Dissertation Finished

Dissertation: PHIL 3020
Student ID: 200 538 571
Academic Year: 2013/14
How do Explanatory Virtues help guide Inference to the Best Explanation in Science?
Abstract:
Inference to the Best Explanation is a popular tool in both the philosophy of science and the
sciences. However, debate as to what characterises the best explanation is thus far
inconclusive; a comprehensive criteria not forthcoming. Through committing to Woodward’s
(2003) ‘counterfactual model of causal explanation,’ this essay will argue for two forms of
explanation, both of which centre upon the explanation’s ability to answer counterfactual
what-if-things-had-been-different questions.
Contents:
I: Introduction.
II: Inference to the Best Explanation.
II.i: Spelling out the Slogan.
II.ii: The Likeliest and the Loveliest.
III: Explanation.
III.i. The Deductive – Nomological Model of Causal Explanation.
III.ii. The Counterfactual Model of Causal Explanation.
IV: Explanatory Virtues.
IV.i: The Aesthetic, Pragmatic, and Theoretic.
IV.ii: Woodward & Hitchcock and Ylikoski & Kuorikoski.
IV.iii: Non - Sensitivity.
IV.iv: Consilience.
V: Conclusion.

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I
This essay will first outline Inference to the Best explanation, with attention given to Lipton’s
(2004) distinction of ‘loveliest’ over ‘likeliest’ as the guide to explanation. Then, it will
attend to defining ‘explanation.’ The essay will outline the deficiencies of Hempel’s (1998)
deductive – nomological model and instead prefer Woodward’s (2003c) counterfactual model
of explanation as a basis for discussion. The essay will then give a brief overview of the
range and nature of virtues proposed; however, discussion will centre on Yliksoki &
Kuorikoski’s (2010) Precision and Non-sensitivity, and Thagard’s (1978) Consilience. These
virtues are chosen for being the most direct promoters of an explanation’s ability to account
for counterfactual considerations – an ability identified in the counterfactual explanatory
model as the important element of explanation. Although Thagard’s consilience is proposed
outside the counterfactual explanatory model, it will be found that, once reshaped to fit the
model, it becomes arguably the most important explanatory virtue.
This essay is not, however, a defence of Inference to the Best Explanation. IBE is a
controversial argumentative tool facing famous objections from philosophers such as van
Fraassen (1989). However, it will be assumed that IBE is successful and a legitimate tool. It
should also be noted that this essay concerns IBE as used in science, not as used more
generally. It may be the case that explanatory virtues in, say, metaphysics are radically
different. Or, they may be the same. However, it is only over science the explanatory virtues
discussed are intended to govern. Their success and failure elsewhere is not considered.
II
II.i.
To infer the best explanation is to select a theory or hypothesis from a given set of theories or
hypotheses which is somehow better than the others in the set. Although IBE can occur in the
conception of an explanation, it takes most prominence in the comparison of explanations.
When multiple explanations are proposed for an event or phenomena, it is through identifying
and selecting the best of these explanations that scientific progress is made. IBE selects a
single hypothesis or theory, discarding all competitors in doing so.
IBE has garnered strong support in recent times. Thagard (1978,1989,2012) , Lipton (2004),
Harman (1965,1968), Glass (2012) amongst others promote the account – some even taking

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its acceptance to be assumed, and instead discussing its application without concern for
justification. However, concerns with IBE have been raised. Most commonly these concerns
take issue with its fallibility; however, these mostly conflate to concerns with abductive or
inductive models generally. By their nature, neither guarantees the reasoner will arrive at
truth, even when adopting a true starting position. However, as Popper’s infamous attempts to
make science a fully deductive practice show, some allowance of non-deductive reasoning is
unavoidable at some level.
However, there are further concerns which are charged specifically at IBE. The most notable
is the ‘best of a bad lot’ problem (van Fraassen, 1989) which argues that when using IBE, if
the correct explanation is not included in the set of possible explanations, to infer the best
explanation from the set will not lead to truth. One response may be to include in the set of
competing hypotheses the hypothesis [None of the others] where the hypothesis merely
describes the event whilst stating that it did not occur according to its competitors. This
would make the set of hypotheses exhaustive, though it is unclear that it would constitute an
explanation. To say why something didn’t happen does not explain how it did happen.
However, as IBE’s legitimacy is assumed, this is not pursued further.
II.ii.
Lipton (2004) identifies two ways an explanation may be regarded as the best: the likeliest
and the loveliest. Rightly, Lipton (2001, p.187) highlights that following the likeliest pushes
IBE towards triviality. If inductive likeliness is a guide to the best explanation, and the best
explanation is simply the most likely, the model becomes circular. Conversely, the loveliest
explanation will be the explanation which gives the greatest understanding. The level of
understanding provided by the loveliest explanation can be seen in the following example:
All the cheese sandwiches at the buffet have gone. Consequently, two competing
explanatory hypotheses are devised. The first: that some person invited to the buffet ate the
sandwiches. The second: that Greedy George (not invited) ate the sandwiches. The first
explanation is the more likely: lots of people were invited, and between them could easily
have eaten several sandwiches. However, we may prefer the second explanation owing to its
revealing greater understanding. Suppose we know that Greedy George particularly likes
cheese, and his whereabouts were unknown prior to the event. Perhaps he has the unfortunate
nickname he does for good reason, and a disregard for obeying rules. These factors reveal a

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greater understanding. Greedy George may not be the most likely, though the explanation
accusing him offers a greater level of understanding.
However, one cannot promote the ‘loveliest’ explanation with such vague notions as
‘understanding.’ It is at this point that the question this essay is concerned with arises. What
characterises the ‘loveliest’ explanation? Lipton remarks that IBE should be stated as
Inference to the Loveliest Explanation; however, he is disappointing non-committal with this.
He only briefly (2004, Ch. 2) outlines several proposals (including reason, unification,
familiarity) giving a short critique of each, but fails to propose his own suggestions. The
overall impression of IBE is given without clear detailing of how it should be applied.
Without this, the account remains fairly directionless: amounting to little more than a slogan.
Consequently, before questions of the ‘loveliest’ explanation can begin to be answered,
attention must turn to explanation itself. The essay the most promising model of causal
explanation will now be identified.
III
III.i.
The most prominent account of explanation emerging in the 20th
century is Hempel’s
Deductive – Nomological model of causal explanation. (1998, pp. 685-694) The model
constructs a deductive argument using initial facts and laws of nature as premises which
explain the event through showing that it must have obtained. It takes the form:
C1, C2, C3... Ck
L1, L2, L3... Lt
E
Jointly, these premises are the ‘explanans’ which consist of particular facts (C) and laws of
nature (L); the conclusion (E) is the ‘explanandum’ (Hempel, 1998, p.686). For a deductive
argument to be sound, the premises must be true therefore no false conditions or laws can be
cited. Hempel places great emphasis on the inclusion of laws – the nomological aspect of the
model. This is owing to their unfailing nature, and therefore must be distinct from
‘accidentally true generalisations.’ (Woodward, 2011) To guarantee the explanation occur

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through a deductive argument, the argument will be strongest containing premises which
cannot be otherwise (are incontestable). Hempel refers to this as ‘nomic-expectancy.’ This
excludes ‘accidental generalisations’ as, although they may actually hold in all instances,
they could fail. Newton-Smith (2001, p.127) captures the intuitive force of the model, stating:
“We feel we understand why something happened if we see that, given certain features in the
situation, it had to happen.”
However, some take issue with the DN model. In particular, it struggles with the
asymmetrical nature of explanation and is too strict in its insistence on laws of nature. As the
model draws no distinction between the nature of the particular facts used in the explanans
and the facts contained in the explanandum, they are interchangeable. This is seen in the well
versed example of a flagpole’s shadow length. (Van Fraassen, 1980, p.104) By the DN
model, a flagpole’s length can, as we would expect, explain the shadow it casts; however, the
shadow can also explain the pole’s length – something we would not expect. The facts can be
swapped between explanans and explanandum and consequently used to explain each other.
The account also suffers from its reliance on laws of nature about which Woodward &
Hitchcock (2003a) raise two concerns. The first (2003a, p.1) being that no detailed definition
exists of ‘law of nature,’ without which their necessary inclusion in explanation is worrying.
Secondly (Woodward & Hitchcock, 2003a p.2), they argue that many sciences do not rely on
laws of nature:
The life sciences and social sciences, as well as branches of the physical sciences such as
meteorology and geology – seem to provide generalisations which are not truly exceptionless,
and which lack many of the other features standardly assigned to laws.
Therefore, as Bartleborth (2002, p.97) suggests, “Hempel’s DN-schema has given us the
wrong impression that explanation is a yes-or-no business.”
III.ii.
However, the DN model shows promise, and can be developed to resolve its problems.
Consequently, Woodward develops the ‘counter-factual account of causal explanation’
(2003), proposing: “explanation is a matter of exhibiting systematic patterns of counterfactual
dependence.” (2003, p.191) For an explanation to be a good explanation it must answer
questions of how the explained phenomena would-have-been-different-were-things-different

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(w-questions) – the explanation will be able to predict the outcome of an intervention. This
model’s structure is similar to the DN model (Woodward & Hitchcock, 2003b, p.182):
X1 = x1, . . . ,(Xn = xn)
Y = g(X1, . . . ,Xn)
Y = y = g(x1, . . . , xn)
Where X is the explanans variables, Y the explanandum variables and the conclusion the
outcome of the counterfactual w-question according to the state of affairs described by the
explanation.
Central to the model are variables, intervention, and invariance. The explanans variables in
the explanation are the ‘particular facts’ from DN which hold a specific value. An
intervention is the act upon one such variable resulting in the value of the variable changing.
A good explanation should include independent variables which could hypothetically be
causally intervened upon which would in turn effect the explanandum variables. Taking the
example of the flagpole shadow length, the flagpole length can be used to explain the length
of the shadow because it could be causally intervened upon to affect the length of the
shadow. The variable under intervention must be “causally independent of and uncorrelated
with [other variables invoked in the explanation]” (Woodward, 2003, p.197). Therefore, the
variable of the flagpole length must be independent of the other variables doing the
explanatory work (angle of light etc.). The independence of the explanans variables avoids
the asymmetry problem. The flagpole’s shadow length is a dependent variable: causally
intervening upon it will not change the length of the flagpole. Therefore, it cannot be used to
explain the flagpole’s length.
The invariance of an explanation is the range over which the explanation will hold under
interventions upon its variables. If an explanation is highly invariant, it will explain the event
over a high degree of counterfactual variances. Unlike the DN model, the explanation does
not have to be exhaustive over the variances – it does not have to be totally invariant.
Invariance is vital to Woodward & Hitchcock’s (2003b) discussion of explanatory depth, and
will be pursued in detail later. The counterfactual explanatory model will be utilised in the
discussion of explanatory virtues.

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IV
There are many different virtues proposed in the literature with many different motivations
for doing so. Thagard (1978, 1989) outlines consilience, analogy, and coherence; Lipton
(2004) suggests familiarity, unification, and coherence among others; Ylikoski & Kuorikoski
(2010) argue for non-sensitivity, cognitive salience and more; and Kitcher (1989) defends
unification as the true explanatory virtue. Consequently, attempting to outline and assess all
these claims is untenable. However, by committing to a specific explanatory model such as
the counterfactual model, the range of virtues is narrowed greatly. This is partially the
motivation for doing so; however, by doing this the remaining virtues are sharpened and
given greater detail. Below, an outline will first be given of the three branches of virtues, the
theoretic, aesthetic, and pragmatic, with only the first branch then pursued. From there, the
essay will concentrate on those virtues most closely related to the counterfactual explanatory
model: Thagard’s consilience, and Ylikoski & Kuoikoski’s precision and non-sensitivity.
Discussion of these virtues will also be supplemented with Woodward & Hitchcock (2003a,
2003b).
IV.i.
Broadly, the virtues align themselves with one of three categories: aesthetic, theoretic, or
pragmatic. Though it is thought that the perfect explanation would fill all three categories, the
categories are given a clear order of preference. This essay will choose not to focus on
aesthetic virtues. Lipton’s (2004) use of the term ‘loveliness’ as characterising explanatory
understanding may insinuate that aesthetic virtues like elegance play a significant role in
explanatory considerations; however, this is misleading. Mackonis (2013) and Barnes (1995)
reject aesthetic virtues. Mackonis (2013, p.979) writes:
... one should argue that aesthetic virtues should not be conflated with explanatory virtues in
the context of IBE ... Explanatory virtues as the sign of explanatory loveliness should provide
understanding rather than aesthetic pleasure.
The appearance of aesthetic virtues in IBE could be considered a misappropriation of
theoretic or pragmatic virtues. Virtues like elegance typically defer to others such as
simplicity of unification. Therefore, aesthetic virtues may not be virtues in their own right,
but rather may be considered as regular consequences of other explanatory virtues. Mackonis

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(2013, p.979) also claims that “[...] aesthetic virtues [are] derivative from some of the
explanatory virtues.”
Mackonis takes issue with aesthetic virtues being ascribed inconsistent traits such as
simplicity and complexity, and symmetry and asymmetry. However, a defender of aesthetic
virtues may complain that this confusion arises in the literature, not the virtues themselves.
For aesthetic virtues to be ascribed inconsistent characteristics suggests strongly a mis-
ascription. However, the complaint of their being derivative from other virtues still stands,
and is enough in its own right to cease in considering aesthetic virtues further.
The theoretic and pragmatic virtues can roughly be characterised as being those of depth and
of conceivability. Mackonis (2013, p.980) describes pragmatic virtues as being a matter of
convenience. Although they are often given status as virtues (Mackonis 2013, Ylikoski &
Kuorikoski 2010, Woodward & Hitchcock 2003b), it is not clear they encompass the same
hierarchal structure as theoretic virtues. I would suggest that the difference lies in an
explanation’s merely having to satisfy a certain level of pragmatic comprehension, meaning
the explanation is judged only in its own right for the pragmatic virtues and not against other
explanations. Therefore, a pragmatic virtue may consist in a minimum requirement rather
than a comparative quality. Furthermore, if the complaint held against aesthetic virtues was
that they defer to the other classes of virtues, and virtues such as simplicity are categorised as
theoretical rather than pragmatic, then the same charge may be brought against the pragmatic
virtues. Either way, discussion will only follow theoretic virtues.
IV.ii.
Once committed to the counterfactual model of causal explanation, one sees clearly what the
explanatory virtues should, largely, aim towards. Whereas previously, explanatory virtues
were suggested and developed without any clear guidance other than intuition, an obvious
requirement of the virtues is revealed. A good explanation will embody the explanatory
virtues; a good explanation will also (according to the counterfactual model) answer a large
range of w-questions. Therefore, explanatory virtues should promote and support the
explanation’s ability to answer w-questions.
Woodard & Hitchcock (2003b) outline several ways in which one explanation can be
considered to be more invariant than another. As invariance directly leads to the answering of

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w-questions, it is an obvious place to start in the search for explanatory virtues. Let G and G’
be two competing explanations; X be the variables in the explanans; and R be the range of
invariance of X. From here, G’ can be judged as a better explanation than G, under several
conditions. They (2003b, p.184, paraphrased) begin with the following condition for
explanation comparison:
Both G and G’ include X; however, whereas G is only invariant over R, G’ is invariant over
R’, which strictly contains R. In this case, G’ is more invariant than G.
This condition is quite obvious; however, it only works for simple cases. If competing
explanations hold all the same explanans variables, and one explanation’s variables hold over
a range which is both greater than and fully encompassing of the other’s, then that first
explanation is deeper/better. As Woodward and Hitchcock (2003b, p.185) concede, this is a
highly idealised case and, in actual application, of limited use. However, it does give a rough
outline of the direction an explanation should be headed. This outline can then be sharpened
with further conditions.
Woodward & Hitchcock go on to detail these conditions (2003b pp.185 – 186). The first
being that G’ is superior to G in the case that G’ has a less disjointed range of invariance to
G. This highlights an explanation’s variables potential for holding several ranges under which
they are invariant. An explanation of an animal’s eating patterns may fail at certain times in
its life; perhaps at certain ages. The explanation may then be disjointed, though is still (at
least partially) explanatory. However, if a competing explanation can account for all ages of
the animal, or fails at fewer intervals, then it is deemed to be more explanatory. There is an
interesting trade-off to be had, as disjointedness can be judged in two ways. An explanation
may be disjointed in having one large gap in its range of invariance, or by having multiple
small gaps. Woodward & Hitchcock do not give this much attention, though their brief quip
(2003b, p.185) concerning the latter type rendering an explanation as being “like the broken
watch that has the dubious virtue of telling exactly the right time twice per day” suggests they
favour the former. One may choose to characterise the absence of disjointedness as the
explanatory virtue non-disjointedness.
Woodward & Hitchcock (2003b, p.184) also suggest the virtue of accuracy; one which
Ylikoski & Kuorikoski (2010, p.210) choose to characterise as ‘precision.’ Precision

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encompasses accuracy but adds also the dimension of scope1
. Accuracy measures the detail
of the explanandum variables. This is, for instance, an explanation of water temperature
reporting 10.4 degrees Celsius rather than 10 degrees Celsius (where the latter is rounded).
Or, as in the Ylikoski & Kuorikoski (2010, p.211), an explanation of why a car is navy blue,
as opposed to a non-specific shade of blue. This follows closely the spirit of Lipton’s
loveliness over likeliness. After all, a car cannot be navy blue without being blue. However,
the latter gives a fuller and thus better explanation.
The second element is the scope of the contrast class of the explanandum variable. This set of
contrasts is the range of alternative values of the explanandum variable discounted. When an
explanation tells us why the car was navy blue, it tells us why it is blue as opposed to some
other colour. The scope is then determined by the size of the set of values discounted: The
larger the set of contrasts, the better the explanation. This can be seen in two competing
explanations which both explain why the car is navy blue. They are equal in respect to the
precision of the explanandum (the car’s actual colour); however, one explanation may
explain why the car is navy blue as opposed to being red, whereas the other may explain why
the car is navy blue as opposed to any other colour. The larger set of contrasts in the latter
explanation determines it to be a better explanation.
A major concern with the characterisation of precision which Ylikoski & Kuorikoski provide
is that, as they (2010, p.211) highlight, the comparison of competing explanation’s precision
becomes very difficult in anything other than simple cases. This owes largely to the virtue’s
comprising of multiple components. One explanation may be very precise in its
explanandum, whereas its competitor may encompass a larger scope. Without any preference
for one over the other given, the virtue is unable to make a comparative assessment. This may
suggest Woodward & Hitchcock’s (2003b, p.184) motivation to only endorse one aspect,
accuracy, of precision. Ultimately, this gives a less descriptive account of the virtue than
given in Ylikoski & Kuorikoski; however, it retains the comparative aspect.
However, both aspects of precision as characterised by Ylikoski & Kuorikoski play an
important role in the answering of w-questions. By being more accurate, an explanation can
answer more, finer grained, w-questions. By encompassing a larger scope, an explanation can
1
Ylikoski & Kuorikoski use the term ‘sharpness’ rather than ‘scope.’

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again answer more w-questions by comparing more contrasts. They are both positive traits.
Therefore, they should be considered separate explanatory virtues. By not conflating the two
under one name, Ylikoski & Kuorikoski’s comparative problems are resolved; they will be
judged separately, and so will not clash. However, there is also no need to choose one over
the other: to lose either is undesirable. Therefore, I suggest that both accuracy and scope
should be kept as individual explanatory virtues.
IV.iii.
Another virtue to be characterised negatively (along with non-disjointedness) is Ylikoski &
Kuorikoski’s Non-sensitivity (2010, p.208); a virtue centring on the robustness of an
explanation2
. The sensitivity of an explanation is the degree to which the explanation is
affected by changes in ‘background factors.’ These background factors are the initial
conditions of the event or phenomena, not background assumptions such as auxiliary
hypotheses. Whereas the invariance in Woodward & Hitchcock concerns itself specifically
with the variables explicitly present in the explanation, non-sensitivity takes on a more
general role, taking on the considerations over all counterfactual variances.
Non-sensitivity clearly promotes the range of w-questions an explanation answers. The larger
the set of background changes the explanation is non-sensitive to, the greater the amount of
w-questions answerable. However, Woodward & Hitchcock (2003b, p.187) stress concerns
over the relevance of the background assumptions being altered. The problem being that:
...any generalization (that is true or approximately so of the system at hand) will be `invariant
under a great many changes in background conditions (such as changes in the price of tea in
China).
A vast amount of background assumptions are made which are simply not relevant to the
explanation, and not causally connected. As a result, changes in these background
assumptions have no effect upon the explanations, and so will all contribute to the non-
sensitivity of the explanation. As a result, virtually all explanations will be considered highly
non-sensitive, pushing the virtue towards triviality. Ylikoski & Kuorikoski (2010, p.209)
attempt to counteract this concern, claiming that non-sensitivity is a context dependent virtue.
2
Weslake (2010, p.278) prefers ‘Robustness,’ which I agree is preferable to non -sensitivity. However, as
Ylikoski & Kuorikoski (2010) opt for ‘non-sensitivity,’ I shall follow non-sensitivity.

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However, this pushes the virtue towards subjectivity, making comparison difficult. Ylikoski
& Kuorikoski (2010, p.209) concede this point, admitting:
Only rough comparisons of sensitivity can be made at a gross level by comparing overall
sensitivity... there are no general principles for comparing the sensitivity of explanations.
Consequently, non-sensitivity becomes a passive virtue; characterising more as the absence
of vice than a positive trait. This is foreseen in its negative characterisation - the lack of
sensitivity rather than a positive quality in its own right. All explanations should be non-
sensitive to relevant background assumptions. If an explanation of the boiling point of water
were to be sensitive to the ‘price of tea in China,’ it would suggest that the explanation is
wildly wrong. Therefore, non-sensitivity should be assumed in an explanation, making its
presence a vice.
However, non-sensitivity does have a positive role to play in the development of an
explanation. As Woodward & Hitchcock (2003b, p.187) suggest, if an explanation were to be
sensitive to a background assumption, it suggests the background assumption should be made
explicit in the explanation. For an explanation to be sensitive to background assumptions
suggests that those background assumptions play some causal role in that which is being
explained. It should therefore be included in the explanation. However, should a positive
description of non-sensitivity present itself, it would have far more power in guiding IBE.
Currently non-sensitivity helps identify bad explanations, though ideally it would identify
good ones. Below, Thagard’s (1978) Consilience will be outlined, and it will be argued that,
once adapted to the counterfactual model, it can provide a positive characterisation of non-
sensitivity.
IV.iv.
Commitment to a specific model of causal-explanation plays an important role in both the
identification and detailing of explanatory virtues. However, such a commitment has only
gained popularity in the literature in recent times. Consequently, previously proposed virtues
operate at an abstract level, leaving them often unable to lend any informative sway to
comparative considerations. However, these previously proposed virtues are not completely
useless. Rather than throwing out the old accounts and starting again, many of the virtues pre-
causal-model-commitment can be evolved to fit a specific model, and can still be used as a

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basis to build an account. This may be somewhat contentious: many philosophers writing
under a specific causal model either dismiss the former works or simply omit any mention of
them, suggesting they have no role to play. However, this is wasteful of resources. This essay
will now outline consilience as proposed by Thagard (1978). In doing so, the relation
between consilience and non-sensitivity will be stressed, and it will be argued that
consilience, once modified to fit the counterfactual explanatory model, is a powerful
explanatory virtue.
Consilience is an explanation’s ability to explain multiple classes of facts. A class of fact
contains all facts which, individually, one would not consider to add anything new to the
explanation after one fact from the class is already accounted for. Thagard (1978, p.83)
writes:
In inferring the best explanation, what matters is not the sheer number of facts explained, but
the variety, and variety is not a notion for which we can expect a neat formal characterisation.
However, the examples Thagard gives (including the work of Darwin (Thagard, 1978, p.80))
are contentious; it is not clear that the geographical and biological examples given fit into a
specific class of fact without exception. On Thagard’s account of consilience it is correct that
no formal characterisation can be given. However, this issue can be resolved with the
counterfactual explanatory model, and will be done so below.
Thagard (1978, p.79) describes an explanation’s consilience as “a measure of how much a
theory explains.” In judging a theory’s consilience, there are both comparative and isolated
considerations to make. First, for an explanation to be considered adequate in its own right, it
must explain facts from at least two different classes. This aspect of consilience allows an
explanation to be judged in isolation; evaluating whether the explanation is satisfactory
independent of alternatives. Thagard does not explicitly defend the identification of two
classes of facts being the minimum requirement. However, it usefully disallows explanations
which have no generality at all, whilst being concessive enough not to rule out useful, yet
narrow, explanations.
The main importance of consilience is, however, in its comparative ability. Simply, for an
explanation to be judged more consilient than its competitors is for the explanation to explain

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more classes of facts than its competitors. This notion lends itself quite naturally to phrases
such as ‘depth’ and ‘breadth’ when discussing explanations – one may consider an
explanation to have greater breadth than another through its ability to explain over a wider
range of facts. Therefore, if one explanation is able to explain facts from classes A and B,
whilst its competitor can explain facts from classes A, B, and C, then the latter explanation is
more consilient than the former, and thus preferable. Thagard also identifies dynamic
consilience: an explanation’s ability to account for an increasing amount of classes of facts
over time. This, however, is extremely difficult to judge both in isolation and comparatively
and so will not be pursued further.
One difficulty Thagard (1978, p.80) identifies with consilience is that the comparison of
classes of facts is not always as straight forward as one explaining more than another.
Unfortunately, Thagard does not go into great detail as to situations where classes of facts
either do not fully over-lap or have no over-lap. The general idea given is that, in most cases,
an explanation’s consilient superiority will be intuitively apparent. Thagard (1978, p.80) does
give some indication of a method for discerning between the two; however, this is simply to
favour the explanation which explains the ‘most important’ facts. Given no criteria for fact
importance selection, this suggestion is far from satisfactory. Thagard (1978, p.81) defends
his stance, arguing that “[t]he lack of precise methods for individuating classes of facts does
not vitiate consilience as a criterion for evaluating explanatory hypotheses.” This is true,
though it pushes consilience to an unsatisfactorily abstract level, making it difficult to apply
to specific explanations. From a comparative viewpoint, if the superiority of one explanation
over another rests upon the importance of the classes of facts accounted for (as in two
explanations which account for roughly the same amount classes of facts not overlapping),
then without any criteria for discerning this importance a superior explanation will not be
identified.
However, the greatest concern with consilience is in its assumptions. Thagard focuses his
attention on variety of facts, assuming that once an explanation can explain one fact from
within a specific class, all facts within that class are thus accounted for. This implicitly
ascribes to DN model’s condition that an explanation will contain a law of nature – a totally
invariant connection which will allow the explanation to hold in all cases. In fact, his
comment quoted above about sheer numbers of facts explained directly contradicts the
importance of invariance. However, as seen in the development of the counterfactual-model,

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Thagard is incorrect to hold this view. Consilience does, though, get close to a very important
aspect of explanatory depth under the counterfactual model.
An explanation accounting for a variety of facts can be seen as a direct promotion of the
amount of w-questions the explanation can answer. Including more classes of facts in the
explanation is most directly akin to increasing the level of independent variables in the
explanation through making explicit those background assumptions which the explanandum
variables are dependent upon, thus expanding the range of w-questions answered. These
background assumptions, thus utilised, are able to open up a new range of w-questions for the
explanation to answer, where previously they may not have been identified. One may also
include other independent variables; but without their being causally connected their
inclusion is unjustified. Therefore, consilience is restricted to only those variables derived
from causally connected background assumptions.
It is important to recognise that this proposal is distinct from Thagard’s consilience in that
Thagard’s would most naturally be seen to increase the explanandum/dependent variables –
the classes of facts explained. However, this could be trivially achieved; one could simply
conjoin two separate explanations to make a single explanation explaining both sets of
explanandum variables. However, two competing explanations should explain the same
thing. Therefore, they should contain very similar, if not the same, dependent variables.
Therefore, consiliently improving an explanation should be seen as increasing the level of
independent variables to account for the same level of explanandum variables.
Consilience, thus construed, draws strong parallels with Woodward & Hitchcock’s (2003b,
p.187) 7th
condition:
G’ makes explicit the dependence of the explanandum on variables treated as background
conditions by G. [Where G’ is judged a better explanation than G]
By making explicit these background conditions, the explanation can account for facts which
previously it could not. Woodward & Hitchcock (2003b, p.188) give the example of Galileo’s
law of free fall against Newton’s second law of mechanics. The former is inferior to the latter
owing to the latter’s ability to make explicit the background assumptions of the former. These
background assumptions being those such as the size of the body the object is in free-fall to

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etc. By making these explicit, Newtonian mechanics can explain how the free-fall would be
different were it to occur, say, on Mars. This increases the classes of facts the explanation
accounts for.
Therefore, an explanation’s consilient quality is an explanation’s ability to make explicit all
those things which its explanandum variables are dependent upon. Its comparative ability is
fairly straight forward: the explanation which leaves least of these background assumptions
(those playing a causal role) implicit will be superior to its competitors. This ties consilience
strongly to non-sensitivity: for an explanation to be highly consilient is for an explanation to
be highly non-sensitive. A maximally consilient explanation will be maximally non-sensitive,
encompassing all those conditions which play a causal role in the explanation. When
competing explanations both omit causal influences (taking them to be background
assumptions), though different ones, the superior explanation will be the explanation which
omits the causal influences least restrictive of the explanation’s ability to answer w-questions.
Viz. the causal influences affecting the least counterfactual considerations.
This promotes a level of complexity within explanations. Thagard (1978) proposes simplicity
as an explanatory virtue, and Ylikoski & Kuorikoski (2010) promote cognitive salience –that
all explanations must be simple enough to be comprehended. However, providing an
explanation retains the ability to be comprehended, it greater complexity (resulting from a
greater level of independent variables) should be embraced. This is intuitive given the
counterfactual explanatory model. The answering of w-questions is what makes a good
explanation. Therefore, the greater the level of independent variables made explicit, the
greater the explanation. Independent variables allow for answered w-questions; thus, high
levels of independent variables are beneficial.
However, there is also an important pragmatic consideration with this conception of
consilience. Many explanations are causally connected to a great deal of their background
assumptions; however, listing them all explicitly would be, for practical purposes, needless.
Therefore, we may be satisfied with the awareness of these causally connected background
assumptions, rather than their explicit detailing. This way, provided the explainer could point
to the causally connected variable, she need not explicitly state it every time the explanation
is discussed. Consilience would then be the explanation’s ability to identify all those causally
related background assumptions should the need arise.

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As consilience and non-sensitivity therefore become two sides of the same coin, consilience
should replace non-sensitivity as an explanatory virtue. It is positively characterised, guiding
IBE in a more natural way: pushing it toward the best explanation rather than avoiding the
worst. The invariance of an explanation would then be judged within consilience, measuring
the range of invariance of the variables which consilience makes explicit. One might be
concerned that this will draw more comparative complications: should an explanation be
highly consilient or highly invariant in the variables it has identified? However, favour should
fall with the former. For a competing explanation to fail to identify a background
assumptions as causal immediately identifies that explanation as deficient, whereas a failure
in the latter does not draw such direct criticism. This will also immediately identify where the
inferior explanation should be improved, whereas again invariance measurements do not.
Therefore consilience, characterised in this way under the counterfactual model, is a powerful
explanatory virtue.
V
The promotion of an explanation’s ability to answer w-questions is kingmaker, and therefore
should be integral to the major explanatory virtues. This manifests itself as several different
virtues. Some of these, as has been seen, are negatively characterised: promoting the absence
of something unsatisfactory rather than the presence of something positive. This is not too
problematic, though it is not ideal. A positive virtue guides IBE more naturally in that it
identifies the best explanation, rather than highlighting its competitors’ flaws. Therefore,
where possible, explanatory virtues should promote the answering of w-questions in a
positive manner.
This promotion can take two forms; one being the maximising of invariance in the
independent and dependent variables, and the other being the increasing of independent
variables. The first allows for clear comparison of explanations holding the same or very
similar variables. However, as has been shown, the identification of further independent
variables both brings about a higher level of answerable w-questions and also reveals greater
understanding of the involved causal relations – the latter of which the first form of
explanatory virtues are unable to do. Thus, consilience – the latter kind of promotion,

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provides an overarching guide to IBE. The former form as explanatory virtues then provide
further comparative conditions within the consilience.
WORD COUNT: 6000
ACKNOWLEDGEMENTS:
I am extremely grateful to my supervisor Dr. J. Saatsi who has guided me with care and
attention, suggesting further readings and helping me develop ideas during scheduled
meetings and via email.
REFERENCES:
Bartelborth, T. 2002. Explanatory Unification. Synthese [Online]. 130(1). pp. 91-107
[Accessed on: 24/11/2013] Available from: http://www.jstor.org/stable/20117201
Glass, D. 2012. Inference to the Best Explanation: Does it Track Truth? Synthese. [Online]
185. pp.411 – 427. [Accessed: 15/12/2013]. Available from:
http://www.jstor.org/stable/41477706
Harman, G. 1965. The Inference to the Best Explanation. The Philosophical Review.[Online]
74(1). pp.88 – 95. [Accessed: 24/11/2013] Available from:
Harman, G. 1968. Knowledge, Inference, and Explanation. American Philosophical
Quarterly. [Online] 5(3). pp.164 – 173. [Accessed: 24/11/2013]. Available from:
Hempel, C. 1998. Two basic Types of Scientific Explanation. Section 6 Chapter 2 in: Curd,
M. & Cover, J. Ed. Philosophy of Science: The Central Issues. USA: W. W. Norton &
Company.

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Kitcher, P. 1981. Explanatory Unification. Philosophy of Science 48. pp.507–531.
Lipton, P. 2004. Inference to the Best Explanation: Second Edition. London, UK: Routledge
Mackonis, A. 2013. Inference to the Best Explanation, Coherence, and other Explanatory
Virtues. Synthese [Online] 190. pp.975 – 995 [Accessed: 24/11/2013] Available from:
Newton – Smith, W. 2001. Explanation – Chapter 19 in A Companion to the Philosophy of
Science. Pp.127 – 133. Ed. Newton – Smith, W. Oxford: Blackwell Publishers.
Thagard, P. 1978. The Best Explanation: Criteria for Theory Choice. The Journal of
Philosophy. [Online] 75(2). pp.76 – 92. [Accessed: 24/11/2013] Available from:
Thagard, P. 1989. Explanatory Coherence. Behavioural and Brain Sciences. 12. pp.435 –
502.
Thagard, P. 2012. Coherence: The Price is Right. The Southern Journal of Philosophy. 50(1).
pp.42 – 49.
van Frassen, B. 1980. The Scientific Image. Oxford: Clarendon Paperbacks.
van Fraassen, B. 1989. Laws and Symmetry, Oxford: Clarendon Press.
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[Accessed on: 07/01/2014] Available from: http://www.jstor.org/stable/10.1086/651316
Woodward, J. 2003. Making Things Happen. Oxford: Oxford University Press.

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Woodward, J. 2011. Scientific Explanation. In: E. N. Zalta (ed.) The Stanford Encyclopedia
of Philosophy [Online]. [Accessed: 09/01/2014], Available from:
http://plato.stanford.edu/archives/win2011/entries/scientific-explanation
Woodward, J. Hitchcock, C. 2003a. Explanatory Generalisations, Part I: A Counterfactual
Account. Nous [Online]. 37(1) pp.1 – 24. [Accessed: 07/01/2014] Available from:
Woodward, J. Hitchcock, C. 2003b. Explanatory Generalisations, Part II: Plumbing
Explanatory Depths. Nous[Online] 37(2). pp.181 – 199. [Accessed: 07/01/2014] Available
from: http://www.jstor.org/stable/3506081
Ylikoski, P. & Kuorikoski, J. 2010. Dissecting explanatory Power. Philosophical Studies: An
International Journal for Philosophy in the Analytic Tradition [Online]. 148(2) pp. 201-219.
[Accessed: 15/11/2013]. Available from: http://www.jstor.org/stable/4060633

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