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Khosrowi, Donal (2020) 'Getting serious about shared features.', British journal for the philosophy of science.,
71 (2). pp. 523-546.

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Getting Serious about Shared Features 
Donal Khosrowi 

 
ABSTRACT 

In Simulation and Similarity, Michael Weisberg offers a similarity-based account of the 
model–world relation, which is the relation in virtue of which successful models are 
successful. Weisberg’s main idea is that models are similar to targets in virtue of sharing 
features. An important concern about Weisberg’s account is that it remains silent on what it 
means for models and targets to share features, and consequently on how feature-sharing 
contributes to models’ epistemic success. I consider three potential ways of concretizing 
the concept of shared features: as identical, quantitatively sufficiently close, and 
sufficiently similar features. I argue that each of these concretizations faces significant 
challenges, leaving unclear how Weisberg’s account substantially contributes to elucidating 
the relation in virtue of which successful models are successful. Against this background, I 
outline a pluralistic revision of Weisberg’s account and argue that this revision may not 
only help Weisberg evade several of the problems that I raise but also offers a novel 
perspective on the model–world relation more generally. 

 

1 Introduction 
2 Weisberg’s Feature-Sharing Account 
3 What is a Shared Feature? 
3.1    Identity 
3.2    Sufficient closeness 
3.3    Sufficient similarity 
4 Turning Weisberg’s Account ‘Upside Down’ 
5 Conclusion 

 

1 Introduction 

Various authors in the philosophy of science literature have offered accounts of the model–

world relation, which is the relation in virtue of which scientific models exhibit their 

predictive and explanatory capabilities. Similarity-based accounts assert that models have 

such capabilities in virtue of being sufficiently similar to their targets in certain relevant 

respects (Cartwright [1983]; Giere [1988], [2004], [2010]; Godfrey-Smith [2006]). In 

contrast to isomorphism-based accounts (Bueno, French and Ladyman [2002]; da Costa 

© The Author(s) 2018. Published by Oxford University Press on behalf of British Society for the Philosophy of Science. 
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any 
medium, provided the original work is properly cited. 

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and French [2003]), they purportedly capture several important intuitions: that the model–

world relation is instantiated in degrees (Weisberg [2013], p. 154); that only relevant 

instances of the relation matter for successful representation (Weisberg [2013], p. 135); and 

that contextual factors such as modellers’ epistemic aims play important roles in scientific 

representation (Giere [2004], [2010]; van Fraassen [2008]; Mäki [2009]). 

Despite these achievements, similarity-based accounts also face several criticisms. One 

is that they remain uninformative about how models’ similarity to targets facilitates their 

epistemic capabilities unless it is specified in virtue of what properties models are 

purportedly similar to their targets (Stefanov [2012], p. 72). Another pertinent criticism is 

offered by Paul Teller ([2001]), who argues that attempts to offer a general account of 

model–world similarity are misguided, as only the particular context of specific modelling 

activities can tell us what it means for models to be relevantly and sufficiently similar to 

their targets. 

Michael Weisberg ([2013]) offers an analysis of model–world similarity that aims to 

resist these concerns. On this account, model–world similarity is determined by the 

proportion of features that models share and do not share with their targets. According to 

Weisberg, his account not only accommodates several important intuitions, but is also 

superior to earlier accounts specifically in virtue of offering an explicit analysis of what 

model–world similarity supervenes on ([2013], pp. 143, 155). 

Weisberg’s account has been criticized on several grounds. Tal ([2015]) worries that it 

involves a circularity. O’Connor and Weatherall ([2016]) argue that a single relation like 

similarity is unable to capture the substantial heterogeneity in how models across the 

sciences relate to their targets. Wendy Parker ([2015]) raises several, more specific 

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Getting Serious about Shared Features 

 
 

	

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concerns, one being that on Weisberg’s account it is unclear what form shared features 

take. 

In what follows, I offer a line of criticism that significantly expands on Parker’s 

concern. Starting from the diagnosis that a fundamental shortcoming of Weisberg’s account 

is that it remains vague about what it means for models and targets to share features, I 

consider three ways in which shared features might be explicated: as identical, 

quantitatively sufficiently close, and sufficiently similar features. I argue that all three 

construals face significant challenges, leaving unclear how Weisberg can achieve his aim 

of providing an informative account of the relation in virtue of which successful models are 

successful. 

I then propose that Weisberg might revise his account as a pluralistic account. This 

revised account offers a menu of different ways in which models can share features with 

their targets: by means of quantitative closeness, identity, isomorphism, similarity, and 

possibly other relations too. This revision does not only help Weisberg address some of the 

challenges that I raise, but also offers a novel perspective on the model–world relation 

more generally. Specifically, it departs from the monistic character of extant similarity- and 

isomorphism-based accounts that have specified one single type of relation to govern the 

manifold ways in which successful models can relate to their targets. The pluralistic 

revision I sketch out adds a conciliatory perspective, suggesting that there can be a division 

of labour between these rival accounts, where certain types of relations, such as 

isomorphism, similarity, identity, and quantitative closeness, are less or more appropriate 

for facilitating epistemic success depending on general characteristics of the modelling 

context, such as the type of model used, the kinds of features at issue, and the types of 

modelling goals being pursued. I argue that this pluralistic perspective offers a promising 

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basis for improving our understanding of the relation in virtue of which successful models 

are successful. 

Let me begin with a sketch of Weisberg’s account before I proceed to my critical and 

constructive contributions. 

 

2 Weisberg’s Feature-Sharing Account 

On Weisberg’s account, model–world similarity is a triadic relation between a model, a 

modeller, and a target system. According to Weisberg ([2013], pp. 39, 147), when 

constructing models, modellers pursue certain modelling goals regarding the model’s scope 

and fidelity desiderata such as predictive accuracy, robustness, explanatory power, among 

others. These goals inform in which respects models and targets are intended to be similar, 

and in which respects it is of no concern whether they are. 

To capture this, Weisberg ([2013], p. 148) introduces the feature set, which contains the 

features of a model and its target that a modeller considers relevant for her modelling. The 

members of this set are divided into two types of features: attributes and mechanisms. 

Attributes are the features of a model or target that can be considered its states, and 

mechanisms are the transition rules that bring about these states ([2013], p. 145). 

With this background in place, Weisberg’s main idea is that features that are shared by 

the model and target should add to their similarity, while features that are not shared should 

detract from it. This draws on an extensive tradition of explicating similarity in terms of 

shared properties (Hesse [1966], p.71, [1974], p. 67; Goodman [1972]). 

Building on this idea, Weisberg ([2013], p. 146) distinguishes two ways in which 

models and targets may fail to share features: first, features that a model exhibits but the 

target does not should detract from their similarity—call them extraneous features; second, 

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Getting Serious about Shared Features 

 
 

	

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features of a target that are not captured by the model should also detract from their 

similarity—call them missed features. Against this background, and drawing explicitly on 

an account by Tversky ([1977]), Weisberg ([2013], p. 147) proposes the following metric 

of model–world similarity that involves the purportedly clearer concepts shared, missed, 

and extraneous features: 

 

𝑆 𝑀,𝑇 = &'∩)' * &+∩)+
&'∩)' * &+∩)+ * &',)' * &+,)+ * )',&' * )+,&+

 . 

 

Here, M refers to the model’s and T to the target’s features and the subscripts a and m 

partition features into attributes and mechanisms. In the numerator, we find the intersection 

of attributes and the intersection of mechanisms, which add to the cardinality of shared 

features. We find the same terms in the denominator. In addition, we find 𝑀- − 𝑇- +

𝑀0 − 𝑇0 , which count the model’s extraneous features, as well as 𝑇- − 𝑀- +

𝑇0 − 𝑀0 , which count the model’s missed features. An increase in either term yields a 

decrease in 𝑆 𝑀,𝑇 ,  penalizing features that are not shared. In the case of perfect 

similarity, they are empty sets, yielding a similarity value of unity. In the converse case of 

minimal similarity, the cardinality of features that are not shared will exceed the cardinality 

of shared features significantly. Hence, in the positive limit of these terms 𝑆 𝑀,𝑇  

approaches zero, so 𝑆 𝑀,𝑇 ∈ 0,1  (Weisberg [2013], p. 147). 

In addition to this calculus, Weisberg’s account also permits the use of term weights 

and weighting functions. Weighting functions capture the idea that modellers may consider 

some features to be more important than others, and term weights capture that modellers 

may wish to put more emphasis on some of the terms, such as in how-possibly modelling 

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where shared attributes are emphasized over shared mechanisms (Weisberg [2013], pp. 

151–2).  

With this sketch in place, let me expand on Weisberg’s pursuit of at least three distinct 

aims. His first aim is to inform about the goodness of fit between models and targets. 

According to Weisberg ([2013], p. 150), his account can help scientists compare ‘the 

relationship that [a] model actually holds to the world to the one that they are interested in 

achieving’. Weisberg’s second aim is to inform about the sources of disagreement between 

scientists about the goodness of fit of models to targets. According to Weisberg, such 

disagreements can be explained as a function of differences in scientists’ feature sets, term 

weights, and weighting functions. The third, analytical aim is to offer a reductive analysis 

of what model–world similarity supervenes on. (Weisberg [2013], pp. 143, 149, 155) This 

is accomplished by specifying several distinct realizers of similarity: instances of shared, 

missed, and extraneous features, as well as contextual and interest-relative factors, 

including modellers’ feature sets, term weights, and weighting functions. 

In pursuing this analytical aim, Weisberg ([2015], p. 303) seeks to address the pertinent 

criticism that similarity is essentially vague, elusive, and may resist general philosophical 

analysis in principle (see, for example, Quine [1969]; Goodman [1972]; Teller [2001]). 

Applied to model–world similarity, this criticism amounts to arguing that as long as it is 

not explicated what model–world similarity consists in, claiming that successful models are 

successful in virtue of being highly similar to their targets does not elucidate the model–

world relation beyond the level of intuitively appealing slogans. Weisberg ([2013], p. 143) 

seeks to evade this concern by offering an analysis of similarity in terms of the purportedly 

clearer concepts of shared, missed, and extraneous features. 

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Getting Serious about Shared Features 

 
 

	

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Even so, while Weisberg explicitly presents his account as providing an analysis of 

similarity, it is unclear what the target of this analysis is. As Parker ([2015], p. 270) argues, 

Weisberg seems to equivocate between three distinct questions that such an analysis may 

seek to elucidate: 

(1) What is the relation in virtue of which successful models are successful? 

(2) What is the sort of relation that generally holds between models and targets? 

(3) What underlies scientists’ judgements of the extent to which models are similar to 

their targets? 

In a reply, Weisberg ([2015], p. 300) clarifies that his account is ‘addressing all three of 

these themes’. In what follows, I focus attention specifically on the first aim: to provide an 

account of the relation in virtue of which successful models are successful. I take this to be 

the most salient and important issue that an account of the model–world relation should 

address. Moreover, I also consider this to be the intended target of earlier similarity-based 

accounts as well as other accounts of the model–world relation. Finally, Weisberg ([2013], 

p. 155) considers his account to be a substantive advancement over its intellectual 

progenitors specifically in virtue of offering an analysis that can better elucidate this 

question. With this in mind, let me turn to my main concern, which is that Weisberg 

remains vague about what shared features are. 

 

3 What Is a Shared Feature? 

While Weisberg is explicit concerning the distinction between missed and extraneous 

features, he does not explicate what shared features are and how instances of shared 

features can be distinguished from missed and extraneous cases. 

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There are several important questions here, including: (1) what is the distinction 

between shared and missed and extraneous features in general? (2) How do scientists 

determine whether a given feature is shared or not? (3) Is feature-sharing a gradual or 

dichotomous concept? (4) What is the role of background theory and modelling goals in 

determining whether features count as shared? 

Without an explication of shared features that can address at least some of these 

questions, Weisberg’s account remains uninformative.1 It offers an analysis of similarity in 

terms of a purportedly clearer concept, feature-sharing, but this concept is no more clearly 

understood than similarity itself. So without an account of what shared features are, it 

remains unclear how and when they contribute to or account for models’ epistemic success, 

and Weisberg’s analysis subsequently fails to elucidate the relation in virtue of which 

successful models are successful. 

This concern is further aggravated when considering Teller’s contextual view on 

model–world similarity. Teller ([2001], p. 402) argues that there can be no general analysis 

of model–world similarity as only the specific details of particular modelling contexts can 

tell us what it means for models to be relevantly and sufficiently similar to their targets. 

Applied to the issue of what counts as a shared feature, a Tellerian view might say that 

there can be no general analysis of feature-sharing as only the details of specific modelling 

contexts can tell us which features need to be shared, as well as in what ways and to what 

degrees they need to be shared. 

																																																								
1 One might worry that it is important to keep the first two questions distinct, because an informative 

account may well be one that addresses (1) but not (2). I agree, but will not consider this here for three 
reasons: First, Weisberg’s account is not informative about either issue, so the distinction is immaterial to 
diagnosing its shortcomings. Second, my call for a more informative account is satisfied by a concretization 
of feature-sharing that addresses (1) but not (2). Third, Weisberg aims for an account that can address both 
(1) and (2). I thank an anonymous referee of this journal for emphasizing the need to clarify this. 

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On this view, there is nothing both general and informative to say about what feature-

sharing consists in that could justify a general account of the model–world relation in terms 

of feature-sharing. So unless Weisberg can find a way to demonstrate that there are 

elucidating ways to structure and systematize the ways in which context matters for 

determining what counts as a shared feature, at some level of generality, this would seem to 

concede that it is the concrete context of particular modelling activities, rather than 

Weisberg’s general account, that elucidates the relation in virtue of which successful 

models are successful. 

This seems undesirable. While the Tellerian view seems to get many things right—for 

instance, by highlighting that modelling practices across and within the sciences are 

sufficiently heterogeneous to suspect that providing a comprehensive account of some 

single relation in virtue of which successful models are successful is difficult—it is also a 

highly unsatisfactory view. Of course, the details of specific modelling contexts are 

important for clarifying in virtue of what relations to targets successful models are 

successful. Yet this does not imply that nothing general can be said about these relations. 

The challenge that this presents for Weisberg’s account then is to demonstrate that feature-

sharing can be concretized in a way that is not fully context-specific and that can offer 

elucidating insights about the model–world relation that neither the Tellerian appeal to 

context nor earlier, slogan-versions of similarity-based accounts can offer. 

With this background in place, let me turn to examine three potential construals to 

concretize shared features: as identical, sufficiently close, and sufficiently similar features. 

I argue that each of these concretizations is individually insufficient for providing an 

informative and general account of the relation in virtue of which successful models are 

successful. 

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3.1 Identity 

The first construal of feature-sharing says that shared features are those that are identical 

between a model and its target. This yields at least two undesirable consequences. The first 

is that it creates implausibly strong requirements for successful representational 

relationships. For instance, the identity construal suggests that a model’s variables need to 

assume all the same values as their referents in the target in order to share features with the 

target. On this construal, many models would incur significant penalties on their similarity 

to targets in virtue of the various idealizations and approximations that they employ. While 

these models may still be epistemically successful relative to a wide range of plausible 

modelling goals, they will typically fail to be identical to their targets in any respects, be 

that estimated parameters versus true underlying values of parameters or predicted values 

versus observed values (Suárez [2003], pp. 234–5). This suggests that many successful 

models would exhibit surprisingly low similarity values even though they may intuitively 

appear relevantly and highly similar to their targets. This is undesirable, as it would 

undermine Weisberg’s envisioned connection between similarity and epistemic success.2 

In the same vein, the identity construal also renders Weisberg’s account vulnerable to 

some of the concerns that he offers against isomorphism-based accounts. Weisberg ([2013], 

p. 141) worries that these accounts impose exceedingly stringent requirements on 

representational relationships and fail several important desiderata for a persuasive account 

																																																								
2 One might think that given a suitable weighting function, even models with few remaining shared 

features can still be highly similar to targets. However, it is unclear whether models in general only need to 
share few truly important features. Moreover, the importance of features does not determine what the 
appropriate (or feasible) feature-level relations for these features are. So the remaining shared features are not 
necessarily important, nor are important features generally more likely to be shared, or best understood as 
shared, in the restrictive sense of identity. I thank an anonymous referee of this journal for raising this 
important point. 

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of such relationships. One of his main concerns is that many successful models fail to be 

isomorphic to their targets in any important respects ([2013], p. 140). Hence, these models 

would not be considered successful representations, even though they may issue accurate 

predictions, adequate explanations or exhibit other kinds of epistemic utility. Irrespective of 

whether one agrees with this criticism, Weisberg’s concern about exceedingly stringent 

constraints imposed by isomorphism-based accounts would seem to extend to his own 

account under the identity construal because, in many cases, exact identity between 

features seems to imply similarly stringent constraints on feature-level relations, precluding 

potentially many epistemically important feature-level relations from counting as shared 

features. 

A related concern is offered by Parker ([2015]), who suggests that thinking of shared 

features as identical features prompts us to construe features in odd ways. Consider the case 

where a model’s parameter 𝛼 is slightly inaccurately parameterized; say it is off by 0.1 

units due to measurement error. Parker’s worry is that the identity construal would have us 

say that the model and target share a feature that is defined as ‘exhibiting a value of 𝛼 that 

is within 0.1 of the true value of 𝛼 in the target’. Parker ([2015], p. 273) argues that at least 

in some cases it seems more natural to understand shared features as sufficiently similar 

features. In a reply to Parker, Weisberg ([2015], p. 303) clarifies that his conception of 

feature-sharing allows us to think of shared quantitative features not as strictly speaking 

identical features (in the sense of full-on quantitative identity), but rather as features that 

are within some threshold range of each other, thus allowing quantitative features to count 

as shared even when they are not exactly identical. Let me expand on two additional 

construals of shared features in turn, which, I believe, capture Weisberg’s concretization. 

 

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3.2 Sufficient closeness 

The sufficient closeness construal says that shared features are those that are sufficiently 

quantitatively close to each other on some subvenient scale. In other words, models’ 

features must be within some threshold range of corresponding features of the target to 

count as shared. While this seems to avoid the implausibly stringent demands of the 

identity construal, it creates two general problems. 

The first problem is that it constrains the scope of Weisberg’s account to quantitative 

features; those that are operationalizable in terms of some quantitative scale that permits 

evaluation of absolute differences between features. Clearly, not all features are readily 

amenable to such operationalization. For instance, it is difficult to make sense of the idea 

that qualitative or categorical features need to be quantitatively sufficiently close to each 

other. Categorical features such as economic agents exhibiting rational preferences, or 

having quasilinear utility functions are not amenable to treatment in terms of sufficient 

quantitative closeness. Another important case is that of mechanisms, where it seems 

unclear how to determine whether a model’s mechanism is sufficiently quantitatively close 

to the mechanism that governs the phenomena of interest in the target.  

The second, more general problem with the sufficient closeness construal is that it 

raises the question how the ranges are determined that distinguish between sufficient and 

insufficient closeness, and hence between shared and missed features. This is important as 

without providing more details on how these ranges are determined, Weisberg’s account 

would seem unable to tell us whether a given model and its target share features, and hence 

whether they are similar at all and, if so, to what extent. 

A first pass at concretizing the sufficient closeness construal in this respect could be to 

say that modelling goals and background theory, including less fully articulated beliefs 

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about the target, jointly inform or determine these ranges. So modelling goals would tell us 

what counts as epistemic success and background theory would help us determine whether 

certain ranges of closeness will be sufficient for promoting such success. For instance, 

physical background theory might give us reasons to believe that a scale model of an 

airplane in a wind tunnel will be useful for successfully predicting airplanes’ stall angle of 

attack only to the extent that the model’s (scaled) geometrical features are within some 

tightly defined range of closeness compared to corresponding features of the target plane. 

So at least in some cases, our understanding of the target is sufficiently sophisticated to 

give us a clear idea of just how close features need to be in order for epistemic success to 

ensue. 

Even so, there are several reasons to be sceptical about whether this way of fleshing out 

the sufficient closeness construal will yield the account that Weisberg aims to offer. First, 

saying that context-specific factors such as modelling goals and our particular background 

theory of the target will help distinguish sufficiently and insufficiently close features would 

seem to concede to the Tellerian view that it is the concrete context of specific modelling 

activities that will tell us what degree of closeness between features will distinguish 

between shared and missed instances. This raises the question why we need a general 

account like Weisberg’s if it is context-specific information that elucidates the relation in 

virtue of which particular successful models are successful. 

Moreover, context-specific information sufficient to distinguish shared and missed 

features is not always available, so even if Weisberg were to say that the modelling context, 

specifically our background theory of the target, will supply the requisite details for 

distinguishing shared and missed features, this would not help us much if there is no such 

background theory.  

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To illustrate, agent-based financial market models (see, for instance, Le Baron [2006]) 

are used to predict and explain phenomena such as asset pricing or stock market dynamics. 

The agents that figure in these models are characterized by features such as utility 

functions, updating rules and forecasting strategies. Such models consequently exhibit 

various parameters that need to be specified, such as agents’ risk preferences, learning 

speeds or their propensity to adopt other agents’ strategies. These parameters are usually 

calibrated by trying to replicate certain stylized features of the target.  

But just how finely do the models’ parameters need to be tuned, and just how 

accurately must the model replicate known features of the target in order to be able to 

successfully predict or explain in other instances? This seems unclear as there is no 

background theory that tells us, for instance, that if we do not adequately calibrate model 

agents’ learning speed to within a certain threshold, then the model will be unable to issue 

accurate predictions of stock market behaviours. This is not surprising either, as it seems 

that in cases like this, where the target is not well understood, the understanding of the 

target required to determine thresholds for distinguishing shared and missed features 

amounts, in part, to the very understanding that the model is intended to facilitate in the 

first place.  

In such cases, the sufficient closeness construal would seem to say little more about 

how relations of quantitative closeness promote models’ epistemic success beyond offering 

the generic suggestion that they need to be closer rather than less close, and preferably 

quite close, although we generally do not know just how close before the model’s epistemic 

capabilities are compromised. So unlike in the model airplane case, modelling goals and 

background theory do not always offer guidance for setting thresholds of sufficient 

closeness, and it remains unclear how shared and missed features can be distinguished. 

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Weisberg might reply that despite these challenges, there is nevertheless some fact of 

the matter as to which ranges of closeness are sufficient to promote models’ epistemic 

success and that a shared feature is one that conforms to this fact, even if we are not in the 

epistemic position to tell what the ranges are. However, this reply faces several problems of 

its own. 

First, this would seem to beg the question whether feature-sharing is indeed the relation 

in virtue of which successful models are successful. While saying that shared features are 

all and only those that contribute to epistemic success would correctly single out those 

feature-level relations that promote our modelling goals, this trivializes the relation 

between feature-sharing and epistemic success because it fails to tell us whether a given 

feature is shared independently of whether it successfully promotes our goals. It is clear 

that the relation between feature-sharing and epistemic success needs to be specified, at 

least in part, according to criteria that do not presuppose the substantive relation between 

feature-sharing and epistemic success that Weisberg aims to establish. 

Second, when laying out the desiderata for his account, Weisberg emphasizes that the 

similarity metric should be tractable in the sense that it ‘should reflect judgments that 

scientists can actually make, as opposed to asserting that the relation holds between 

inaccessible, hidden features of models and targets’ ([2013], p. 137). This suggests that 

Weisberg aims for an account that enables scientists (and perhaps philosophers) to tell 

whether a given model is similar to its target and to what extent. So by Weisberg’s own 

ambitions, it does not seem enough to suggest that there is some epistemically inaccessible 

fact as to whether a given feature is shared or not, and that reference to such fact gives us 

the desired account of the relation in virtue of which successful models are successful. 

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Third, at least in some cases, the very idea that there is some fact as to whether a 

specific range of closeness singles out features that promote epistemic success seems 

elusive. Consider models with multiple parameters such as the agent-based models outlined 

earlier. For one specific parameter, such as agents’ learning speeds, the question of whether 

certain values are sufficient to promote success will also hinge on the values of other 

parameters, such as agents’ risk preferences. If one of the model’s parameters is grossly 

mis-specified, such as when agents have implausible risk preferences, even exact identity 

in other parameters such as learning speed may not be sufficient to help modellers achieve 

any modelling goal. This is despite the model sharing many features, and thus exhibiting 

high degrees of similarity. This suggests that the way in which ranges of sufficient 

closeness are determined may also need to accommodate such interdependencies.3 But this 

makes matters even more challenging, since we would need to know a great deal about the 

target in order to tell which combinations of parameter values are jointly sufficient to 

render a model epistemically successful. 

I take these concerns to indicate that the sufficient closeness construal is not adequately 

concretized by pointing out that some conjunction of background theory and modelling 

goals will take care of telling us when features are sufficiently close. So without further 

concretization, the sufficient closeness construal, by itself, seems insufficient to accomplish 

Weisberg’s aim of providing an informative account of the relation in virtue of which 

successful models are successful. Let me focus attention on a third construal of shared 

features. 

 

3.3 Sufficient similarity 

																																																								
3 See Parker ([2015]) for a related concern about interdependencies between feature weights. 

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The third construal says that shared features are those that are sufficiently similar to each 

other. For instance, an agent-based financial market model that can approximately replicate 

a certain dynamical feature of the target (for instance a time-series characteristic such as 

leptokurtic returns distributions) may be considered sufficiently similar to the target with 

respect to that feature, and hence share the feature, whereas if it fails to be similar, this will 

yield a missed feature. This way of construing shared features again creates problems. 

If Weisberg accepts the construal, then his analysis remains uninformative because it 

tells us that what it means for models to be similar to their targets is that they are similar in 

certain important respects, not too dissimilar in too many other important respects, while 

they may be similar or dissimilar in any number of unimportant respects. This does not 

elucidate the relation in virtue of which successful models are successful.4 If Weisberg sets 

out to analyse model–world similarity in terms of feature-sharing, and shared features are 

explicated as sufficiently similar features, then this just prompts all the same concerns that 

have affected earlier accounts of model–world similarity. While Weisberg’s account may 

still be taken to offer interesting ideas on how the global similarity of models to targets 

hinges on local, feature-level similarities, as well as interest-relative factors such as feature-

weights, the feature-level similarities would remain a black box, and there do not seem to 

be any accounts of feature-level similarity that offer a remedy for this. 

This does not mean, however, that the sufficient similarity construal is implausible. It 

seems that at least for some kinds of features and some kinds of modelling activities, 

shared features are best understood as sufficiently similar features.  

																																																								
4 See Tal ([2015]) for a related concern about circularity. 

 

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The most general case is that of qualitative features; features that are vaguely defined 

and either do not permit of quantitative operationalization at all (at least not in useful or 

cognitively tractable ways) or are not operationalized in this way. Examples for this type of 

feature are descriptions of shapes and structures, such as saying that DNA strands are 

connected by a T-shaped juncture, complex dynamical features and patterns, as well as 

categorical features such as morphological or behavioural characteristics that are multiply 

realizable. 

If there is no useful way to quantitatively operationalize qualitative concepts such as T-

shapedness of DNA junctions, but such features are considered epistemically relevant, then 

it seems that the question of whether model and target share the feature should be 

understood either as a question of whether they are identical or whether they are similar in 

this respect. 

I take it that construing shared qualitative features as identical features will often be 

difficult because qualitative concepts are usually vaguely defined. If features such as T-

shapedness are vaguely defined, then at least some instances of T-shapedness on the part of 

a model may be epistemically uninteresting but still count as identical and hence shared. 

This would undermine the substantive connection between feature-sharing and epistemic 

success that Weisberg aims to establish. Likewise, if T-shapedness is more narrowly 

defined, then this might exclude epistemically beneficial instances from the realm of shared 

features because the requisite features on the model’s part will fail the definition. This 

would also undermine the connection between feature-sharing and epistemic success, 

because successful models would exhibit fewer feature-sharing relations. This suggests that 

it is difficult to handle qualitative features under the identity construal, since feature 

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semantics would have to be constantly adapted in order to match our judgments of whether 

identity in these features, so defined, promotes epistemic success. 

This problem can be sidestepped by saying that the model and target are sufficiently 

similar with respect to features such as T-shapedness. This allows answering questions 

about how similar model and target are, and whether such similarity is sufficient for 

attaining our modelling goals in two separate steps. I take this to be a strong reason in 

favour of construing shared qualitative features as sufficiently similar features. 

The sufficient similarity construal may also be a useful way to express what it means 

that models and targets share mechanisms. Neither the identity nor the sufficient closeness 

construal seem apt to capture the kinds of relations that need to obtain between models’ and 

targets’ mechanisms in order for models to represent their targets successfully. For 

instance, for many epistemic purposes in social and behavioural sciences it is sufficient to 

model mechanisms at relatively high levels of abstraction intended to capture key 

qualitative causal relationships. This proceeds without making attempts to ensure that all 

causal relationships relevant to the phenomena of interest, but not necessarily relevant for 

the particular epistemic activity at issue, are encoded in the model’s mechanism. In such 

cases, mechanisms may be considered sufficiently similar at some level of abstraction 

relevant to the modeller’s epistemic interests, where sufficient similarity may be further 

explicated in terms of partial identity or partial similarity in components of the mechanisms 

at issue. 

Even so, while at least in some cases the sufficient similarity construal seems to be a 

plausible way to construe feature-sharing relations, for instance when qualitative features 

or mechanisms are at issue, targets are not well understood and background theory and 

sophisticated understanding of the target are unavailable, this construal also faces 

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essentially the same challenges that affect the sufficient closeness construal. Saying that 

(some kinds of) shared features are sufficiently similar features can only yield an 

informative analysis of the relation in virtue of which successful models are successful if it 

says at least something about the distinction between sufficiently and insufficiently similar 

features, and preferably also something on how scientists can determine whether features 

are sufficiently similar. 

As anticipated, there are two important challenges here. First, spelling out whether 

features are sufficiently similar should not beg the question whether sufficiently similar 

features contribute to epistemic success. So it is not enough to say that features are 

sufficiently similar whenever they contribute to epistemic success and insufficiently similar 

otherwise. Second, the sufficient similarity construal would also need to specify what 

sufficient similarity amounts to, at least partly, in general rather than context-specific 

terms. This is to push back on the Tellerian view that it is context-specific information, 

rather than a general account like Weisberg’s, that tells us what it means for models to be 

sufficiently similar on the level of particular features. 

In summary, the above concerns suggest that, at least in its present form, Weisberg’s 

account does not provide an informative analysis of the relation in virtue of which 

successful models are successful that critics of similarity-based accounts have called for 

and that Weisberg promises to offer. Such an account should not bottom out at the level of 

a feature-sharing metric that does not concretize what shared features consist in, but should 

provide more details about the feature-level relations that can constitute instances of shared 

features. As I have argued, however, doing so raises at least three significant challenges. 

The first is about scope: some kinds of feature-sharing relations seem best understood 

in terms of similarity, whereas others might be better understood in terms of sufficient 

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closeness or identity. Each of these relations seems appropriate for some types of cases, but 

none of them seems privileged in the sense of accounting for a majority of feature-sharing 

instances. The second challenge, posed by the Tellerian view, is that pointing to contextual 

factors to help distinguish shared and missed features, without further general analysis, 

would boil down to saying that contextual information is what elucidates the relation in 

virtue of which successful models are successful. This leaves unclear why we need a 

general account like Weisberg’s for this purpose. The third challenge is concerned with 

establishing Weisberg’s envisioned connection between feature-sharing and epistemic 

success in a way that does not beg the question whether shared features, in any of the 

senses outlined above or others, contribute to epistemic success. Together with the second 

challenge, this creates a tension between saying too little about when and how certain 

feature-level relations contribute to epistemic success, and on the other hand relegating this 

important issue to be settled by the contextual details of each particular modelling activity, 

thus leaving unclear how Weisberg’s account substantially contributes to elucidating the 

relation in virtue of which successful models are successful. 

I consider this situation unsatisfactory, which is why I now want to offer some 

constructive suggestions for how Weisberg might revise his account, adopting a pluralistic 

account of feature-sharing instead. I argue that doing so can evade at least the first two of 

the above challenges, and may partly address the third. While I do not aim to fully develop 

a pluralistic revision of Weisberg’s account here, I hope that my sketch of this revision will 

make clear that it offers a promising option for Weisberg as well as a novel perspective on 

the model–world relation more generally. 

 

4 Turning Weisberg’s Account ‘Upside Down’ 

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As the preceding discussion indicates, different kinds of feature level relations seem 

suitable for elucidating the relation in virtue of which successful models are successful 

depending on the kinds of features, models, and modelling goals at issue. This suggests that 

a version of Weisberg’s account that further concretizes the idea of feature-sharing should 

be pluralistic about the kinds of relations that can instantiate shared features. To 

accommodate this pluralism, my key suggestion is to turn Weisberg’s account ‘upside 

down’ and say that the relation in virtue of which successful models are successful, at the 

most general level, is one of feature-sharing instead of similarity. So rather than starting 

from the slogan that successful models are successful because they are highly similar to 

targets, and providing a reductive analysis of similarity in terms of feature-sharing, the idea 

is to begin from (weighted) feature-sharing as the general relation in virtue of which 

successful models are successful and take a pluralistic view concerning how shared 

features can be instantiated: as identities, similarities, sufficient quantitative closeness, 

isomorphisms, and possibly other relations. 

With this menu of relations in place, a pluralistic version of Weisberg’s account helps 

concretize how the suitability of different kinds of feature-level relations changes with 

respect to the kinds of features, models, and modelling goals at issue, as well as our 

understanding of the model and the target. As I have argued in Section 3, each of the 

construals considered there seem useful for some kinds of models, features, and modelling 

goals, but not others. A pluralistic account builds on this by allowing that one and the same 

model can relate to its target in several different ways at once. For instance, a model may 

exhibit (partial) identities in some respects (such as mechanisms) and sufficient quantitative 

closeness or similarities in others (such as parameters and categorical characteristics). This 

seems useful, as different feature-types often seem to require different kinds of relations to 

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express how models successfully relate to their targets on the level of such features. 

Qualitative features seem best understood in terms of similarity, while quantitative ones 

seem better understood in terms of sufficient closeness. Categorical features might be 

understood in terms of identity, and more complex structural features might be best 

captured by isomorphisms. This is how differences in feature-kinds induce differences in 

how feature-sharing is best characterized.  

A pluralistic account is not only responsive to the nature of the features at issue, but 

also to the modelling goals underlying the modelling activity in question. It permits that 

one and the same feature pair can require different kinds of relations depending on general 

contextual factors, such as the kinds of modelling goals and the envisioned fidelity of the 

kinds of inferences that the type of model is intended to facilitate. This, too, seems useful, 

as it is plausible to think that the types of feature-level relations that are suitable to promote 

epistemic success, as well as the required fidelity of such relations, vary with respect to the 

kinds of modelling goals pursued.5 For instance, ambitious goals concerning predictive 

accuracy may require more stringent constraints on what counts as a shared feature, such as 

tight ranges of quantitative closeness, or sufficient similarity in precisely characterized 

qualitative features.  

A pluralistic feature-sharing account hence provides significantly more resources and 

flexibility to flesh out the details of how general characteristics of the modelling context 

																																																								
5  This is in line with Parker’s ([2015], p. 275) suggestion for how Weisberg’s account could be 

concretized. It also seems to address O’Connor and Weatherall’s concern that a single relation like similarity 
is unlikely to accommodate the substantial heterogeneity in how successful models relate to their targets 
([2016], p. 625). Finally, my proposal is also compatible with the pluralistic spirit of Suárez’ ([2015]) 
deflationary account of representation. It would be incompatible, however, with a deflationary view on what 
constitutes successful representational relationships as I maintain that it is possible to spell out general 
constraints on what constitutes such relationships on the feature level. 

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constrain and determine the particular ways in which, and degrees to which, models need to 

share features with their targets in order to be epistemically successful. 

How much does this move to pluralism depart from Weisberg’s initial project, then? 

Very little in some respects, more dramatically in others. For once, a move to pluralism still 

allows Weisberg to retain the core of his account: the (weighted) feature-sharing metric. 

This seems useful as it already captures several important intuitions about the model–world 

relation: that it is gradual; that only relevant instances matter for epistemic success; and 

that contextual factors such as modelling goals and judgments about the relative 

importance of features play important roles in scientific representation. 

At the same time, there are also significant departures involved in a pluralistic revision. 

Weisberg’s metric would not figure as a metric of similarity anymore, but only as a metric 

of the (weighted) degree of feature-sharing between models and targets, which is now the 

principal relation in virtue of which successful models are successful. Importantly, turning 

Weisberg’s account ‘upside down’ involves abandoning the aim of analysing away 

similarity as an uninformative and too general a gesture that, at best, summarizes but does 

not elucidate the manifold ways in which successful models relate to targets. As I have 

argued, this does not seem to be the right way to go. There are cases where it seems highly 

plausible to say that on the level of features, models can (or should) relate to their targets 

by means of similarity, such as when features are qualitative or vaguely defined, desired 

levels of inferential fidelity are modest, targets are not well understood, and strong 

background theory is unavailable. So contra Weisberg as well as many critics of similarity, 

a pluralistic account maintains that similarity has a legitimate role to play in philosophical 

accounts of the model–world relation; not as the principal relation in virtue of which 

successful models are successful, but as one among several other relations that models 

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exhibit vis-à-vis their targets on the level of features. So, similarity should figure as a 

legitimate component of an account that maps out several conceptually distinct kinds of 

feature-level relations that are differentially suitable depending on the general and context-

specific features of different modelling activities. 

As such, similarity will perhaps remain a conceptual primitive, in line with the concerns 

articulated by many of its critics. This does not mean, however, that similarity, on the 

feature level, will be devoid of explanatory power. For instance, saying that a DNA 

computer model used for qualitative prediction of DNA strand displacement kinematics 

needs to be sufficiently similar to its target with respect to features such as T-shapedness of 

specific DNA junctures can provide more detail about what it means to share the feature in 

question than appealing to an unanalysed notion of feature-sharing to account for models’ 

epistemic success. Specifically, saying that feature-level similarity is the appropriate 

feature-level relation for the type of case at hand can now proceed against a richer 

background where similarity is characterized as the appropriate relation for the epistemic 

purposes at issue specifically because T-shapedness is a vaguely defined, but otherwise 

well-understood qualitative feature. This not only provides more detail about what kind of 

feature-level relation is relevant for epistemic success, but can also tell us why similarity, 

rather than some other relation, is the appropriate kind of relation for the type of case at 

hand. The general aspects of the modelling context, such as the nature of the features at 

issue (qualitative), and the types of modelling goals pursued (for instance qualitative 

prediction of DNA strand displacement kinematics) can help us appreciate why, in the type 

of case at hand, similarity in features is all we need for epistemic success to be likely, or, in 

other cases, perhaps all we may hope for. So at least in cases where this is so, feature-level 

similarity can play a legitimate role in an account of the model–world relation. This is how 

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my suggestion to understand feature-sharing as a general relation that can be instantiated by 

several more specific relations, including similarity, departs importantly from Weisberg’s 

initial project. 

Let me expand on how a move to pluralism helps Weisberg address some of the 

challenges I have discussed. The first, immediate advantage is that it helps alleviate 

concerns about scope: none of the construals of feature-sharing considered above seem 

individually sufficient to handle the full gamut of feature-types that models may share with 

their targets. A pluralistic account can address this by offering a menu of such construals 

and by elaborating how their suitability varies with respect to the kinds of features, models, 

and modelling goals at issue. The construals outlined here may serve as a starting point, but 

a concretization of Weisberg’s account is of course not limited to just those relations I have 

examined. Other relations such as isomorphisms suggest themselves as well. This is 

interesting because in contrast to earlier, monistic accounts of the model–world relation in 

terms of either similarity or isomorphism, a pluralistic revision carves out space for each of 

these relations as complementary, rather than rival accounts of how models relate to targets 

on the level of features.6 It thus offers a conciliatory perspective on these relations, and an 

opportunity for their proponents to specify the conditions under which they consider these 

relations to be particularly suitable to capture how successful models relate to their targets. 

The second way in which a move to pluralism helps evade the challenges raised above 

is that it helps Weisberg push back on the Tellerian view. As I have argued, an account of 

feature-sharing should not boil down to saying that it is the specific context of particular 

modelling activities that will tell us what constitutes a shared feature. This would raise the 

																																																								
6 While isomorphism-based accounts can be understood as a way of explicating what it means that 

models are similar to targets, at least some authors, including Weisberg, seem to think that isomorphism and 
similarity-based accounts are rival accounts of the model–world relation. 

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question why we should have an account like Weisberg’s at all if what eventually does the 

work it is supposed to do is context-specific information. To be sure, this would not be a 

problem if the Tellerian view were a satisfactory position. But I do not think that it is. I 

consider the Tellerian view to be an unsatisfactory view as it suggests that there can be no 

general taxonomy of different types of models and modelling strategies that can 

successfully single out epistemically significant commonalities of tokens of these types 

with respect to how models relate to targets and in virtue of what kinds of relations they 

tend to be epistemically successful. It seems that there are ways, even at relatively coarse-

grained levels of classification, to distinguish between different types of modelling 

activities concerning the respects in which, the particular ways in which, and the degrees to 

which models involved in these activities need to be suitably related to targets for epistemic 

success to be likely. 

Weisberg’s account already covers important ground here. For instance, when 

elaborating the role of term weights, Weisberg illustrates how relatively general aspects of 

modelling activities help concretize the respects in which feature-sharing relations need to 

obtain. For instance, Weisberg argues that hyper-accurate modelling requires extensive 

feature-sharing in both attributes and mechanisms, whereas how-possibly modelling 

generally disregards shared mechanisms and focuses on the replication of known attributes 

of the target. ([2013], p. 151) In addition to term weights, the weighting functions included 

in Weisberg’s account further concretize the respects in which feature-sharing relations 

need to obtain. As Weisberg suggests, relatively general background theory can offer 

additional constraints on the kinds of features for which feature-sharing relations are 

particularly important. These constraints, too, can be formulated generally rather than 

applying only to particular modelling contexts. Such distinctions on what terms of the 

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similarity metric and what particular features tend to be more important with respect to 

general differences in the kinds of modelling goals pursued push back on the Tellerian 

view already: they seem sufficient to grant that it is possible to say some interesting and 

general things about the respects in which models used in certain types of modelling 

activities need to share features with their targets. 

At the same time, as I have argued at length, Weisberg’s account still falls short of 

elucidating the ways in which or degrees to which models need to be suitably related to 

targets in those relevant respects in order for epistemic success to be likely. This is because 

knowing what the respects are in which feature-sharing would be important for epistemic 

success does not, by itself, tell us what kinds of relations need to obtain between the 

features in question for them to count as shared, and to promote epistemic success. A 

pluralistic account of feature-sharing can offer help here, as it provides more resources to 

specify what kinds of feature-sharing relations and what degrees of such relations (if 

applicable) seem suitable under varying conditions concerning the types of models, 

features, and modelling goals at issue. 

This can proceed at different levels. For instance, mirroring Weisberg’s elaborations, 

hyper-accurate modelling may require higher fidelity of feature-level relations to promote 

epistemic success. This could mean that mechanisms need to be identical, rather than 

broadly similar, and that parameters need to exhibit high, rather than low degrees of 

quantitative closeness. How-possibly modelling may involve various different relations on 

the level of attributes depending on the specificity of the phenomena one is interested in 

replicating. For instance, for some Schelling-type agent-based models, rough similarities or 

broad ranges of closeness in attributes such as degree of segregation may suffice to serve 

modellers’ envisioned goals. In other cases, where explananda phenomena are more 

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precisely characterized, feature-sharing may require more sophisticated relationships, such 

as sufficient closeness in several quantitative features at once.7 

Such distinctions may proceed at finer-grained levels of classification as well, 

extending to model-types that are individuated not by the general purposes of the modelling 

activity in which they figure but by their type-specific characteristics. For instance, 

parametric structural equation models used in social sciences are constructed to represent 

causal mechanisms by specifying a set of variables and equations that characterize the 

functional relationships among these variables. These models are used for a variety of 

epistemic purposes, such as accurate prediction of the quantitative causal effects of 

counterfactual interventions. For such purposes it often seems that nothing short of identity 

in all parts of the mechanism relevant to the production of the outcome variables of interest 

will be enough for the model to help achieve modellers’ intended goals. The model’s 

parameters, too, will need to be accurately specified for each of the equations that 

determine the relations between the model’s variables. This is in contrast to qualitative 

causal models that may generally address the same overall themes, albeit with significantly 

different intended levels of fidelity. Here, it is perhaps sufficient that models share the most 

important causal relationships that figure in the target’s mechanism, and that the signs of 

these relationships are correctly specified; but there is generally no concern for accurately 

estimating parameters or even more complex non-linear relationships. This suggest that 

qualitative similarity, rather than identity and maximal quantitative closeness seem more 

suitable to capture the relations that are important for achieving the kinds of modelling 

goals for which these models are used. 

																																																								
7 See Bokulich ([2014]) for a discussion of levels of abstraction in how-possibly modelling. 

 

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It is important to recognize that these differences in what constitutes shared features are 

general in nature and apply to a class of models, rather than to individual models on a case-

by-case basis. While all aspects that constrain or determine what counts as a shared feature 

will of course always be instantiated by some concrete context, this does not mean that they 

are context-specific. Rather, many of these aspects will generalize readily over different 

instances of models or modelling goals. It does not seem to matter, for instance, whether 

one is investigating the effect of class size on students’ performance, the clinical 

effectiveness of medical treatment on causally heterogeneous populations, or the effects of 

insecticide-treated bed nets on malaria infection rates. In each of these instances, the type 

of model used and the type of aim pursued by modellers constrain to a significant extent 

the appropriate ways in which models must relate to their targets on the level of features. 

What counts as a shared feature in each of these cases is not constrained by information 

that is idiosyncratic to any particular modelling context, as the Tellerian view would 

suggest, but by general aspects that are instantiated by each of the specific contexts. 

Indeed, the context-specific aspects of modelling activities rarely seem to tell us many 

interesting things about what counts as a shared feature on their own. There is nothing 

about investigating the effects of class size on students’ performance that, by itself, tells us 

what kinds of feature-level relations will bear favourably on the epistemic success of a 

model chosen to investigate this effect. Instead of originating from context-specific aspects, 

such insights will typically be supplied by a conjunction of domain-general theory, our 

understanding of the kinds of models that we use, and general aspects of the modelling 

activity, such as the types of features and modelling goals involved. Of course, context-

specific aspects can supply additional constraints on how models need to be related to their 

targets; think for instance about the context-specific epistemic risks involved in making 

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decisions based on a model’s predictions. However, it seems that such details do not, by 

themselves, tell us what kinds of feature-level relations promote epistemic success. So 

context matters but, pace Teller, not only context matters for providing an account of how 

successful models relate to their targets, and contextual information alone does not always 

yield such an account on its own. 

A pluralistic version of Weisberg’s account then allows us to take a conciliatory view 

on the tension between general accounts like Weisberg’s and the Tellerian view. 

Conciliatory here means that there is a division of labour between them in that up to some 

point, relatively general aspects of modelling activities constrain, but perhaps do not fully 

determine, what counts as a shared feature, and context-specific details will allow us to fill 

in the remaining blanks, if and when such information is available. How this labour will be 

divided, and whether a general and pluralistic version of Weisberg’s account can push back 

on the Tellerian appeal to context by a significant margin, is of course an open question 

that a more fully developed version of this proposal will need to investigate. Even so, 

irrespective of how much exactly such an account succeeds in this respect, it may be 

elucidating in itself to see just where the boundaries of general accounts and the concrete 

details of particular modelling contexts will meet to jointly elucidate the relation in virtue 

of which successful models are successful. 

Finally, let me briefly comment on what a move to pluralism will do for addressing the 

third challenge, namely that in concretizing the conditions under which features are 

sufficiently close or similar, there is the danger of begging the question on whether shared 

features contribute to epistemic success. As I have argued, clarifying what constitutes a 

shared feature must be settled independently of the eventual epistemic success that shared 

features are supposed to bestow on models. Here, a pluralistic revision does not seem to 

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offer much of a remedy. While it provides more resources to elaborate on the relative 

suitability of certain kinds of feature-level relations for certain kinds of models, features, 

and modelling goals, a good chunk of this problem may remain untouched; particularly in 

those recalcitrant cases where background theory and our understanding of the target are 

highly limited. Here, even a pluralistic account may need to bite the bullet and say that 

while there may, in principle, be more specific constraints on what counts as a shared 

feature, these constraints will not always be accessible to scientists and philosophers at 

satisfactorily precise levels. 

In such cases, the context-specific details of a particular modelling activity, too, may be 

insufficient to shed more light on what shared features are. When background theory and 

understanding of the target are limited, it seems that, at least transiently, we may be unable 

to tell what constitutes a shared feature. So even a Tellerian view may need to concede that 

whenever the concrete details of the case will leave it unclear whether a feature is 

sufficiently similar or close before the eventual epistemic success or failure becomes 

apparent, offering an account of the relation in virtue of which these particular successful 

models are successful remains an elusive endeavour. 

This is perhaps not a surprising conclusion, however. In such cases it seems that 

elucidating what sorts of feature-level relations promote epistemic success is among the 

very purposes for which scientists build models in the first place, that is, to develop a 

cumulatively more sophisticated understanding of what works and what does not by means 

of trial and error, rather than by means of antecedent guidance concerning what is already 

understood to render a model successful. In these cases, perhaps the best that philosophers 

interested in the model–world relation can hope for is that eventually, and after the fact, an 

account like Weisberg’s together with the context-specific details of the particular case will 

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help us make sense of why epistemic success ensues, if and when it does. However, this, 

too, can be part of a revised version of Weisberg’s account, in that it may elaborate how 

our understanding of the target bears on our ability to distinguish shared and missed 

features at all. 

This makes clear that a pluralistic revision of Weisberg’s account will not fully evade 

the challenges I have raised here. Despite this, it may still offer an interesting starting point 

for addressing some of them, as well as for thinking more generally about how these 

challenges, and the pluralistic character of the view I have sketched, bear on the prospects 

of providing more informative philosophical accounts of the model–world relation. 

 

5 Conclusions 

I have argued that Weisberg’s weighted feature-sharing account does not provide an 

informative analysis of similarity as the relation in virtue of which successful models are 

successful. I began by highlighting an important vagueness concerning what feature-

sharing consists in. I then examined three construals of how this concept may be 

concretized and argued that they face several important challenges. This made clear that 

none of these off-the-shelf explications can individually render Weisberg’s analysis 

informative about the issues it is intended to elucidate. 

Against this background, I sketched out a pluralistic revision of Weisberg’s account as 

a promising option to improve on this situation. By drawing on the joint resources of 

several, more specific construals of feature-level relations, a pluralistic revision promises to 

offer more insights concerning how general aspects of modelling activities constrain and 

determine the ways in which features need to be shared. Moreover, such a revision 

provides a doubly conciliatory view: first, concerning the tension between general accounts 

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like Weisberg’s and contextual accounts like the Tellerian view; and second, concerning 

similarity and isomorphism-based contenders for a general account of the model–world 

relation. In virtue of this conciliatory character, I hope that this proposal helps stimulate 

future contributions aimed at improving our understanding of the relation in virtue of which 

successful models are successful. 

 
Department of Philosophy 

Durham University 
Durham, UK 

donal.khosrowi@durham.ac.uk 
 
 

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