The Beginning of Infinity: Explanations That Transform the World (54 page)

There are circumstances under which there
is
a good explanation linking the measurable proxy such as marking checkboxes with a quantity of interest, and in such cases there need be nothing unscientific about the study. For example, political opinion surveys may ask whether respondents are ‘happy’ with a given politician facing re-election, under the theory that this gives information about which checkbox the respondents will choose in the election itself. That theory is then tested at the election. There is no analogue of such a test in the case of happiness: there is no independent way of measuring it. Another example of bona-fide science would be a clinical trial to test a drug purported to alleviate (particular identifiable types of) unhappiness. In that case, the objective of the study is, again, to determine whether the drug causes
behaviour
such as saying that one is happier (without also experiencing adverse side effects). If a drug passes that test, the issue of whether it really makes the patients happier, or merely alters their
personality to have lower standards or something of that sort, is inaccessible to science until such time as there is a testable explanatory theory of what happiness is

In explanationless science, one may acknowledge that actual happiness and the proxy one is measuring are not necessarily equal. But one nevertheless calls the proxy ‘happiness’ and moves on. One chooses a large number of people, ostensibly at random (though in real life one is restricted to small minorities such as university students, in a particular country, seeking additional income), and one excludes those who have detectable extrinsic reasons for happiness or unhappiness (such as recent lottery wins or bereavement). So one’s subjects are just ‘typical people’ – though in fact one cannot tell whether they are statistically representative without an explanatory theory. Next, one defines the ‘heritability’ of a trait as its degree of statistical correlation with how genetically related the people are. Again, that is a non-explanatory definition: according to it, whether one was a slave or not was once a highly ‘heritable’ trait in America: it ran in families. More generally, one acknowledges that statistical correlations do not imply anything about what causes what. But one adds the inductivist equivocation that ‘they can be suggestive, though.’

Then one does the study and finds that ‘happiness’ is, say, 50 per cent ‘heritable’. This asserts nothing about happiness itself, until the relevant explanatory theories are discovered (at some time in the future – perhaps after consciousness is understood and AIs are commonplace technology). Yet people find the result interesting, because they interpret it via everyday meanings of the words ‘happiness’ and ‘heritable’. Under that interpretation – which the authors of the study, if they are scrupulous, will nowhere have endorsed – the result is a profound contribution to a wide class of philosophical and scientific debates about the nature of the human mind. Press reports of the discovery will reflect this. The headline will say, ‘New Study Shows Happiness 50% Genetically Determined’ – without quotation marks around the technical terms.

So will subsequent bad philosophy. For, suppose that someone now does dare to seek explanatory theories about the cause of human happiness. Happiness is a state of continually solving one’s problems, they conjecture. Unhappiness is caused by being chronically baulked in one’s attempts to do that. And solving problems itself depends on
knowing how; so, external factors aside, unhappiness is caused by not knowing how. (Readers may recognize this as a special case of the principle of optimism.)

Interpreters of the study say that it has refuted that theory of happiness.
At most 50 per cent
of unhappiness can be caused by not knowing how, they say. The other 50 per cent is beyond our control: genetically determined, and hence independent of what we know or believe, pending the relevant genetic engineering. (Using the same logic on the slavery example, one could have concluded in 1860 that, say, 95 per cent of slavery is genetically determined and therefore beyond the power of political action to remedy.)

At this point – taking the step from ‘heritable’ to ‘genetically determined’ – the explanationless psychological study has transformed its correct but uninteresting result into something very exciting. For it has weighed in on a substantive philosophical issue (optimism)
and
a scientific issue about how the brain gives rise to mental states such as qualia. But it has done so without knowing anything about them.

But wait, say the interpreters. Admittedly we can’t tell whether any genes
code
for happiness (or part of it). But who cares how the genes cause the effect – whether by conferring good looks or otherwise? The effect itself is real.

The effect is real, but the experiment cannot detect how much of it one can alter without genetic engineering, just by knowing how. That is because the way in which those genes affect happiness may itself depend on knowledge. For instance, a cultural change may affect what people deem to be ‘good looks’, and that would then change whether people tend to be made happier by virtue of having particular genes. Nothing in the study can detect whether such a change is about to happen. Similarly, it cannot detect whether a book will be written one day which will persuade some proportion of the population that all evils are due to lack of knowledge, and that knowledge is created by seeking good explanations. If some of those people consequently create more knowledge than they otherwise would have, and become happier than they otherwise would have been, then part of the 50 per cent of happiness that was ‘genetically determined’ in all previous studies will no longer be so.

The interpreters of the study may respond that it has proved that
there can be no such book! Certainly none of them will
write
such a book, or arrive at such a thesis. And so the bad philosophy will have caused bad science, which will have stifled the growth of knowledge. Notice that this is a form of bad science that may well have conformed to all the best practices of scientific method – proper randomizing, proper controls, proper statistical analysis. All the
formal
rules of ‘how to keep from fooling ourselves’ may have been followed. And yet no progress could possibly be made, because
it was not being sought
: explanationless theories can do no more than entrench existing, bad explanations.

It is no accident that, in the imaginary study I have described, the outcome appeared to support a pessimistic theory. A theory that predicts how happy people will (probably) be cannot possibly take account of the effects of knowledge-creation. So, to whatever extent knowledge-creation is involved, the theory is prophecy, and will therefore be biased towards pessimism.

Behaviouristic studies of human psychology must, by their nature, lead to dehumanizing theories of the human condition. For refusing to theorize about the mind as a causative agent is the equivalent of regarding it as a non-creative automaton.

The behaviourist approach is equally futile when applied to the issue of
whether
an entity has a mind. I have already criticized it in
Chapter 7
, in regard to the Turing test. The same holds in regard to the controversy about animal minds – such as whether the hunting or farming of animals should be legal – which stems from philosophical disputes about whether animals experience qualia analogous to those of humans when in fear and pain, and, if so, which animals do. Now, science has little to say on this matter at present, because there is as yet no explanatory theory of qualia, and hence no way of detecting them experimentally. But this does not stop governments from trying to pass the political hot potato to the supposedly objective jurisdiction of experimental science. So, for instance, in 1997 the zoologists Patrick Bateson and Elizabeth Bradshaw were commissioned by the National Trust to determine whether stags suffer when hunted. They reported that they do, because the hunt is ‘grossly stressful . . . exhausting and agonizing’. However, that
assumes
that the measurable quantities denoted there by the words ‘stress’ and ‘agony’ (such as enzyme levels
in the bloodstream) signify the presence of qualia of the same names – which is precisely what the press and public assumed that the study was supposed to
discover
. The following year, the Countryside Alliance commissioned a study of the same issue, led by the veterinary physiologist Roger Harris, who concluded that the levels of those quantities are similar to those of a human who is not suffering but enjoying a sport such as football. Bateson responded – accurately – that nothing in Harris’s report contradicted his own. But that is because neither study had any bearing on the issue in question.

This form of explanationless science is just bad philosophy disguised as science. Its effect is to suppress the philosophical debate about how animals should be treated, by pretending that the issue has been settled scientifically. In reality, science has, and will have, no access to this issue until explanatory knowledge about qualia has been discovered.

Another way in which explanationless science inhibits progress is that it amplifies errors. Let me give a rather whimsical example. Suppose you have been commissioned to measure the average number of people who visit the City Museum each day. It is a large building with many entrances. Admission is free, so visitors are not normally counted. You engage some assistants. They will not need any special knowledge or competence; in fact, as will become clear, the less competent they are, the better your results are going to be.

Each morning your assistants take up their stations at the doors. They mark a sheet of paper whenever someone enters through their door. After the museum closes, they count all their marks, and you add together all their counts. You do this every day for a specified period, take the average, and that is the number that you report to your client.

However, in order to claim that your count equals the number of visitors to the museum, you need some explanatory theories. For instance, you are assuming that the doors you are observing are precisely the entrances to the museum, and that they lead
only
to the museum. If one of them leads to the cafeteria or the museum shop as well, you might be making a large error if your client does not consider people who go only there to be ‘visitors to the museum’. There is also the issue of museum staff – do they count as visitors? And there are visitors who leave and come back on the same day, and so on. So you need quite a sophisticated explanatory theory of what the client means
by ‘visitors to the museum’ before you can devise a strategy for counting them.

Suppose you count the number of people coming
out
as well. If you have an explanatory theory saying that the museum is always empty at night, and that no one enters or leaves other than through the doors, and that visitors are never created, destroyed, split or merge, and so on, then one possible use for the outgoing count is to check the ingoing one: you would predict that they should be the same. Then, if they are not the same, you will have an estimate of the
accuracy
of your count. That is good science. In fact reporting your result without also making an accuracy estimate makes your report strictly meaningless. But
unless
you have an explanatory theory of the interior of the museum – which you never see – you cannot use the outgoing count, or anything else, to estimate your error.

Now, suppose you are doing your study using explanationless science instead – which really means science with unstated, uncriticized explanations, just as the Copenhagen interpretation really assumed that there was only one unobserved history connecting successive observations. Then you might analyse the results as follows. For each day, subtract the count of people entering from the count of those leaving. If the difference is not zero, then – and this is the key step in the study – call that difference the ‘spontaneous-human-creation count’ if it is positive, or the ‘spontaneous-human-destruction count’ if it is negative. If it is exactly zero, call it ‘consistent with conventional physics’.

The less competent your counting and tabulating are, the more often you will find those ‘inconsistencies with conventional physics’. Next,
prove
that non-zero results (the spontaneous creation or destruction of human beings) are inconsistent with conventional physics. Include this proof in your report, but also include a concession that extraterrestrial visitors would probably be able to harness physical phenomena of which we are unaware. Also, that teleportation to or from another location would be mistaken for ‘destruction’ (without trace) and ‘creation’ (out of thin air) in your experiment and that therefore this cannot be ruled out as a possible cause of the anomalies.

When headlines appear of the form ‘Teleportation Possibly Observed in City Museum, Say Scientists’ and ‘Scientists Prove Alien
Abduction is Real,’ protest mildly that you have claimed no such thing, that your results are not conclusive, merely suggestive, and that more studies are needed to determine the mechanism of this perplexing phenomenon.

You have made no false claim. Data can become ‘inconsistent with conventional physics’ by the mundane means of containing errors, just as genes can ‘cause happiness’ by countless mundane means such as affecting your appearance. The fact that your paper does not point this out does not make it false. Moreover, as I said, the crucial step consists of a definition, and definitions, provided only that they are consistent, cannot be false. You have
defined
an observation of more people entering than leaving as a ‘destruction’ of people. Although, in everyday language, that phrase has a connotation of people disappearing in puffs of smoke, that is not what it means in this study. For all you know, they
could
be disappearing in puffs of smoke, or in invisible spaceships: that would be consistent with your data. But your paper takes no position on that. It is entirely about the outcomes of your observations.

So you had better not name your research paper ‘Errors Made When Counting People Incompetently’. Aside from being a public-relations blunder, that title might even be considered unscientific, according to explanationless science. For it would be taking a position on the ‘interpretation’ of the observed data, about which it provides no evidence.