on reality” is the magic of science, really, and its ability to do that rightly commands respect and often deserves belief. Anything that doesn’t succeed at that feat — whatever masquerades as science but has no real predictive or explanatory power — doesn’t have a legitimate claim to be believed.

Knowledge and hope

The traditional understanding of knowledge as “justified true belief” is internalist. That is to say, for a true belief to count as an item of knowledge, it must satisfy a third condition of being “justified”, which is a state of mind. Justification is traditionally understood as being internal to the mind.

More recent “naturalized” epistemology is externalist. That is to say, for a true belief to count as an item of knowledge, it must satisfy a third condition of being connected in a special way to the real world “outside” the mind. There are various ways of characterizing this special connection: it must be reliable, it must “track” truth, it must be sustained by a law-like process, the belief in question must be non-accidentally true. I’ll use the word ‘reliable’. But whichever words we use for it, the connection reaches outside the mind, and so part of it is external to the mind.

I think these two ways of thinking about knowledge correspond to “is” and “ought” in an interesting way.

The internalist is looking for justification — and (in theory at least) he can check whether a belief is justified by examining the way it is linked to his other beliefs through relations of implication. Foundationalists think justified beliefs are implied by “basic” beliefs; coherentists think there is a network of mutual implication. Either way, these other beliefs are in the mind, and so they can potentially be put “before the mind” for inspection. According to this understanding of knowledge, we can have assurance that we know. In fact the main thrust of traditional epistemology is “doctrinal”: it’s aimed at assuring the radical sceptic that we do in fact have knowledge. We know something when a belief is justified, and it is justified or not as a matter of fact — an “is”.

Instead of seeking justification, the externalist wants reliability. And he isn’t “looking” for reliability so much as “hoping” for it. He can’t directly check whether the connection he hopes is reliable actually is reliable, because one end of it lies outside his mind. According to this understanding of knowledge, we can’t have an internal assurance that we know, because some aspects of knowledge are aspirational. We aspire to the goal of having reliably true beliefs. To the potential knower, such aspirations are better expressed by the word ‘ought’ than ‘is’. None of the beliefs he already has — as a matter of fact — can imply that these aspirations are met, because “oughts” cannot follow from “is”s alone.

This aspirational aspect of knowledge might be likened to “the object of the game” for a chess-player. The would-be knower and the chess player have goals: to have reliably true beliefs, and to get the opponent’s king into checkmate, respectively. These goals are the object of “oughts”: the would-be knower’s beliefs ought to be reliably true, and the player’s moves ought to bring the goal of checkmate closer. In both cases, the “ought” guides behavior in a nontrivial way.

Of course neither of these is a moral “ought”. Proper epistemic practice obliges us rationally rather than morally to aim for reliably true beliefs. Chess players’ implicit acceptance of the rules of chess — specifically, the rule that specifies the object of the game — obliges them to aim for checkmate. Someone who gets bored and plays “suicidal” chess to end a game quickly isn’t guilty of a moral failing, he’s just not playing chess properly.

The chess player has to interact with his opponent: he can’t focus on his own moves to the exclusion of his opponent’s moves. Analogously, the potential knower has to interact with the world: he can’t focus on his own beliefs to the exclusion of “answers” the world gives in response to the “questions” he “asks” it. In practice, this “questioning” of the world is the testing of hypotheses. To form new beliefs or new theories by simply building on what one already believes — including “data” — is like playing chess without paying attention to your opponent’s moves. In effect, this is what Bayesians do when they make epistemic decisons on the basis of “degrees of belief”. (I shall have more to say about Bayes’ Theorem in a forthcoming blog post.)

The “object of the game” of empirical knowledge is to transcend internal assurances and aim for reliably true beliefs — an external matter, which usually involves the testing of hypotheses.

I mentioned above that traditionally, epistemology was internalist. The tradition continues to this day, and it affects the way epistemology is taught in university philosophy courses: they tend to begin with Descartes’ Meditations, and typically don’t move very far beyond that. They tend to treat Gettier problems as a mere curiosity. Internalism can also affect the way scientists do science. Some sciences — especially those that appeal to “overwhelming evidence” to counter scepticism — use “internalist” methods of shaping models to fit “data” that may as well have been gathered beforehand. In effect, this is to eschew testing in favor of an internalist sense of assurance.

Proper science and knowledge are aimed at truth, not at assurance. Their aspirational aspects entail that testing is essential. To use another analogy: a miser might get assurance from counting coins he already owns, but he can’t make money unless he runs the risk of losing money by investing it in outside projects. In pursuit of truth rather than profit, science too must “cast its net” beyond “data” already gathered.

Whatever it is, I’m against it

Irish President Michael D Higgins — a sociologist, not a philosopher — is leading a campaign to teach philosophy in secondary schools. Almost everyone seems to welcome this idea. I think it stinks. — Why? What could possibly go wrong with such a laudable enterprise?

It seems to me that whatever eventually gets taught, it will affect students of low, middle and high ability in different ways. It will intimidate those at the bottom, indoctrinate those in the middle, and infuriate those at the top.

Let’s start at the bottom. Philosophy is hard. I don’t just mean that it requires intelligence. More importantly, it requires a “strange” turn of mind with the creativity to juxtapose previously unconnected ideas, the imagination to consider far-fetched scenarios, the ability  to look at things from a “meta-level” perspective, and comfort with abstraction.

Some of the most intelligent, gifted people I’ve ever met didn’t have this required strangeness of mind, and were hopeless at philosophy. They simply didn’t “get it”. They were self-confident adults who knew they had an abundance of talent in other areas, so they weren’t downcast by their lack of ability in this one area. But I shudder to think of what less confident children will make of a topic that is simply incomprehensible to them.

Moving up to students of middling ability: here they will get the central idea, and most will enjoy the classes. But what they are taught won’t be genuine philosophy. It will be what their teachers call “thinking skills” and “ethics”. Proponents of teaching philosophy in schools try to defend the neutrality of “thinking skills” by saying it just means formal logic, “critical thinking” and the like. This is a good time to remember the motto of all good philosophy: “know thyself”. Let’s be honest. What we call “skill” in thinking consists of thinking we approve of. I rate Kant an unskilled thinker, others think he is one of the greatest philosophers who ever lived. Many rate AJ Ayer an unskilled thinker because they disagree with what he thinks. And so on. Even in universities, teachers of philosophy are prone to presenting philosophical ideas to show them up as flawed, and to set up the alternative as more correct. Always, the “more correct” way of thinking is the one that is more highly skilled according to their own lights.

But wait. Formal logic must be neutral, right? Well, logic itself may seem value-free and theoretically uncommitted, but learning logic isn’t. If we treat deductive arguments as the acme of human thought, we will inadvertently promote the most plodding forms of traditional epistemology, which assume that the ideal of reason is to be the conclusion of a valid argument. That’s not a deliberate attempt to brainwash, but it is an insidious form of indoctrination. (Personally, I blame that indoctrination for so much bad science using brainless inductive methods.)

The vast majority of great thinkers — scientists, mathematicians, artists — never took a logic class in their lives. A person doesn’t need to study logic to think in a logical way. I don’t think studying logic improves a person’s ability to think logically. At best it may help describe the patterns that logical thought takes, so those who have a special interest in such things can communicate with one another. Analogously, learning to be a theatre critic doesn’t make one a better playwright. The arrogance of those who assume better thinking results from “thinking like me” is breathtaking.

This urge to shape minds really shifts into high gear with ethics. Just reflect for a moment on how many well-meaning secondary school teachers will be eager to correct the “unskilled thinking” behind such evils as sexism, homophobia, and “climate denial”. On the receiving end, many well-meaning secondary school students will be eager to have their moral prejudices confirmed by the “authority” of a philosophy teacher. When these two kinds of evangelism meet, the result is missionary zeal — for orthodoxy.

That is nothing like real philosophy, which involves awkward questions rather than agreeable-sounding answers. But even if a few teachers and students realize they’re doing nothing like real philosophy, their hands are tied, because strictly speaking these students are children, and children are forbidden to discuss awkward questions. It’s too invasive.  Teachers can’t allow children to discuss such questions as whether suicide is an act of spite, whether homosexuality is a mental disorder, whether sexual or racial differences are innate. Many of the students are bound to be “affected by these issues”, as they warn on BBC. But even those who want to discuss them can’t give their consent to do so because they’re just children.

This brings us to the top level — the small proportion of students who do have some real philosophical acumen. They’ll have a good idea of what philosophy is all about, because they’ll probably have done a bit of it on their own already. They will recognize that the promotion of orthodoxy and curtailment of free discussion falls far short of the real thing. That will anger and frustrate them, and it may even get some of them into trouble. Despite ill-informed rumors to the contrary, anything like real Socratic dialogue is dangerous.

It is this last type of student who might choose to study philosophy at the third level, in university. But how many will be put off by the cardboard sham that passes for philosophy at the second level?

Most philosophy is bad philosophy. Good philosophy mostly consists of un-learning the bad philosophy you learned before, or arrived at on your own before you realized you were doing philosophy.

What is induction?

I use the word ‘induction’ a lot. But the word can be a bit slippery. Hume is celebrated for his “problem of induction”, and he was indeed concerned with what we nowadays call “induction”. But Hume himself didn’t use the word ‘induction’ for what he had his problem with.

What do I mean when I use the word ‘induction’?

A classic example of induction is the inference from “the swans I’ve seen so far have been white” to “all swans are white”. This inference assumes that “nature continues uniformly the same” (as Hume put it), so that as-yet unseen swans are similar in the relevant way to the swans I’ve seen already. Nowadays, we would put it in terms of scientific laws, which describe the sort of reliably universal regularities Hume had in mind.

Putting it in terms of scientific laws conveniently illustrates why induction is often unreliable. Swans are not universally white — some are black. Because swans’ color is not regular in the lawlike way required — in other words because nature does not continue uniformly the same from one swan to the next as far as color is concerned — the inference above is unreliable.

Because inferences like that involve generalization, induction is sometimes characterized as inference “from the particular to the general”, but that is actually a rather poor way of characterizing it. Words like ‘all’ can appear in the “premises” of inductive inferences as well as being absent from their “conclusions”. For example, consider the inference from “all of the electrons observed so far have had a charge of minus one” to “the next electron we observe will have a charge of minus one”, or to “any electron has a charge of minus one”, or to “the electron is a subatomic particle with charge minus one”. Superficially, these look like inferences from greater to lesser generality.

The assumption of lawlike regularity

At the risk of belaboring the point, it isn’t always obvious when induction is involved in an inference. For an induction to be reliable, it has to be underwritten by a lawlike regularity in the real world, whether or not we are aware of it — it’s often a matter of sheer luck. But even when an induction is unreliable because there is no real lawlike regularity, it still counts as a case of induction if it assumes that there is.

So, if we’re wondering whether induction is involved in an inference, it’s probably safer to look for an assumption of lawlike regularity than to look for words that typically signal generalization or extrapolation.

Sometimes the required assumption of lawlike regularity isn’t all that obvious. Suppose we take a sample of people and find that 10% of them have red hair. We then use statistical extrapolation — an application of induction — to claim that 10% of the entire population of the world has red hair. For this to be any better than a shot in the dark, the sample must be representative of the world’s population, at least in respect of the proportion who have red hair. It isn’t enough for the sample to reflect this feature of the larger population by accident — it must do so systematically, so that the generalization from sample to entire population is non-accidental. (Laws are sometimes characterized as “non-accidental generalizations”.)

Statistical extrapolation

There is more than one way an induction can go wrong. In the current example, if the proportion attributed to the entire population is too precise — for example, if we claim that exactly 10.0001% have red hair because that is the exact proportion in the sample — the detail is overly-fine-grained. Detail of that sort — not underwritten by lawlike regularity — is merely artifactual. That is, it is a misleading by-product of our own methodology rather than a feature of the real world. It is analogous to seeing one of your own eyelashes reflected in the lens of a microscope.

Skillful sampling is vital for reliable statistical extrapolation. A sample should be representative of the population as a whole, and that takes skill. Some rigorous-looking statistical methods are meant to estimate how representative samples are, but too often, these methods themselves rely on induction, by extrapolating the variability of samples to the entire population. To my mind, these statistical methods are the products of a quest for assurance rather than a quest for truth. A better idea is to test sampling techniques. For example, the sampling techniques of voter popularity polls before elections are tested by actual election results. Nate Silver accepted credit for predicting the most recent US election results, but more credit is due to the people who were able to get such representative samples of voters.

Non-numerical examples of induction are tricky enough. Things get worse when numbers are involved. Perhaps worst of all is the completely spurious idea that we can have a numerical measure of “how much the conclusion of an induction deserves to be believed”, usually assumed to be some arithmetical function of “the number of instances an induction is based on”.

Induction versus guessing and testing

I hope it is clear that there are several “problems of induction”. It is a distinctly problematic form of reasoning, mostly because it apes deduction. It’s what people come up with when they try to imagine what an argument would look like if it could deliver “empirical” conclusions that do more than just re-arrange ideas expressed in premises. Behind it lies the malign assumption that evidence consists of being shown to be the conclusion of an argument. (I beseech you, dear reader, to reject this assumption!) When combined with popular ideas about science being “based on observation”, induction can acquire a hallowed status — a status it doesn’t deserve. It’s not the “complementary, alternative form of reasoning to deduction”, and it doesn’t appear much in any of the respectable sciences.

Rather than relying on induction, science is mostly a matter of guessing followed by testing. Rather than starting off with observations and proceeding by extrapolating from them in a mechanical way, science starts off with explanatory hypotheses and proceeds by devising tests for them — feats that call for imagination, creativity, and cunning. Rather than seeking assurances in the form of inductive arguments, science seeks truth by casting a wider net to check for falsity.

Karl Popper recognized the centrality to science of making “bold conjectures” that stand despite the possibility of their refutation. He rejected induction altogether as unscientific. But I think he went way too far here. I also think it was ridiculous to claim as he did that a theory’s passing a test doesn’t give us any reason to think the theory is true.

I would argue that passing tests usually gives us good reason for thinking theories are true. I agree with Quine that induction is a special case of the hypothetico-deductive method (of guessing and testing just mentioned) and that a broader understanding of the latter helps to explain why induction is sometimes reliable.

One of the main attractions of induction is it removes the sense of guesswork from empirical reasoning. Instead of “having a stab” at things, induction “frog-marches” us from observations to conclusion in what might seem a reassuringly “inescapable” way. It has a mechanical feel, like deduction. Let us not be too easily seduced by these attractive features!

Example: modeling a compound pendulum

I’ll try to illustrate why with an example — an artificial one, concocted specially to show how induction can fail to deliver the goods. Image a compound pendulum of two rigid parts that behaves chaotically — that is, its configuration critically depends on initial conditions, so that over time its movements are practically unpredictable. And because they are practically unpredictable, they can’t be modeled in a computer. (Not in practice, anyway.) I can make computer versions of compound pendulums that behave just like real compound pendulums behave, but I can’t make a single computer model that mimics the behavior of a given actual compound pendulum.

But suppose I don’t know that. Suppose I set out to create a computer model of this very apparatus, encouraged by the thought that each of its two moving parts behaves in a well-understood, completely lawlike way. As “inputs” I obviously use my knowledge of the simple, elegant laws that describe their movements. But I also know that more than that’s involved: I need to experiment a bit with different lengths of the rigid bars that make such a pendulum, different masses, different centers of gravity, different moments of inertia, and so on.

Every time I run my model, adjusting one or other of these input variables, I compare the progress of my computer model and the configuration over time of the actual compound pendulum, to see where they begin to diverge. Although I will make progress at first, there will come a point in every single run at which the divergence becomes significant — too large for me to count my model as a model of that actual, given compound pendulum.

Now, if I were asked to defend my computer modeling, I might say that I have been working with numerous models, and that they all have been rigorously “tested”. The models that have failed such a “test” have been diligently thrown out, I might claim, and I have learned from my errors by making new models with better initial values for the relevant variables.

But I would just be kidding myself. None of these models is a “bold conjecture”, nor is any of the “tests” anything like the real test of a hypothesis. A real test involves cunning on the part of the experimenter — and sweating palms on the part of the theorist whose reputation is on the line. There is the possibility of failure rather than the expectation of some further tinkering. What we have here instead is the mechanical adjustment of numerical values to fit some “data” that may as well have been gathered beforehand. Rather than individual models being tested, the entire process of model-generation is being adjusted to fit a series of data-points. In effect, it is the fitting of a curve through them. This fitting is guided by the assumption that future motion of the pendulum will “continue uniformly the same” in matching the progress of the computer model. It assumes there is a lawlike connection between them, or at least that one can be found. (It can’t.)

This is induction. It has many of the problems that attend induction. There is no way around the fatal mismatch between model and reality that I purposely built into the example.

A compound pendulum is a very simple apparatus, whose behavior can’t be captured by induction. I leave it as an exercise how much we can hope that anyone could model more complicated items such as the climate of an entire planet subject to many, many more variables.