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WHAT IS A GOOD PREDICTION?
…an intellectual trait widely considered a great asset in science – the commitment to
parsimony – can be a substantial liability in real-world forecasting exercises. (Tetlock
2005, 68)
ABSTRACT
Prediction has not received treatment as a philosophical topic in its own right. It has
been discussed in connection with explanation, realism, and other topics. But the
question “What is a good prediction?” has not been the focus of these treatments. This
paper aims to address that question directly. I set out some central distinctions and
stances on prediction: between testing and forecasting predictive contexts; between
temporal and epistemic notions of prediction; between prediction claims and activities;
and between predictive goodness and truth. I go on to argue that there is no readymade theory of prediction in terms of extrapolation, laws of nature, causation, or
mechanisms, because there are circumstances in which each produces bad predictions. I
review some psychological evidence concerning cognitive styles associated with
predictive accuracy; the better style seems to be “fox-like”, as opposed to “hedgehoglike”. Finally I sketch a philosophical theory of good prediction: a good predictive
activity secures warrant for its claim by identifying and verifying currently observable
consequences of the prediction claim it supports, where “currently observable
consequences” need to meet certain specified conditions. This account also elucidates
the plagued question of how exactly prediction and explanation are related.
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I.
Alex Broadbent
abbroadbent@uj.ac.za
PREDICTION AS A NEGLECTED TOPIC
Philosophers have given remarkably little thought to prediction, and almost none to
prediction as a philosophical topic in its own right. The lack is particularly striking in
the philosophy of science, where one might expect prediction to be a central topic. Yet it
is not. Undergraduate courses cover confirmation, explanation, laws of nature, and
causation; but the only questions commonly asked about prediction are whether “novel
predictions” have special confirmatory significance, and whether prediction stands in a
close logical relation to explanation. The question, “What is a good prediction?” does not
range alongside “What is a good explanation?”, “What is causation?” and the rest. There
are no canonical theories of prediction – neither descriptive theories of what in it fact is,
nor prescriptive theories of what it must be like to be good. No great philosopher has
staked out a view of the topic. Carl Hempel studied the logic of confirmation and of
explanation (C. G. Hempel 1945a; C. G. Hempel 1945b; C. Hempel and Oppenheim 1948),
but he did not write “Studies in the Logic of Prediction”. Prediction is discussed in both
the aforementioned studies, but again only incidentally, in relation to the central topic.
Given this picture of philosophical neglect, it is an easy matter to read much of what
philosophers of science have published on prediction in the last hundred years or so. To
summarise that scarce literature, philosophers have discussed:

whether prediction is a goal of science (e.g. Whitmore 1942; Salmon 1981;
Douglas 2009);

how predictions are related to explanations, especially when the latter are
construed as essentially deductive in character (e.g. C. Hempel and Oppenheim
1948; Scheffler 1957; Rescher 1958; Canfield and Lehrer 1961; Rescher 1963;
Kim 1964; Suchting 1967; Hanna 1969; Douglas 2009);
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
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how prediction is related to confirmation (e.g. C. G. Hempel 1945a; Matthew
1971; Merrill 1979; Douglas 2009);

further, whether predictions offer better evidence for or against theories than
empirical claims of non-predictive kinds (e.g. C. G. Hempel 1945a; Maher 1988;
Maher 1993; White 2003; Lipton 2005; Douglas 2009);

whether “novel predictions” offer evidence for scientific realism, or can be
accommodated by various anti-realisms (e.g. Van Fraassen 1980; Laudan 1981;
Lawson 1985; Lipton 2004; Lipton 2005).
Philosophers have not seriously asked how predictions are generated, or how we
distinguish, or ought to distinguish, good from bad predictions. Sometimes it seems that
they have assumed that predictions are a deductive fall-out of theorising, and are thus
generated more or less automatically. It remains common to see it assumed that
universal generalisations employing empirical predicates – such as “All ravens are
black” – have predictive import, such as “The raven I see at the raven park tomorrow
will be black.” First Pierre Duhem and later W.V.O. Quine clearly showed that this
assumption is false (Duhem 1914 Pt II Ch IV; Quine 1953): merely framing a universal
generalisation using empirical predicates does not guarantee predictive import; often,
perhaps always, auxiliary hypotheses are needed before there is an entailment.
Alternatively, philosophers may be silent on prediction because they see it as a
psychological matter, like the psychological process of theory generation, and thus an
empirical mystery about which they, as philosophers, need not say anything. This is not
a stable attitude. The relation of theory to empirical evidence and to superempirical
virtures is the bread and butter of philosophers of science. We do not feel shy about
saying what makes a good scientific theory, notwithstanding our lack of insight into the
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spark of genius that produced it. Likewise, we should not feel shy about saying what
makes a good prediction, notwithstanding our lack of psychological insight into the
psychological processes of predicting. And anyway, these processes are not as
mysterious as all that: we will take a cursory glance at some psychological evidence in
Section IV19 below.
There are thus no good excuses for ignoring the philosophical sense of the question,
“What is a good prediction?” Three points of preamble are in order before proceeding to
an answer. First, I am not denying that prediction is discussed in many disciplines
outside philosophy: economics, epidemiology, climate science, and so forth. None of
these disciplines seek to arrive at necessary and sufficient conditions for a prediction to
be good, however. Rather they seek practical, usually domain-specific methods for
making and improving predictions. Just as paint manufacturers do not struggle with the
question “What is colour?”, sciences engaged in forecasting do not struggle with the
question “What is a good prediction?” in the general sense that it is intended here. There
is no easy answer outside philosophy, just as there is no easy answers can be had to
“What is explanation?” or “What is causation?” by pointing out that many scientific
disciplines make use of these notions.
Second, there are treatments of prediction by philosophers that are not philosophical in
the sense I am seeking. Chief among them is the work of Peter Spirtes, Clark Glymour
and Richard Scheines. In the context of causation, they distinguish philosophical from
mathematical approaches. Philosophical approaches seek necessary and sufficient
conditions; mathematical approaches “seek to provide axioms that use the notion of
causation without defining it, and to investigate the necessary consequences of those
assumptions” (Spirtes, Glymour, and Scheines 2000, 3). These authors also discuss
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prediction at length, but their approach is explicitly not philosophical in the sense of
providing necessary and sufficient conditions. Similar remarks apply to philosophicallyinformed work by Judea Pearl and colleagues (Halpern and Pearl 2005; Pearl 2009).
Thus although they can justifiably claim to have treated the question of this paper, they
explicitly set aside the project of answering it in the traditional philosophical fashion,
which they regard as having track record distinctly inferior to that of the mathematical
approach (Spirtes, Glymour, and Scheines 2000, 3). More power to them; but I want to
have a go at the traditional kind of answer.
Third, let me acknowledge those philosophical treatments that do take prediction
seriously. Nancy Cartwright’s recent work is directly concerned with prediction (and
indeed lit the fuse for the present paper). But she does not seek to offer a general
answer to the question of this paper, so much as identify false assumptions between
predictive activities in policy contexts (e.g. Cartwright 2010; Cartwright 2011). Another
interesting treatment of prediction, coming closer to directly addressing the general
question posed in my title, is Heather Douglas’s (2009). Before setting off into the
wilderness, let us consider whether it answers our question.
Douglas starts by making the same point I am making in this section:
Despite the fact that most philosophers acknowledge the general importance of prediction
for science, the vast majority of the intellectual focus between the two goals rests on
explanation. Prediction is rarely a topic in its own right, appearing mainly in discussions of
confirmation, realism, and other topics. It has been this way for over 40 years. (Douglas
2009, 445)
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Welcome as this sentiment is from my perspective, Douglas goes on to treat prediction
as if its main value were derivative on a general understanding of the world:
The value of predictions has always been clear: predictions help us check whether our
accounts of the world have any veracity and, once proven reliable, help us make better
decisions about how to proceed in our lives. (Douglas 2009, 453)
Notice that she does not say that the value of predictions is that they tell us what is
going to happen. It is rather that they provide a vehicle for testing our “accounts of the
world”. This is a convoluted, though philosophically common, way to understand the
point of prediction, which for the common person is primarily useful for what it can tell
us about the object of prediction, namely, the future, and only secondarily for what it
can tell us about the tools used to make the prediction.1
Douglas’s conception of the usefulness of understanding prediction (as opposed to the
usefulness of prediction) is likewise derivative:
The overemphasis on explanation, and excessive distancing between explanation and
prediction, has led us astray. If we reconsider the possibilities of a relationship between the
two, particularly beyond the straitjacket of a purely logical relationship, we can get a
deeper answer concerning why explanations are important, thus answering more
thoroughly Salmon’s query, “Why ask ‘why?’?” [sic] We can also gain insight into which
explanations should be taken more seriously in science and which less so. (Douglas 2009,
445)
Throughout the paper, and especially in the concluding paragraphs, it is clear that a
theory of explanation rather than prediction is being presented. Prediction does occupy
The paper contains several further remarks bearing this interpretation out (Douglas 2009, 454, 455,
457).
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an important role in the picture, since explanation is presented as, roughly, a tool for
generating predictions that may be used to test theories. But the manner in which
explanations generate predictions is not the subject of close attention, and the question
“What makes a prediction good for testing a theory?” is not broached. The title confirms
that explanation is the beneficiary of the discussion: “Reintroducing Prediction to
Explanation”.
I single Douglas’s (very useful) paper out because it is the closest I have found to an
explicit contemporary treatment of prediction. Closest, but still not very close: it does
not present a theory of prediction. Douglas does not treat prediction as a philosophical
topic in its own right, apart from its links to explanation, notwithstanding her apparent
awareness of the possibility and lack of such a treatment.
I hope that the remainder of this essay will dispel the idea that there is little
philosophical interest in prediction. The next section draws four basic, important
distinctions concerning prediction. Section III considers philosophical resources that
might offer ready-made philosophical models of prediction, and finds them all wanting.
Section IV considers empirical evidence concerning the nature of good prediction,
drawn Philip Tetlock’s large psychological study on predictive reliability of putative
political experts. In Section V I offer a theory of good prediction.
II.
DISTINCTIONS
There are several important distinctions to be drawn concerning different ways one
might think about prediction, or alternatively, between different kinds of prediction.
Making these distinctions helps us to avoid verbal disputes about whether the word
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“prediction” is correctly used one way or another. It also helps us see in outline what a
theory of good prediction might be like.
First, we can distinguish between two contexts in which predictions are made: the
context of testing and the context of forecasting. A prediction made in the context of
testing is made for the purpose of testing some hypothesis that was used to generate the
prediction. It may not be a serious claim about the future (or any other time); it may be
entertained entirely hypothetically. A prediction made in the context of forecasting, on
the other hand, is a genuine effort to say what is going to happen. Philosophers of
science naturally assume the context of testing: Douglas, as we have seen, expresses the
practical importance of prediction in terms of what predictions can tell us about our
theories. Epidemiologists and economists, on the other hand, naturally assume the
context of forecasting, and would express the importance of prediction in terms of what
it can tell us, not about the theories that generated the prediction, but about the future.
Second, we can distinguish between temporal and epistemic predictions (or temporal
and epistemic senses of “prediction”; it makes no difference). A temporal prediction
entails a claim about the future. Thus Israel Scheffler takes it as obvious that predictions
are bound to temporal locations, and thus cannot be propositions:
…note that ‘is a prediction’ is not properly applicable to abstract sentences or
propositions, since the same sentence ‘It rains on May 8, 1952’ is or is not a prediction
depending on the temporal circumstances of its utterance. (Scheffler 1957, 295)
This line of reasoning relies on the implicit premise that a prediction is about the future
of the time of utterance.
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On the other hand, an epistemic prediction need not entail a claim about the future, but
rather entails a claim that (i) falls short of knowledge, and (ii) does so because the truth
of the claim has not yet been checked by any more direct means than the means by
which it was arrived at in the course of making the prediction.
Thus Heather Douglas writes:
I… will take prediction to be indexed to the predictor’s epistemological state rather than
temporal location. Thus, a claim that we should find a piece of evidence in a particular
context is a prediction, even if that context occurred in the past and the evidence is merely
preserved and awaiting discovery. Indeed, if someone claims that some event should have
taken place, but they do not know that it did, and someone else already does, that should
still count as a prediction. (Douglas 2009, 446)
She points out that Peter Lipton takes a similar stance (Lipton 2004, 173). Given the
role prediction plays in debates about scientific realism, it is probably reasonable to
surmise – though tricky to make a tight case – that this epistemic way of thinking about
prediction is more common than the temporal, among philosophers of science.
I am sceptical that the epistemic notion of prediction can be satisfactorily defined, or
that it represents a stable or epistemologically significant category. I am suspicious of
the implied distinction between directly accessing a fact and merely predicting it. I am
also suspicious of this sort of philosophical reconstruction of everyday language and
thought. Predictions seem paradigmatically to concern the future. The philosophical
reconstruction asks us to lightly set this seeming aside. I need more argument than this
before I will be persuaded to abandon the idea that prediction has something important
to do with the future, and thus to abandon any serious philosophical investigation of
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that idea. Note, finally, that the epistemic notion of prediction makes it hard to count
predictions as knowledge, since prediction is classified as an epistemic state short of
knowledge. Whereas on the temporal notion, it seems natural to say that predictions
may sometimes be known. They are just propositions about the future, and qualify as
knowledge provided they are warranted in whatever way known propositions must be
warranted. The epistemic stance’s bar on saying predictions can ever count as
knowledge seems to me a strong reason for allowing temporal predictions alongside
epistemic, since it seems that sometimes temporally predictive claims can be warranted
well enough to be known (e.g. concerning the date and duration of the next solar
eclipse), while temporally non-predictive claims can (obviously) be insecure (e.g.
concerning the outcome of a football match that has already happened but whose score
you have not yet heard).
However, I do not need to pursue these doubts, nor to engage in unpromising verbal
disputes about whether someone who makes an inference “that some event should have
taken place, but they do not know that it did” (Douglas 2009, 446) is really predicting,
or making an inference that does not deserve that name. I need only disagree with
Douglas and Lipton to the extent that they claim priority or exclusivity for the epistemic
notion, and since no argument is offered for such a priority claim, we can safely
disregard it. There may be two notions of prediction, epistemic and temporal. My
interest is in the temporal notion of prediction, which I take it is clear enough to survive
either alongside the epistemic notion, or after a rejection of the epistemic/temporal
distinction.
Third, prediction is similar to explanation in that it suffers from a product/process
ambiguity (Ruben 1993, 16). “Prediction” is ambiguous between a claim, that is, a
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propositional entity like “It will rain tomorrow”, and an activity, such as consulting one’s
barometer in an effort to discover whether it will rain tomorrow (Broadbent 2013, 86).
Fourth, we can distinguish two ways of thinking about predictive goodness,
corresponding loosely to the claim/activity distinction. One might think of good
predictions as settled by fact, by which I mean that all and only true predictions are good
predictions. This is a natural way – though not, I will suggest, a helpful way – to think
about prediction claims. It has no applicability to prediction activities, however, which
are not propositional and do not take truth values. Thus there must be another way of
thinking about predictive goodness, as not settled by fact.
Together, these four distinctions allow us to structure our approach to the central
question. We can say that a good prediction claim is one that is (a) true and (b) based on
a good prediction activity. To endorse the factive notion of prediction claims is akin to
saying that knowledge is true belief; lucky guesses are ruled in, and reasonable errors
are ruled out.2 We can then ask what the appropriate “basing” relation is, and – more
importantly – what makes a good prediction activity. We can confine our attention to
temporal predictions that are made in the context of forecasting. Having arrived at a
satisfactory treatment, we may want to extend it to deal with, or explain, the epistemic
notion of prediction, and predictions in the context of testing.
The first order of business, then, is to understand better what makes a prediction
activity good. The basing relation, the goodness of prediction claims, and the possibility
of extending the treatment to epistemic predictions and testing predictions will all
This denial of the factive notion of prediction is central to my motivation for working on this topic. I
want to escape the “winner takes all” attitude to predictions that dominates public debates about climate
change, the economy, and other matters of great and general concern.
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depend on the nature of good prediction activity. The nature of good prediction activity
will therefore be my focus in the remainder of this piece.
III. APPROACHES TO PREDICTION
In this section I review the most obvious conceptual tools that might be used to
generate a philosophical theory of prediction, and find them all wanting.
A.
EXTRAPOLATION
I take it as axiomatic that predicting future events in the empirical world necessarily
relies on inductive inference.3 Either the prediction activity itself will be inductive, or if
it is deductive then its premises are inductively supported. The simplest way for
induction to feature is for predictions to amount to simple enumerative inductions from
similar past cases: in a word, extrapolation (see e.g. Szklo and Nieto 2007, 376).
The difficulty with extrapolation as a model of good prediction is that extrapolations are
often poor predictions. Share prices, for example, go down as well as up. Just because a
price went up yesterday does not mean it will go up today.
The natural retort is that a good extrapolation will consider the whole pattern to be
extrapolated, not just a segment of it. Yesterday’s trend on a particular share price, to
pursue that example, is a poor basis for extrapolation because that is not the complete
body of data from which to extrapolate. There are price cycles lasting longer than a day.
I use “induction” to cover all kinds of non-deductive inference. Some authors employ a narrower sense
of “induction”, closer to Russell’s enumerative formulation of a Principle of Induction (Russell 1912 Ch 6),
and treat “abduction” or “inference to the best explanation” separately. But given the difficulty of even
describing and hence distinguishing non-deductive inferences with any degree of rigour (C. G. Hempel
1945a; Goodman 1983 Ch 3), I use the term “induction” to refer to all non-deductive inferences. One
might reasonably ask what makes them inferences; that is tantamount to asking what induction is.
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At this point we must distinguish between two ways one might take the question “How
good is this prediction?” The first way of taking the question we might call the internal
sense. Supposing for sake of illustration that we confine ourselves to linear
relationships in bivariate data, the internal question is answered well by the linear
correlation coefficient r, which tells us how strong a given linear relationship. The closer
r is to 1 or -1, the stronger the positive or negative correlation, and thus the better any
given prediction of an unobserved value of one variable based on the value of the other
is likely to be.
The latter claim is no logical truth. It relies on the assumption that the linear
relationship in the observed data holds also for the unobserved data: that the pattern in
the observed data represents the pattern in all the data, observed and unobserved. The
second, external sense of the question asks whether this is true.
Generalising, we can ask either the internal or the external question of a given method.
We can ask how good a given prediction using that method is likely to be, given the
assumptions necessary for the method to work (the internal question); or we can ask
whether the circumstances are propitious for the method to work (the external
question).
In the case of extrapolation, “Expect more of the same” really means “expect more of the
same in the same circumstances”. Bertrand Russell ably demonstrated the point by
imagining a chicken who wrongly extrapolated from past experience that the
approaching farmer would feed it, only to have its neck wrung (Russell 1912 Ch 6). In
some circumstances, the exact opposite of an extrapolation is warranted.
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What this shows is that the notion of extrapolation is on its own not sufficient to ground
good prediction. One is right to extrapolate when circumstances are right for one to
extrapolate, as well as what to do when they are not. A good prediction activity tells us
what these circumstances are, and extrapolation does not. A good prediction activity
may employ extrapolation, but it will answer the external question as to whether
circumstances are right to do so.
B.
PREDICTION FROM LAWS OF NATURE
Perhaps a good prediction claim is one derived from a law of nature, with the “deriving”
being the good prediction activity. This would yield a close symmetry with the
deductive-nomological (DN) theory of explanation, and this symmetry has received
considerable discussion (e.g. C. Hempel and Oppenheim 1948; Scheffler 1957; Rescher
1958; Canfield and Lehrer 1961; Rescher 1963; Kim 1964; Suchting 1967; Hanna 1969;
Douglas 2009). Is there a viable DN theory of prediction?
It is hard to dispute the claim that laws of nature may be useful for making predictions.
If one wishes to predict the next solar eclipse, DN prediction is likely to work very well.
But the solar system is a special case, as perceptive commentators have noted
(Anscombe 1971), since it is a system that proceeds in an orderly fashion with
remarkably little intervention from external factors within human timeframes.
The central difficulty with DN prediction is that it cannot handle intervention by
external factors, of the kind that are largely absent from the progress of the solar system
over the timeframe of a prediction of the next solar eclipse. All known scientific laws
only hold ceteris paribus – “other things being equal”. As for extrapolation, the question
then becomes whether other things are equal. The external question for a prediction
relying on laws is whether the ceteris paribus clause is satisfied, and whether the initial
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conditions are as one supposes them to be.4 DN prediction does not suffice for good
prediction, because the mere fact that a claim is deduced from laws plus initial
conditions does not suffice to show that the ceteris paribus clause for that law is
satisfied. Even when predicting an eclipse, the goodness of a prediction invoking laws
depends upon our background knowledge that intrusions into the Solar System are
highly unlikely, as well as on assumptions about the negligibility of inevitable
measurement errors concerning the initial positions of the planets.
These assumptions might be perfectly legitimate. Again, I am not disputing that
deduction from natural law is a reasonable way to predict solar eclipses. Rather, I am
disputing the idea that deduction from law provides the basis of a theory of good
prediction. A theory of good prediction must tell us why this is a good way of predicting
eclipses. The contemplated answer is, “Because it uses laws of nature.” That is a bad
answer because it does not distinguish this case from other circumstances, such as
predicting the weather, where deduction from laws of nature is a bad prediction
activity. There is thus more to good prediction than deduction from natural law.
C.
PREDICTION FROM CAUSES
It is commonly supposed among those who think about public policy that knowledge of
causes is necessary for reliably predicting the outcome of policy interventions (see e.g.
Rutter 2007). Causal knowledge can protect us from being led into error when mere
extrapolation goes wrong. If, for example, we know that a program to educate mothers
about infant nutrition worked in part because mothers in that region control food
purchases and distribution within the household, then we will not mistakenly predict
If you are for some reason allergic to ceteris paribus clauses, or if you believe there are non-ceterisparibus laws, then the external question becomes whether all the initial conditions are as the prediction
supposes them to be.
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that it will work in a region where husbands do the shopping and mothers-in-law head
the household (Cartwright 2010). Perhaps there are classes of predictions that may
count as good despite a lack of causal knowledge (I invite the reader to identify some),
but at least as practical a rule of thumb, it seems safe to exclude as good those
predictions where the causes of the predicted phenomena either are not known or do
not form part of the justification for the prediction.
However, causal knowledge is clearly not sufficient for prediction, and as such we
cannot rest with a simple causal model of good prediction. There are some thorny issues
here concerning the distinction between general and particular causal claims. If we
know the truth of a particular causal claim then (standardly) that implies the
occurrence of the effect in question. But this is not the case in the context of forecasting.
When forecasting, we usually know that a given event can cause a given effect. But we
may not know that it will cause that effect on this occasion. The same event can be a
cause of both E and not-E. For example, if housing prices crash tomorrow, the rate of
inflation will likely be among the causes. But if housing prices rise tomorrow, the rate of
inflation will also be among the causes. It contributes in either case. If there is a storm
tomorrow, the air pressure is among the causes; and likewise if there is not. If you speak
your mind in a meeting, the presence of oxygen is among the causes; and likewise if you
remain silent. Many causes of actual effects are like this: they could also have been
among causes of other events that did not in fact occur.5 From the point of view of
predicting, this means that merely because a given event can cause something does not
mean that it will on a particular occasion, and therefore that some warrant is required
This is analogous to the situation with causal explanation. To be at all useful, an explanation must cite
not merely any cause, but a cause that is a difference between the causal history of the explanandum and
the causal history of its non-occurrence. This point is discussed further below in Section V.
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for the inference that a cause that is capable of having a given effect will in fact have that
effect.6
We need to know how to move from “can cause” to “will cause”. This move will depend
on knowledge of other causal circumstances. Whether oxygen causes silence or speech
depends on what else is going on. We might say that causal knowledge is sufficient for
prediction when the knowledge is of a sufficient cause; but that is a bad move, because
we probably never have knowledge of sufficient causes in their entirety – such
knowledge would need to exclude every possible intervener, including meteorites from
out of space, solar flares, highly unlikely quantum events, and so forth. If good
prediction requires knowledge of that sort then we can never achieve it. Surely the
difficulty of knowing these things does have something to do with the difficulty of
prediction. To invoke sufficient causes is tantamount to saying that warrant for using
causes to predict only comes when one knows all the circumstances are as they need to
be. Perhaps; but that would be depressing if so. If we want to draw a useful line between
good and bad predictions, we need a standard that can be humanly satisfied, and so
should reject those standards that clearly would yield true predictions but are equally
clearly unattainable by humans, just as we reject those standards that would wrongly
judge bad predictions to be good.
D.
PREDICTION FROM MECHANISM
Much, probably too much, has been made of the notion of mechanism in recent
philosophy of science. Perhaps prediction requires knowledge of an underlying
There are theories of causation, including my own, on which the foregoing is not true. Elsewhere I have
argued that a radically “unselective” (Lewis 1973, 559) or “egalitarian” (Hall 2004, 112) notion of
causation does not do justice to the use we make of causal knowledge in predictive and other contexts
[author refs]. Jonathan Schaffer’s contrastive theory of causation likewise gives resources to deny some of
the causal claims above (Schaffer 2005, 342–6; Schaffer 2010; see also remarks on selection in Schaffer
2007). But for sake of argument I shall in this section adopt the widely-held unselective or egalitarian
posture that anything featuring in the causal history of an event is equally a cause of that event.
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mechanism. Such knowledge is presumably a kind of causal knowledge, but with bells
and whistles: one does not simply know that A causes B; one has a detailed picture of
how A causes B. It is the difference between knowing that winding your watch causes
the hands to go round, and understanding how winding causes the hands to go round –
understanding the watch mechanism. And indeed it is plausible that someone who
understands the watch mechanism will make better predictions about the watch than
someone who merely knows that winding causes ticking.
Knowledge of mechanisms, as a kind of detailed causal knowledge, is surely useful for
predicting. But it is neither necessary nor sufficient, as I have argued elsewhere
[author’s ref]. Prof Barometer knows how his barometer works but is ignorant of the
fact that air pressure falls with altitude, due to his cloistered life at a university located
in a low-lying English swamp. His predictions about the weather go wrong when he
visits his old school friend Joe User, who lives in a hilly region. Joe is ignorant about the
workings of barometers but knows from experience that changes in altitude affect their
reliability; he is safe from a set of errors that Prof Barometer is not, and is in fact a more
reliable predictor using his barometer. Prof Barometer’s knowledge of the mechanism is
not sufficient for good prediction. And Joe User’s ignorance of the mechanism does not
prevent him from making good predictions, showing that knowledge of the mechanism
is not necessary for good prediction.
Again, circumstances matter. One can predict using mechanistic knowledge, provided
that the circumstances are right for the mechanism to work. In different circumstances
the same mechanistic knowledge might yield quite different predictions. Our question,
“How good is this prediction?”, taken in the external sense, asks what warrant there is
for supposing that the circumstances are right for the mechanism to work and not
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break. Although further mechanistic knowledge may be invoked in establishing this
warrant, the mere fact that a mechanism is used to make a prediction obviously does
not in itself establish that circumstances were right for its use. Thus our theory of good
prediction cannot start from the point that a good prediction is one that employs
mechanistic knowledge, since bad predictions also can.
IV. THE PSYCHOLOGY OF GOOD PREDICTION
The psychologist Philip Tetlock has conducted a long term study of the predictive
accuracy of putative experts in the social and political sciences (Tetlock 2005).
“Putative” because their accuracy is poor enough to cast doubt on their expert status in
many cases. Predicting political events such as the fall of the Soviet Union may be
different from predicting natural events such as tomorrow’s weather. But it would be
foolish to ignore this evidence merely on that basis.
Tetlock posed “large numbers of experts large numbers of questions about large
numbers of cases” and applied “no-favoritism scoring rules to the answers” (Tetlock
2005, 8). The results are sobering:
When we pit experts against minimalist performance benchmarks – dilettantes, dartthrowing chimps, and assorted extrapolation algorithms – we find few signs that expertise
translates into greater ability to make either “well-calibrated” or “discriminating”
forecasts. (Tetlock 2005, 20)
Perhaps even more worryingly, professional background is not significantly correlated
with forecasting accuracy, including whether or not the expert has a doctorate, years of
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professional experience, and whether the expert has access to classified information
(Tetlock 2005, 69).
However, Tetlock does not recommend general scepticism about human predictive
ability. He argues that certain cognitive styles perform noticeably better than others:
they are correlated with forecasting accuracy. Tetlock takes over Isiah Berlin’s
distinction between thinkers who are hedgehogs and thinkers who are foxes (Berlin
2013). Hedgehogs know one big thing: they have an overarching idea or intellectual tool
which they use to explain and to predict everything. They tend to be intellectually
aggressive, making bold claims, and resisting altering those claims when evidence
comes to light suggesting that they might be wrong. They tend to be confident of their
opinions, including predictive ones, presumably because they are convinced of the truth
of the big idea that generates them. Foxes, on the other hand, use a range of cognitive
tools, respond to changing evidential situations, qualify their predictions, and are less
certain of them. Tetlock argues that foxy thinking encourages ideological moderation,
while hedgehog-ish thinking lends itself to extremism (Tetlock 2005, 86).
Elsewhere I have advocated a simple heuristic for prediction by asking the question,
“What could possibly go wrong?” I have not done Tetlock’s study justice in this
summary, but it nevertheless provides some empirical plausibility that thinkers who
follow this rule of thumb are better predictors than those who do not. This suggests that
it would not be entirely hedgehog-ish to seek to develop and generalise an account of
prediction along lines which I have gestured at elsewhere [author’s ref].
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V.
Alex Broadbent
abbroadbent@uj.ac.za
A THEORY OF GOOD PREDICTION
The trouble with seeking a model of prediction in terms of any of the concepts identified
in Section III is not that they are useless for making predictions. On the contrary,
without appealing to one or other of these notions, it is hard to see how prediction could
amount to more than guesswork. Taken together, the review of conceptual resources in
Section III and psychological evidence in Section IV suggests that a good prediction
activity is not so much a matter of which method you pick, but of ensuring that
circumstances are right for your chosen method to work.
Thus the task of coming up with a philosophical theory of prediction is narrowed to the
slightly less daunting task of coming up with an account of the rightness of
circumstances for a predictive method to be employed. To make the task less daunting
still, I want to draw an analogy between prediction and the standard realist picture of
the way confirmation works for claims about unobservables. (This analogy does not
commit us to scientific realism, only to realism about the future, which I take to be
acceptable to the empiricist and realist alike.) The hope is that the way realists think we
can get knowledge of unobservables provides a good starting point for explaining how
we might get knowledge of the future.
Suppose a scientist entertains some claim T whose truth is not readily observable
(whatever that exactly means). T might be a definite hypothesis, for example, that there
are subatomic particles, known as electrons. These particles cannot be seen, nor
detected with any existing instrument. How does the scientist test this theory and
gather confirmatory or disconfirmatory evidence? On a standard realist view, she works
out what observable differences there are between her hypothesis and various
incompatible alternative hypotheses, and then she makes observations concerning
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those differences. Her theory is confirmed if the observable differences are in favour of
her theory, and disconfirmed otherwise.
In this example, one aspect of electron theory is that charge is quantized, arising (on
electron theory) from the distribution of charged subatomic particles. If it is possible to
measure the charge on an electron, then one will have evidence that tells between the
hypothesis that charge is quantized and that it is continuous, and so between the
hypothesis that there are subatomic particles, including electrons, and that there are
not. Thus when Robert Millikan experimentally measured the charge on the electron, he
provided evidence that simultaneously confirmed one hypothesis and disconfirmed
another. Millikan’s experiment would have been of much less interest if its results had
been equally compatible with either theory.
Let me stress two features of this picture. First, not just any incompatible hypothesis is
countenanced as providing a useful foil for the theory under test. The foil must have
something going for it, in order for beating that hypothesis to count in favour of the
theoretical claim in question. One does not (as in a cartoon I have seen) strap puppies to
missiles on the basis that good science requires testing our assumptions about the likely
outcome. This is not because we have a wealth of data about puppies that have been
fired off with missiles in the past; it is rather because there is no serious rival to the
obvious account of their fate. Impressive confirmatory evidence comes from tests that
distinguish a theory from serious rivals, not from just any contrary empirical claim.
Second, contrasts play an important role. This way (and maybe there are others) of
obtaining evidence about unobservables seems to depend minimally upon identifying
differences between how things would be if the theory is true, and how things would be
if the theory were false. Note in particular that “contrast” does not just refer to the
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negation of the hypothesis under test. Substantive contrasts, i.e. contrasts between T
and something logically stronger than not-T, are important because the mere negation
of a hypothesis about unobservables does not necessarily imply any observable
difference. Denying that there electrons does not, by itself, yield the consequence that
charge is not quantized. To generate a rival observable claim, one needs a rival theory
that is logically strong enough for the job. Merely negating a theory about
unobservables does not usually yield any observable consequences at all.
The two points are connected because the negation of a serious hypothesis is not
necessarily, and indeed not usually, a serious rival. We don’t strap puppies to missiles to
test the hypothesis that they will be blown to smithereens against its mere negation,
since the mere negation is not a serious rival hypothesis, even though the hypothesis it
negates is highly credible. The point is reminiscent of Quine’s claim that the
complement of a projectible predicate is not necessarily projectible (Quine 1969, 115).
I want to propose a theory of good prediction activity that is analogous to the manner in
which claims about unobservables are, in the realist’s eyes, confirmed by observations.
A good prediction activity is one that (a) identifies and (b) verifies currently observable
consequences of the prediction claim it supports.
“Currently observable consequences” satisfy two conditions. First, the predictor is able
to observe them before the date that the prediction concerns. They are analogous to the
“observable consequences” of unobservable claims, although clearly any causal
implication in “consequence” will need to be rethought for the case of prediction, where
the matters that one infers lie in the future of one’s evidence. We will discuss this
shortly.
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Second, currently observable consequences of the truth of the prediction are also
differences from currently observable consequences of some foil that is contrary to the
prediction. The two remarks concerning contrastive inference apply as follows. First,
defeating the foil confers support on the prediction only to the extent that the foil enjoys
support of its own. Second, some substantive, contentful foil, and not merely the denial
of the prediction claim, is necessary in order to be able to generate the requisite
currently observable consequences against which to test the prediction claim.
Confusion might arise at this stage because the analogy with theoretical claims about
unobservables suggests that it is the prediction claim that is being tested, whereas I
have so far talked about checking that circumstances are right for the method that was
employed to produce the prediction. The confusion is resolved by seeing inappropriate
circumstances for a given method as one way that a predictive claim might be cast into
doubt. The truth of the prediction claim is the ultimate goal, so it makes sense to focus
on testing the claim against rival ways things might go. In my critique, I have focused in
particular on the failure of putative methods for arriving at a prediction, because I have
been concerned to refute the idea that employing any of these methods is sufficient for
good prediction. But there is no reason to confine our assessment of prediction claims
to an assessment of the methods used to produce them.
The analogy between predictions and confirmation of theories about unobservables
gets us only so far. It breaks down when we consider the causal structures underlying
this kind of inference in each case. The realist picture of theoretical confirmation is
often cast as inference to the best explanation. The theory T explains experimental
results better than rival theories, and, according to the proponent of inference to the
best explanation, is therefore not merely preferable to those other theories, but likely to
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be true, if the rival theories are good enough (Lipton 2004 esp. Chs 3-5). But future
events cannot explain past ones, so we cannot cast predictive inference as inference to
the best explanation, without further elaboration.
In the case of prediction, explanation is still involved, but the explanans is not the
prediction claim. On the model I have sketched, the business of making a prediction
good is one of explaining why it rather than alternatives is true. The alternatives are
those predictive claims entailing different currently observable consequences. In
verifying the currently observable consequences, one simultaneously rules out these
rival theories that contradict the prediction claim you are supporting. You are thus
giving causal differences between the situation that will obtain if the prediction is true,
and that which will obtain if various alternatives are true. In other words, you are
supplying a contrastive causal explanation of the contrast between the event predicted
and some foil.
This model resolves a longstanding confusion about the relation between prediction
and explanation. On the one hand, there clearly seems to be a link. Douglas argues that
explanation provides cognitive tools for making predictions (Douglas 2009). The theory
proposed here is consonant with that view and puts some flesh on the bones, by
showing how explanation is involved in securing the warrant for a prediction. On the
other hand, counterexamples to the claim that every prediction is an explanation
abound. Some of these are handled by the distinction between prediction claims and
prediction activities. Merely making a claim about the future clearly does not require
explanation. Others are handled by the notion of good prediction. A prediction activity
may not involve explanation, but on this model, a good predictive activity will include a
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series of contrastive explanations as to why the prediction claim, and not any plausible
contrary, is true.
VI. CONCLUSION
In the context of forecasting, a good prediction claim is a true claim about the future
based on a good prediction activity, and a good prediction activity is one which
identifies and verifies observable consequences of the prediction claim it supports. That
is the full statement of the theory of prediction that we have arrived at, and of the
answer to the title question of this paper.
There remain a number of loose ends and unanswered questions. First, I have not
attempted to demarcate “scientific prediction”, in the manner that Hempel sought to
demarcate scientific explanation. Second, although I have sought to connect good
prediction with other epistemic exercises, and with forms of inference that are thought
by some to yield knowledge (I am referring to the analogy with confirmation of claims
about unobservables), I have not squarely addressed the question whether predictions
are ever knowledge of the future. Third, I have not tried to decide whether only an
internalist account can provide a model of good prediction, or whether the references to
checking, explanation, and so forth can be schematized in an externalist fashion. My
hunch is that it can’t be done: that good prediction, as I have characterised it, is
necessarily an active intellectual exercise. But I have not argued this case.
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