11888 850..861


$SSUDLVLQJ�0RGHOV�1RQUHSUHVHQWDWLRQDOO\
$XWKRU�V���7LOO�*U¾QH�<DQRII
6RXUFH��3KLORVRSK\�RI�6FLHQFH��9RO������1R�����'HFHPEHU��������SS���������
3XEOLVKHG�E\��7KH�8QLYHUVLW\�RI�&KLFDJR�3UHVV�RQ�EHKDOI�RI�WKH�3KLORVRSK\�RI�6FLHQFH�$VVRFLDWLRQ
6WDEOH�85/��http://www.jstor.org/stable/10.1086/673893 .
$FFHVVHG������������������

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp

 .
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact support@jstor.org.

 .

The University of Chicago Press and Philosophy of Science Association are collaborating with JSTOR to
digitize, preserve and extend access to Philosophy of Science.

http://www.jstor.org 

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=ucpress
http://www.jstor.org/action/showPublisher?publisherCode=psa
http://www.jstor.org/stable/10.1086/673893?origin=JSTOR-pdf
http://www.jstor.org/page/info/about/policies/terms.jsp
http://www.jstor.org/page/info/about/policies/terms.jsp


Appraising Models Nonrepresentationally

Till Grüne-Yanoff*

Many scientific models lack an established representation relation to actual targets and
instead referto merely possible processes, backgroundconditions, and results. This article
shows how such models can be appraised. On the basis of the discussion of how-possibly
explanations, five types of learning opportunities are distinguished. For each of these
types, an example—from economics, biology, psychology, and sociology—is discussed.
Contexts and purposes are identified in which the use of a model offers a genuine oppor-
tunity to learn. These learning opportunities offer novel justifications for modeling prac-
tices that fall between the cracks of standard representationalist appraisals of models.

1. Introduction. Philosophers’ approaches to appraising models have largely
been focused on their representational functions. On the basis of widely
accepted accounts of what models are—representations—they propose that
models should be appraised accordingly: they are good models to the extent
that they are good representations. Various criteria for good representations
have been proposed, including isomorphism !van Fraasen 1980", similarity
!Giere 1988", and partial resemblance !Mäki 2009". The implicit assumption
underlying these accounts is that models represent real targets—entities or
properties that are found in the real world. Without this assumption, none
of the assessment criteria for models would have much bite: they require
comparing model properties with properties that can be independently ob-
served, measured, or at least indirectly inferred.

As long as modeling practices satisfy these requirements, such repre-
sentational accounts of model appraisal might work fine. However, there is
convincing evidence that many scientific modeling practices do not satisfy
them. The first kind of evidence comes from the way many modelers de-
scribe their own work. Instead of seeking to represent aspects of the real
world, they claim to be aiming at constructing possible or parallel worlds
that may give relevant insights about the real world in more indirect ways

*To contact the author, please write to: Royal Institute of Technology !KTH", Stockholm &
TINT, Helsinki, Teknikringen 78B 10044, Stockholm, Sweden; e-mail: gryne@kth.se.

Philosophy of Science, 80 (December 2013) pp. 850–861. 0031-8248/2013/8005-0017$10.00
Copyright 2013 by the Philosophy of Science Association. All rights reserved.

850

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


!Sugden 2000; Rubinstein 2006". In particular, they claim that these model
constructions involve reference to possible processes, possible background
conditions, and even possible phenomena or properties. Crucially, modelers
claim that such models !at least sometimes" offer a genuine contribution to
our knowledge about the real world.

The second kind of evidence comes from a growing realization among
philosophers that the common realist defenses of model idealization often
do not apply. Among others, it has been pointed out that scientists rarely
invest work in de-idealizing an existing model !Frigg and Hartmann 2009",
that models often are inferentially highly nonrobust with respect to many of
their assumptions !Cartwright 2009", and that models often idealize the very
factors they purport to isolate !Grüne-Yanoff 2011". Philosophers’ arguments
that such models adequately represent their targets despite their many ideal-
izations thus fail in these cases. Yet scientists make use of such models re-
gardless, and so the question arises, whether and how these modeling strate-
gies can be justified.

Philosophers, if they treat such cases at all, have by and large appraised
such modeling practices as playing merely a heuristic role, for example, in
“conceptual exploration” !Hausman 1992", “getting acquainted with mech-
anisms” !Hartmann 1995", “defin#ing$ the extreme of a continuum of cases”
!Wimsatt 2007", or facilitating “creative thought” !Holyoak and Thagard
1995". This heuristic justification is weak because success criteria for such
functions are unclear in the extreme. Furthermore, it places the use of such
models in the same category as taking a walk, reading the newspaper, or
whatever else scientists do in order to inspire themselves to further theory
development. Bunching important kinds of scientific modeling together
with practices that cannot be rationally accounted for seems an unsatisfac-
tory state, which this article seeks to repair.

In particular, I argue that we can learn from models, even if they lack an
established representation relation to real-world targets. Section 2 specifies
what I mean by learning from a model. Section 3 draws on the literature of
how-possibly explanation in order to distinguish different kinds of learning
opportunities from such models. Section 4 illustrates each kind with a con-
crete scientific model and argues that in particular contexts and for specific
purposes, one learns from each. Section 5 presents conclusions.

2. Learning from Models. Learning from a model M, I suggest, is consti-
tuted by a change in confidence in certain hypotheses, justified by reference
to M !Grüne-Yanoff 2009". Under the standard, representational account,
such learning is accomplished by !i" investigating certain properties of the
model and !ii" establishing that the model is a sufficiently accurate repre-
sentation of a !real-world" target in order to license an inference from model
to target. Aerodynamic behavior of a scale model of a new type of airplane,

APPRAISING MODELS NONREPRESENTATIONALLY 851

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


for example, is investigated in a wind tunnel. It is then concluded that an
actual airplane of that type has similar properties, given that the scale model
and the actual plane are sufficiently similar with respect to the proportions
of their hull elements, the geometry of their wings, and so forth. If the model
user believes in the validity of the model investigation and the sufficient
similarity between the model and the target, and her prior beliefs about the
plane’s aerodynamics are not identical to the model result, then she has
learned from the model about the world.

I claim that one can learn from a model even without establishing its
representational adequacy. That is, reference to mere model investigations—
with no established representational link to any real-world target—may jus-
tify changing one’s confidence in some hypotheses about the world.

Let me clarify this claim. First, it is important to stress that reference to
such a model should justify changing one’s confidence in some hypothe-
sis about the world. It is trivial that investigating a model might result in
some justified changes of our beliefs in the model artifact. That is not what
I mean here. Instead, I claim that investigating the model can teach us about
something different from the model itself.

Second, I do not claim that such models justifiably affect our beliefs
about particular actual entities or about properties instantiated in the real
world. That would require models that represented these entities or proper-
ties sufficiently well. Instead, I claim that they might change claims about
possible entities, properties, or processes. Such possibility claims are about
the world: they are about what is possible in this world, not about facts in
some other, possible, world. Furthermore, many such possibility claims are
highly relevant for theoretical and practical objectives. Consider, for exam-
ple, the following points:

• A policy maker seeking to reduce urban segregation might change
her policies upon learning that racist preferences are not a necessary
cause of segregation.

• A scientist seeking to explain a population dynamic might change
his explanatory strategy when learning that this dynamic cannot be
produced from actual background conditions with a set of plausible
migration decision rules alone.

• A policy maker who learns that altruistic preferences are adaptive
under certain possible conditions might change her evaluation of
certain institutional regulations.

Thus, changes in the confidence of hypotheses of the above kind affect
the ways we seek to explain and control the actual world. If models would
justify changes in the confidence of such hypotheses, one would learn from
such models about the world.

852 TILL GRÜNE-YANOFF

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


Third, such possible entities, properties, or processes are conceptually
linked to the world. For example, a possible migration rule in an agent-based
model falls under the same concept as actual agents’ decision rules concern-
ing migration. But this conceptual link does not require a resemblance be-
tween the model rule and any real-world target. Thus, the possibility that
such a model presents is relevant for the discussion of actual population dy-
namics, even if the model rule does not adequately resemble !or is not es-
tablished as adequately resembling" actual migration rules.

Finally, the most relevant kind of possibility the models I discuss here
deal with is epistemic possibility, that is, the possibility of entities, processes,
or properties not ruled out by what the agent knows or believes. Acknowl-
edging this makes clear that we do not learn from the model in isolation but
rather from the model in conjunction with background knowledge and be-
liefs. This knowledge at least partly consists in claims about possible, non-
actual states, which does not !or cannot" refer to actual targets. In this con-
junction, the model performs two epistemic functions: it conceptualizes
existing beliefs in a new way, and it draws inferences from the thus estab-
lished possibility, given all relevant beliefs.

Models thus help only to present existing knowledge and infer from it.
Cognitively unlimited beings would have no need for them. But this is just
the same as with the representational account: we learn from those models
only because they help us make inferences from beliefs we gleaned from the
target systems. In the cases discussed here, however, the knowledge pro-
cessed by the model is different: it contains beliefs about possible entities,
processes, or properties, which cannot be obtained by establishing an ade-
quate representation of the model to actual target systems. Thus, adequate
representation is not a useful appraisal criterion for such models. Instead, I
propose learning as the appropriate criterion.

3. How-Possibly Explanations. How can we learn from models that do
not have an established representation relation to a real-world target? To
answer this question, the extant literature on how-possibly explanations is
instructive. This literature controversially discusses what characterizes how-
possibly explanations; what distinguishes them from how-actually, potential,
or how-possible explanations; and whether how-possibly explanations are
explanations at all. In this article, I eschew these controversies. Instead I use
the conceptual distinctions offered by this debate to categorize different learn-
ing opportunities that models without an established representation relation
to a real-world target might provide.

i" The debate commences with Dray’s !1957" claim that how-possibly
explanations have a different aim and a different structure from how-actually
explanations. How-possibly explanations aim at giving an account how
events that are considered impossible could have happened. How-actually

APPRAISING MODELS NONREPRESENTATIONALLY 853

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


explanations, in contrast, aim at accounting for how actual events have
happened. Furthermore, Dray argues that how-possibly explanations rebut
the impossibility of the explanandum by giving a necessary condition for its
occurrence. He contrasts this with actual explanations offering sufficient con-
ditions for their explananda.1

This distinction is relevant for the present analysis. Actual explanation
requires the identification of true !sufficient parts of" causes that brought
about the explanandum. Models that adequately represent relevant targets
contribute to such explanations. How-possibly explanations, in contrast,
identify elements of possible causes for an explanandum. Models can iden-
tify such possible causes—and hence contribute to how-possible explana-
tions—without representing real-world entities or properties. Thus, by affect-
ing our belief in impossibility claims, we can learn from such models.

ii" The Dray-Reiner type of how-possibly explanations focuses on iden-
tifying some initial conditions, which show the possibility of the explanan-
dum. Another kind of how-possible explanation instead focuses on indi-
cating the sort of process through which the explanandum could possibly
have been produced. Here the purpose lies in giving an account of how cer-
tain !possible" initial conditions might yield the explanandum through a pos-
sible model process—a question that is of particular interest in those cases
in which the explanandum and the initial conditions in question are prima
facie incompatible.

iii" Another purpose is to account for the possibility of a real target object
having a certain property, produced by a possible process from actual back-
ground conditions !what Forber #2010$ calls “local” how-possibly explana-
tions". Here both initial conditions and explanandum are given, and the how-
possibly explanation either seeks to identify any process that produces the
latter from the former or investigates how a particular model process can—
or cannot—produce the actual target properties from the given conditions.

iv" Forber !2010" distinguishes such local from what he calls “global”
how-possibly explanations. Global how-possibly explanations account for
the possibility that an idealized object has a certain property, produced by a
possible process from possible background conditions. Forber suggests that
the purpose of these how-possibly explanations is investigating the capabil-
ities of general model processes.

v" Finally, how-possibly explanations have been interpreted not in con-
trast to how-actual explanations but rather as their precursors. According to
this view, how-possibly explanations are similar to how-actually explana-
tions in that they satisfy most explanatory virtues, but they are inferior in

1. Reiner !1993" has criticized Dray’s account, pointing out that how-possibly expla-
nations do not really identify necessary conditions of the explanandum, but rather nec-
essary parts of a sufficient condition for the explanandum.

854 TILL GRÜNE-YANOFF

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


that they lack adequate empirical support !Resnik 1991, 143". In particu-
lar, they are reasonably complete, showing how the explanandum was gen-
erated through a process from initial and background conditions. But pro-
cess and background conditions are not well supported empirically, so the
account offers a mere possible or potential explanation.

Let me summarize. Models that lack an established representation relation
to actual targets have a number of distinct purposes, which have been dis-
cussed in the philosophical literature under the heading of “how-possibly
explanations.” As the analysis of some of the key controversies in this liter-
ature showed, this notion contains a number of disparate scientific objec-
tives—some of them explanatory, some offering other forms of epistemic
gain. In the next section, I discuss each of these five kinds of learning pur-
poses at the hand of a scientific modeling example, showing how in particular
situations and for particular purposes, one can learn from each.

4. Five Cases of Learning. In the following subsections, I give five ex-
amples of models that lack an established representation relation to actual
targets. Each example corresponds to a type of how-possibly explanation
discussed in the previous section. For each case, I identify contexts and pur-
poses in which these respective models offer an opportunity to learn about
the world.

i. AffectingImpossibilityClaims. Schelling’s!1971" checkerboardmodel
produces an abstract pattern of spatial segregation that he claims can be
found in many cities but is not associated with any concrete settlement or
even type of settlement. Schelling produces this abstract result with two sets
of tokens, initially distributed randomly over a checkerboard. Tokens move
according to an iterated rule until no more movements occur. The rule is
this. For a given token, if more than half of the tokens on !Moore-" neigh-
boring fields are of a different type, then this token will move to an-
other vacant field, with less than half of the neighboring fields occupied
with tokens of the other type. Schelling claims neither this process to rep-
resent an actual migration process nor the checkerboard to represent an ac-
tual neighborhood. Nor does he justify these model choices with reference
to a real-world phenomenon. But he claims that the process is started by
an actual initial condition, namely, the !nonracist" preference of individuals
not to be in the minority. It is the one aspect of his model that he seeks to
connect with the actual world, citing behavioral examples from restaurants,
clubs, and classrooms.

Thus, the model system that produces the segregation pattern is not es-
tablished as an adequate representation of any real-world system. The spe-
cific shape of the preferences—the only part of the model that is justified
as an adequate representation—alone does not produce the segregation pat-

APPRAISING MODELS NONREPRESENTATIONALLY 855

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


tern. Appraised from a representational perspective, therefore, such a model
should be regarded with suspicion.

Yet we learn from Schelling’s model. It shows the possible production of
an abstract pattern !a segregation of the two types of tokens on the check-
erboard" from a possible and one actual initial condition and a possible pro-
cess. In the context of spatial residential segregation, where the abstract
segregation pattern might be realized, this possible production result is of
particular importance: until then it was widely believed that racist prefer-
ences were a necessary cause of segregation. Schelling’s model shows that
segregation patterns might be produced by another cause, which is an actual
property of agents in many real-world populations: namely, the preference
not to be in the minority !it shows only that it might be produced because it
does so in a merely possible context—with an environment and a process
that our knowledge does not rule out but that we by no means can assume
to be the actual environment or process". The model result thus justified
changing one’s confidence in hypotheses about racist preferences being a
necessary cause of segregation. Anyone who had high confidence in such
hypotheses learned from Schelling’s checkerboard model.

ii. Identifying Potential Mechanisms. Ainslie’s !2001" feedback model
of self-control produces a behavioral phenomenon: the moderate impulsiv-
ity of human choices in the absence of precommitment devices. Ainslie sees
this phenomenon exemplified, for example, in the considerable number of
addicts, most of whom eventually overcome their addiction. His model pro-
duces this result from a particular shape of time preferences !a shape in-
versely proportional to delay of consumption" and a process of recursive self-
prediction—prediction that is fed back to the ongoing choice process. The
employed preference shape !also known as “hyperbolic discounting”" was
first proposed in order to account for impulsive choice and hence is con-
sidered an actual initial condition by some. Yet the moderate impulsivity of
human choice has led many to doubt that humans actually discount future
value hyperbolically.

It is exactly the aim of Ainslie’s model to show that the hyperbolic de-
scription is compatible with moderate impulsive behavior by directly stoking
it on the one hand and by indirectly moderating it, on the other hand, through
a process of self-prediction that arises from this hyperbolic form itself.
Given this objective, Ainslie cannot treat the hyperbolic shape as a fact, but
only as a possibility. Furthermore, he readily admits that the process of re-
cursive self-prediction is inaccessible to controlled experiment and hence
remains a mere possibility. Thus, the model produces the behavioral phe-
nomenon from two merely possible elements, neither of which is justified
as an adequate representation of real-world properties or processes. Addi-

856 TILL GRÜNE-YANOFF

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


tionally, the behavioral phenomenon to be produced is often seen as evi-
dence against the hyperbolic shape of preferences. A representationalist ap-
praisal of Ainslie’s model would therefore surely be negative.

Yet one learns from Ainslie’s model in two ways. First, the model justifies
a change in confidence in the hypothesis that intertemporal behavioral data
are incompatible with a hyperbolic shape of discounting. If self-prediction
were operational, then hyperbolic discounting would be compatible with mod-
erate impulsiveness. Given that self-prediction is not ruled out by our back-
ground beliefs, we learn from the model that hyperbolic discounting is indeed
possible, even if we observe moderate impulsiveness.

Ainslie draws a second and stronger lesson from this possibility. If hy-
perbolic discounting is possible, he argues, then in order to produce mod-
erate impulsiveness, we need a process like recursive self-prediction. Al-
though self-prediction is only a possible process, the paucity of alternative
describable processes not ruled out by our background beliefs lets Ainslie
conclude that this result indeed should increase one’s confidence in self-
prediction. In Ainslie’s words, “a small number of selected thought experi-
ments yield a valid rejection of the null hypothesis—that contingent self-
prediction is unnecessary for volition” !2009, 145". Thus, we learn from the
model about a potential mechanism that produces the phenomenon under
specified possible conditions. This in turn justified raising our confidence
in the possibility of hyperbolic discounting and—under the additional as-
sumption that alternative possible processes with that result are hard to
find—also our confidence in the possibility of the potential mechanism. Con-
sequently, those with low confidence in either of these two claims learned
from Ainslie’s model.

iii. Learning the Impossibility of Producing a Property. Axtell et al.’s
!2002" Anasazi model seeks to produce a historically documented popula-
tion dynamic of a Native American settlement in the US Southwest from
contemporary soil and meteorological data, through possible migration de-
cision processes of the modeled people. These processes involved rules
whether to reproduce, to split up households, or to leave the settlement, given
harvest levels. The model thus seeks to produce an actual phenomenon from
actual initial conditions through a set of possible model processes.

Unlike the previous examples, this model was built with the help of
computer simulations, which allowed the modelers to run through a large
number of different but related possible processes in order to choose the
one that produced a result with the best fit to the actual population dynamic.
What all these processes have in common is that they model the environ-
mental effect on individual decisions directly !the authors call these “push
factors”", disregarding interactive social or cultural influences !“pull factors”".

APPRAISING MODELS NONREPRESENTATIONALLY 857

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


These model processes were not identified by behavioral evidence from
the historical populations !no relevant records exist" but rather as possi-
bilities constrained by knowledge about today’s microsocieties. Appraised
from a representational perspective, the modelers’ claim that their model
explains !i.e., identifies the true causes of the population dynamic" should
therefore be regarded with caution. In any case, the model fails to produce
the most salient feature of the actual population dynamic, the complete ex-
odus around AD 1400.

Yet, one learns from the Anasazi model—specifically from its failure.
The model shows that with any available push-type process, the exodus
cannot be produced. This should increase one’s confidence that it is impos-
sible to produce this property with these kinds of processes. Consequently,
the model justifies increasing one’s confidence in the belief that another ca-
pacity !cultural pull factors" must be included in a model to produce the ac-
tual population dynamics from the initial conditions.

iv. Capacities of a Possible System. Güth’s!1995"indirectevolutionary
approach offers a model of preference evolution, which produces pref-
erences for reciprocity. The model starts with a population of agents who
have different preferences over objects of choice !e.g., consumption bun-
dles or behavioral strategies". Agents’ rational choices then are determined
according to their preferences so that different preferences lead to differ-
ent choices. Depending on their choice !and the environment in which the
choice is made", an agent will have greater or lesser reproductive success
than other agents with different preferences and hence different choices.
Assuming that preferences are inherited, differential reproduction of agents
then leads to differential replication of preferences in the population. Clearly,
the background conditions of this model, in particular, the distribution of
preferences in the population and the differential reproductive success of
certain choices, are not rooted in beliefs about actual real-world targets but
are mere possibilities. Similarly, the model processes by which preferences
are reproduced across generations, and by which preference-based choices
yield reproductive advantages or disadvantages, have no established rela-
tion to real-world targets. Instead, they are mere possibilities, adapted from
selection processes of much simpler !and more uniform" traits. Even the re-
sult—preferences for reciprocation—is described only in abstract terms, and
Güth makes no attempt to link it to concrete real-world targets. So again, a
representationalist appraisal would not find much to praise in this model.

Nevertheless, one can learn from Güth’s model. It shows that preferences
with certain abstract properties can be produced through natural selection
in nonactual circumstances.2 More concretely, a possible adaptive system,

2. In this case reciprocation, but in related papers, Güth also produces preference for
fairness and trust.

858 TILL GRÜNE-YANOFF

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


free from external intervention or constraints, has the capacity to produce
preferences for reciprocation. This is a lesson of potential practical rele-
vance. Anyone who with high confidence believed that reciprocation, fair-
ness, or trust could not possibly be an adaptive trait has good reason to
change his belief when confronted with this model.

v. Potential Explanation. Trivers’s !1971" reciprocal behavior model
produces a concrete actual result, the particular behavioral patterns ex-
hibited by cleaner fish !labroides dimidiatus" and their hosts. To this end,
it employs an actual process, frequency-dependent selection, which is found
in many instances of biological and cultural evolution. Cleaner and host, so
Trivers argues, are engaged in an indefinitely repeated prisoner’s dilemma
game, where the gains of cooperation !i.e., the cleaner feeds on the host’s
parasites and the host does not eat the cleaner" are sufficiently high to ensure
differential reproductive success over unilateral defection. However, Triv-
ers’s model does not employ actual but rather possible background con-
ditions. In fact, the very purpose of his model is to identify initial conditions
that would license a selection explanation of reciprocal behavior between
cleaner and host. These include “that hosts suffer from ectoparasites; that
finding a new cleaner may be difficult or dangerous; that if one does not
eat one’s cleaner, the same cleaner can be found and used a second time;
that cleaners live long enough to be used repeatedly by the same host; and if
possible, that individual hosts do, in fact, reuse the same cleaner” !1971, 41".

That Trivers list these conditions in this way makes clear that his model,
at least at the time of writing, lacks established representational links to its
potential target system. It does not represent what the initial conditions of
the cleaner-host system actually are, but rather what they possibly could be.
A representationalist appraisal would—correctly—point out that Trivers’s
model, because of the nonestablished initial conditions, does not provide a
how-actually explanation of the cleaner-host symbiosis.

Yet, contrary to the representationalist appraisal, this does not relegate
the model to a merely heuristic function. Rather, one can !or, rather, could
in 1971" learn important lessons from the model. It offers a potential ex-
planation of a specific phenomenon that previously was either unexplained
or explained differently. Before 1971, many people probably believed that
no such adaptive explanation was possible. Or if they did, they probably
had low confidence that Trivers’s list would be the set of necessary con-
ditions for such an adaptive explanation. His model gave them good reasons
to change these beliefs, and it taught many marine biologists what to look
for when observing labroides.

5. Conclusions. I have argued that one can rationally justify the use of
models that do not have an established representation relation to any actual
target by showing that one learns from them about the world. To this end, I

APPRAISING MODELS NONREPRESENTATIONALLY 859

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


characterized learning as justifying a change in confidence in certain hy-
potheses about the world. I then discussed a number of hypotheses relating
to possibility claims and argued that changing one’s confidence in any of
them would affect the way scientists and policy makers seek to explain and
control the actual world. These hypotheses, although consisting in possibil-
ity claims, thus are about the world.

To analyze different kinds of possibility claims made with such models, I
employed conceptual distinctions from the discussion of how-possibly ex-
planations. Five kinds of learning opportunities emerged. I illustrated each
with a concrete scientific model. In particular contexts and for specific pur-
poses, I argued, one could learn from each of them.

Of course, one cannot learn from every model of this kind: many such
models present possibilities that everybody already believes in or impossi-
bilities nobody doubts. For those models, my account does not provide jus-
tification. This is just how it should be. It is the delicate art of the modeler
to sense the scientific community’s conviction that something is necessary
or impossible and then to construct a model that convinces them to change
their conviction. When this art fails, such models do not teach anything. But
where it succeeds, these important functions justify modeling practices that
are neglected by a representationalist appraisal of models.

REFERENCES

Ainslie, George. 2001. Breakdown of the Will. Cambridge: Cambridge University Press.
———. 2009. “Recursive Self-Prediction in Self-Control and Its Failure.” In Preference Change:

Approaches from Philosophy, Economics, and Psychology, ed. Till Grüne-Yanoff and Sven
Ove Hansson, 139–58. Berlin: Springer.

Axtell, Robert L., Joshua M. Epstein, Jeffrey S. Dean, George J. Gumerman, Alan C. Swedlund,
Jason Harburger, Shubha Chakravarty, Ross Hammond, Jon Parker, and Miles Parker. 2002.
“Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long
House Valley.” Proceedings of the National Academy of Sciences 99 !3": 7275–79.

Cartwright, Nancy. 2009. “If No Capacities Then No Credible Worlds: But Can Models Reveal
Capacities?” Erkenntnis 70 !1": 45–58.

Dray, William H. 1957. Laws and Explanations in History. Oxford: Oxford University Press.
Forber, Patrick. 2010. “Confirmation and Explaining How Possible.” Studies in History and Phi-

losophy of Biological and Biomedical Sciences 41 !1": 32–40.
Frigg, Roman, and Stephan Hartmann. 2009. “Models in Science.” In Stanford Encyclopedia of

Philosophy, ed. Edward N. Zalta. Stanford, CA: Stanford University.
Giere, Ronald N. 1988. Explaining Science: A Cognitive Approach. Chicago: University of Chi-

cago Press.
Grüne-Yanoff, Till. 2009. “Learning from Minimal Economic Models.” Erkenntnis 70 !1": 81–99.
———. 2011 “Isolation Is Not Characteristic of Models.” International Studies in the Philosophy

of Science 25 !2": 1–19.
Güth, Werner. 1995. “An Evolutionary Approach to Explaining Cooperative Behavior by Recip-

rocal Incentives.” International Journal of Game Theory 24:323–44.
Hartmann, Stephan. 1995. “Models as a Tool for Theory Construction: Some Strategies of Pre-

liminary Physics.” In Theories and Models in Scientific Process, ed. William E. Herfel,
Wladyslaw Krajewski, Ilkka Niiniluoto, and Ryszard Wojcicki, 49–67. Amsterdam: Rodopi.

860 TILL GRÜNE-YANOFF

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp


Hausman, Daniel. M. 1992. The Inexact and Separate Science of Economics. Cambridge: Cam-
bridge University Press.

Holyoak, Keith, and Paul Thagard. 1995. Mental Leaps: Analogy in Creative Thought. Cambridge,
MA: Bradford.

Mäki, Uskali. 2009. “MISSing the World: Models as Isolations and Credible Surrogate Systems.”
Erkenntnis 70 !1": 29–43.

Reiner, Richard. 1993. “Necessary Conditions and Explaining How-Possibly.” Philosophical
Quarterly 44 !170": 58–69.

Resnik, David B. 1991. “How-Possibly Explanations in Biology.” Acta Biotheoretica 39:141–49.
Rubinstein, Ariel. 2006. “Dilemmas of an Economic Theorist.” Econometrica 74 !4": 865–83.
Schelling, Thomas C. 1971. “Dynamic Models of Segregation.” Journal of Mathematical Soci-

ology 1 !2": 143–86.
Sugden, Robert. 2000. “Credible Worlds: The Status of Theoretical Models in Economics.” Jour-

nal of Economic Methodology 7 !1": 1–31.
Trivers, Robert L. 1971. “The Evolution of Reciprocal Altruism.” Quarterly Review of Biology

46:35–57.
van Fraasen, Bas. 1980. The Scientific Image. Oxford: Oxford University Press.
Wimsatt, William. 2007. Re-engineering Philosophy for Limited Beings: Piecewise Approxima-

tions to Reality. Cambridge, MA: Harvard University Press.

APPRAISING MODELS NONREPRESENTATIONALLY 861

This content downloaded from 130.229.157.133 on Fri, 24 Jan 2014 07:54:05 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/page/info/about/policies/terms.jsp

	Cit p_3:1: 
	Cit p_4:1: 
	Cit p_6:1: 
	Cit p_9:1: 
	Cit p_11:1: 
	Cit p_16:1: 
	Cit p_17:1: 
	Cit p_18:1: 
	Cit p_19:1: 
	Cit p_20:1: 
	Cit p_21:1: