Epistemic Contributions
of Models: Conditions for
Propositional Learning
François Claveau
Université du Québec à Montréal
Melissa Vergara Fernández
Erasmus University Rotterdam
This article analyzes the epistemic contributions of models by distinguishing
three roles that they might play: an evidential role, a revealing role and a
stimulating role. By using an account of learning based on the philosophical
understanding of propositional knowledge as true justified belief, the paper
provides the conditions to be fulfilled by a model in order to play a determined
role. A case study of an economic model of the labor market—the DMP model—
illustrates the usefulness of these conditions in articulating debates over the epi-
stemic contributions of a given model.
Introduction
Models are powerful tools that can make us learn. Few contemporary ob-
servers of science doubt that, and economists agree; the highest honours of
their discipline go to the most influential model builders. Among a long
list of modellers who are Nobel laureates, we count Peter A. Diamond,
Dale T. Mortensen and Christopher A. Pissarides, who were awarded the
prize in 2010 as a recognition of their work in developing a model of the
labor market—the DMP model.1
While researchers agree that models make significant epistemic contri-
butions in science, judging whether a specific model made us learn is no
We thank Jaakko Kuorikoski, Caterina Marchionni, Julian Reiss, Philippe Verreault-
Julien, Marcel Boumans, two anonymous referees, and participants at MS5 (Helsinki, June
2012), the fourth biennial SPSP Conference (Toronto, June 2013), and the EIPE PhD
seminar for their helpful comments. M. Vergara Fernández is especially indebted to Luis
Mireles-Flores and Attilia Ruzzene. F. Claveau acknowledges the financial support of
SSHRC (767-2009-0001 and 756-2012-0516).
1. DMP stands for the initials of the three modellers.
Perspectives on Science 2015, vol. 23, no. 4
©2015 by The Massachusetts Institute of Technology
doi:10.1162/POSC_a_00181
405
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Epistemic Contributions of Models
easy matter. The recent literature on models, though rich in insights, is
not as helpful as one might hope in dealing with this issue. Much energy
has been spent arguing that models can be highly useful and there are
today lists of what they can do (e.g., Morgan and Knuuttila 2012, p. 73).
Unfortunately, these lists give us little handle when it comes to analyzing
claims about whether we have learned from a specific model and in what
sense.
The main goal of this article is to help with such analysis. In particular,
we highlight three epistemic roles that models can play in our learning
about the world. In addition, we provide conditions that are sufficient
for each role to be actually played by a given model. A secondary contri-
bution of our paper is to connect more tightly the discussion on “learning
from models” to general epistemology. We connect the two by using the
traditional account of propositional knowledge to analyze how models can
help us learn about the world. Our explicit epistemological perspective
allows us to structure the relationship among our three epistemic roles
and to articulate how learning from models fits into a more general picture
of knowledge acquisition.2
The scope of our project must be properly delimited. We do not claim
that the three roles identified are the only ones models might play. We are
also not the first to try to supply conditions for learning from a model. For
instance, the proposals by Alexandrova (2008), that models supply open
formulae and Grüne-Yanoff (2009), that they falsify impossibility hypoth-
eses, can be understood in terms of attempting to provide sufficient
conditions. Yet, the present article goes beyond these contributions by
identifying conditions for a number of epistemic roles and by articulating
these conditions with the help of the traditional account of propositional
knowledge.
Our general account of learning is presented in the next section. We
then discuss our three epistemic roles. Finally, we present a case study
of the DMP model, which is meant to illustrate how our conditions can
help in structuring a fruitful debate over the epistemic contributions of a
given model.
On Learning
To be able to characterize precisely potential epistemic contributions of
models, we need to be clear on what we take learning to be. For the purpose
of this paper, we propose to take learning to be the process of “coming to
2. By using an epistemological concept of learning, we are not suggesting that other
perspectives—e.g., cognitive—are not fruitful or important.
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Perspectives on Science
407
know” (Audi 2011, p. 162), and to rely on the traditional account of knowl-
edge as true justified belief. According to this account, which is about
knowledge of propositions, an agent knows a proposition if and only if three
conditions hold: (i) the proposition is true, (ii) the agent believes the prop-
osition, and (iii) the agent has an appropriate justification for this belief.3
Propositional knowledge is only one type of knowledge, which excludes
other types of knowledge such as knowledge-how (see Fantl 2012). This
restricted focus of ours might be a significant omission when we think
about models since it is very likely that models contribute to know-how
besides contributing to know-that (i.e., propositional knowledge). For
instance, through exercising with models, one might develop abilities to bet-
ter react to various real world happenings much in the same way an aircraft
pilot develops intuitions and reflexes in a flight simulator. Although we rec-
ognize that a significant amount of learning can be related to knowledge-
how, we think that providing explicit conditions for learning in terms of
propositional knowledge is already a significant contribution, to which
we limit ourselves here.
The traditional account of knowledge as true justified belief (KATJB) is
not without its faults. Since Edmund Gettier’s famous article (Gettier
1963), it is largely granted that the three conditions stated above, though
apparently necessary, are not fully sufficient for knowing a proposition.
Once the general structure of Gettier’s counterexamples is understood, it
is easy to produce thought experiments in which a true justified belief
can intuitively not count as knowledge (Zagzebski 1994). Although this
implies that there could be cases that our account would regard as involving
learning—acquiring knowledge—when in fact knowledge is not acquired,
Gettier cases are scarce. By their very nature, Gettier cases can amount to
only a small proportion of the elements in the set of all true justified beliefs
(Hetherington 2011, p. 121). Since our goal is not to provide a definition of
knowledge, an account that reliably, but fallibly, distinguishes between
knowledge and non-knowledge is satisfactory.
There are good reasons to stick to KATJB in this article despite its
drawback. First, the account focuses on what epistemologists still believe
to be the concepts most tightly connected to propositional knowledge:
truth, belief and justification. In fact, most of the recent accounts of prop-
ositional knowledge try to modify KATJB just enough to avoid Gettier
3. Some terms in this definition of knowledge—foremost “truth” and “justification”—
could be given a variety of interpretations. We do not need to commit to specific interpretations
for the purpose of this paper. For the major contending theories of truth and justification see
entries in the Stanford Encyclopedia of Philosophy (e.g., Glanzberg 2013; Ichikawa and Steup 2012)
and readers like Bernecker and Dretske (2000) and Bernecker and Pritchard (2011).
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Epistemic Contributions of Models
cases (Hetherington 2011; Ichikawa and Steup 2012). Second, KATJB is
simpler than most other accounts and the best known. And third, none of
the alternative accounts are free of problems; they all seem to fail to pro-
vide necessary and sufficient conditions for knowledge. There is simply no
account that perfectly distinguishes knowing from not knowing.
What is clear, however, is that knowing is a state of an agent: at a cer-
tain point in time, an agent knows or does not know a proposition. By
contrast, learning is a process of passing from a state of not knowing a
proposition to the state of knowing it—it is coming to know. Thus, we
characterise a process as learning if an agent starts the process lacking
belief or justification (or both) in a true proposition, and ends it with
both belief and justification in the proposition.4 Before turning to models,
we want to discuss the possible instances of learning implicit in the previous
statement.
Learning can involve the process of coming to believe a true proposition.
We want to distinguish between two possible ways in which this process of
belief generation occurs. First, the agent can change her mind—change her
doxastic attitude—with respect to this proposition. In this case, the agent
starts the process either disbelieving the proposition or withholding judg-
ment with respect to it and finishes the process believing it.5 Second, the
agent might start the process without even having a doxastic attitude for
the proposition. Indeed, an agent holds, at any point in time, doxastic
attitudes for only a tiny fraction of all the possible propositions she could
envisage. In the 18th century, no one had a doxastic attitude for the value of
Planck’s constant. The process of coming to believe a true proposition can
thus involve forming a doxastic attitude rather than simply changing it.
In addition to, or instead of, coming to believe, learning can occur
through the process of coming to be justified to believe a proposition.
The concept of justification relies on the distinction between adequate
and inadequate evidence: an agent comes to be justified to hold a certain
doxastic attitude if and only if her evidence for this attitude crosses the
4. In our account, the process of learning ends with knowledge. Some might want to
work with a more permissive account for which learning is “coming closer to knowing”
instead of “coming to know.” The concept of “closeness” on which this alternative account
relies is however difficult to pin down. It leads to difficult questions: Are we learning if we
come to be justified in believing a false proposition? What if we come to believe a true
proposition for entirely crazy reasons? Though we do not try to develop such a weaker
account in this article, it might be possible to do so successfully; we therefore present
our account as supplying only jointly sufficient (but perhaps not jointly necessary) condi-
tions for learning.
5. Here we conceptualise doxastic attitudes in a trichotomous framework: disbelief, with-
hold judgment and belief, but it could also be rephrased in terms of, say, degree of belief.
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Perspectives on Science
409
threshold for adequacy. Evidence for a proposition suggests that the prop-
osition is true; if the evidence is adequate, truth is indicated reliably. But
even adequate evidence is fallible; truth and justification should not be
conflated.
It is helpful to think about epistemic justification in terms of a network
of doxastic attitudes for propositions connected to each other. Propositions
can stand in an evidential relation to each other—believing one proposition
warrants, to some degree, believing another. Since Paula believes that “the
clock indicates 14.00 local time,” she feels confident that “it is not night.”
If we locate the doxastic attitude for the proposition “it is not night” at the
center of our network, Paula’s belief that “the clock indicates 14.00” will be
connected to this central node, together with many other doxastic attitudes.
The set of doxastic attitudes having an evidential relation with the dox-
astic attitude at the center of the network constitutes the evidence for this
attitude. This evidence will be adequate or inadequate depending on prop-
erties of the network, such as its evidential density. This property summa-
rizes the number of doxastic attitudes connected to the central attitude.
The density of Paula’s network centred at the belief in the proposition
“it is not night” would be higher if, on top of believing “the clock indicates
14.00 local time,” she also had a doxastic attitude for “the sun is shining
through the window.”
To sum up, we take learning to be about coming to hold a justified
belief for a true proposition. Learning means that, at the start of the pro-
cess, the agent does not know the proposition. Depending on what the
agent is missing—belief or justification—learning involves either coming
to believe or coming to be justified in believing (or both). In any case, the
process ends with the three conditions for knowledge being met: truth,
belief and justification. In the rest of this paper, this account will be used
to answer the following question: How can models make us learn?
Learning with Models
Something that can be easily granted for most models is that by construct-
ing and manipulating a model, an agent learns propositions about the model
that she works with. We call these model propositions. Morgan (2012) refers
to this learning as “enquiring into the model.”
Two conditions must hold for it to be the case that an agent has learned
with the model about this same model. First, the agent must be in the prop-
er end state: there must be some true model proposition that the agent
justifiably believes. In other words, the agent must, at the end point, know
this proposition. Second, the agent’s knowledge must have been acquired
thanks to the modelling exercise. In particular, there are two relevant
counterfactual dependencies: either the agent would not have believed the
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Epistemic Contributions of Models
proposition had it not been for the activity of generating the model, or she
would not have been justified in believing the proposition (or both).
A model must thus make the agent believe the proposition, or make
the agent be justified in believing the proposition, or both. How does a
model make an agent believe a proposition? Modelling arguably generates
beliefs in the two ways discussed in the previous section. Toying with a
model makes the agent form doxastic attitudes for many model proposi-
tions that were not even on her radar before. That is, prior to the model-
ling exercise, the agent plausibly possessed doxastic attitudes just for a
few model propositions—based on intuitions or theoretical considerations.
Likewise, modelling might also lead the agent to revise previously-held
doxastic attitudes with respect to some model propositions.
Regarding justification, the manipulation of a model typically provides
justification for its model propositions. For instance, the fact that Arrow
and Debreu (1954) derived the existence of an equilibrium in their general
equilibrium model looks like adequate evidence for their belief that “an
equilibrium exists in this model.” It is also plausible to say that they
did not have adequate evidence for their belief in this proposition prior
to their derivation since the effort put in the derivation would make little
sense otherwise. Note that this derivation and the belief that Arrow and
Debreu are competent modellers are solid grounds for observers like us to
grant one aspect of the end-state condition: this model proposition must
be true. In short, in cases like the general equilibrium model of Arrow and
Debreu, it seems implausible to deny that a model contributes to learning
about itself in that agents come to believe and come to be justified in
believing true propositions about it.
Granting that a model makes us learn about itself is obviously not
granting much. Now we turn to how the agent’s learning about a model
can be a stepping-stone to learn about other target systems, especially phe-
nomena in the real world.
Evidential Role: The Model Contributing to Justification
We start with what is perhaps the most contentious—and most discussed—
potential epistemic contribution of models. Roughly, the idea of the evi-
dential role is that model propositions, by contributing to justify real-world
propositions, can contribute to learning about the world.
To begin, let us take the following real-world proposition: “Low employ-
ment protection is a cause of the low unemployment rate in the USA.” At
a certain point in time, an agent might lack justification—might have
inadequate evidence—to believe this proposition and consequently develop
strategies to increase the strength of her evidential network. The agent
might, for example, investigate whether countries with more employment
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Perspectives on Science
411
protection typically have higher unemployment rates. By doing similar
empirical research, she will increase the chance of being justified in holding
her doxastic attitude for the initial proposition.
The question at issue when it comes to discussing the plausibility of an
evidential role for models is whether model propositions can have the same
function of strengthening one’s evidential network for a real-world prop-
osition. There are three conditions that must hold jointly for a model to
play an evidential role. First, an end-state condition: there is a true real-
world proposition p that the agent justifiably believes. Second, there is at
least one model proposition q that is part of the agent’s evidential network
for p. Finally, if the agent did not have the doxastic attitude she has for q,
she would not be justified in believing p. In other words, at least one
model proposition makes a difference to knowledge: given the context,
the doxastic attitude for model proposition q is necessary for justification.6
This condition is meant to rule out situations in which the evidence is
already adequate to justify the belief in p. In such situations, even if it were
granted that a model proposition is part of the evidence for p—the second
condition—there would not be an epistemic contribution, since the model
proposition would be redundant for justification.
Whether and how often the second condition holds for economic
models is the most contentious issue in the discussion of the evidential role
in the literature. An influential view is that some propositions known to be
true of the model are evidence for real-world propositions if, and only if,
the model appropriately isolates the key features of the real-world system
(e.g., Cartwright 1989; Mäki 2009). This view, however, leads some
scholars to a skeptical conclusion (e.g. Reiss 2007; Alexandrova 2008):
it seems that many specific assumptions are built in economic models that
are doing more than cleanly isolating the key features.
Nevertheless, it can be argued that there are ways to avoid the conclu-
sion that propositions about economic models are never part of the evi-
dential network for real-world propositions. To start with, a model can
indicate the falsity of particular types of real-world hypotheses—e.g.,
claims that something can never be the case—even though the model does
not cleanly isolate key features of the real-world (Grüne-Yanoff 2009).
6. In contrast to the counterfactual dependence involved in learning about a model (see
above) and to most of the ones discussed for the other roles below, the counterfactual de-
pendence here is not causal, but rather logical or analytical (Kim 1973, p. 570). At the end
state, the doxastic attitude for the model proposition is necessary for the evidence to believe
p to pass the threshold for adequacy. When counterfactual dependence is causal, assessing it
requires going back in the causal process resulting in knowledge to judge whether this
process (and its end state) have been causally dependent on the model.
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Epistemic Contributions of Models
More generally, asking for a clean isolation of the target’s key features
appears too severe when we think of models as experiments in analogy to
the experiments that we perform on one part of the world in order to learn
about another part of it. For instance, we routinely test drugs on mice to
assess their potential toxicity for humans. We run these experiments
because we think that their results are evidentially relevant to our doxastic
attitudes for claims about drug toxicity for humans. This source of evidence
is far from perfectly reliable—a lethal drug for mice might be beneficial
for humans and vice versa—which comes from the fact that mice do not
share all the key features of a human organism. But it can hardly be denied
that propositions about these experiments are often part of the evidential
network of propositions about humans. The same might hold for models
as credible worlds (Sugden 2000). Model economies are unlike real economies
in many respects, much like mice are unlike humans. But the similarities
shared by the two economies might be enough for model propositions to
be counted as part of the evidence for real-world propositions.
We will not provide here a general, philosophical account of what it is
for a model to be similar to a real economy, similar in the right way such
that model propositions can become part of the evidential network for real-
world propositions. Yet, the conditions provided in this subsection can
help in structuring an argument to the effect that a specific model played,
or not, an evidential role. This usefulness of our framework is illustrated
below in our case study of the DMP model. Our illustration will also show,
however, that these arguments are typically hard to uphold.
Revealing Role: The Model as Hypothesis Generator
We now turn to a potential epistemic contribution of models that is less
discussed and sometimes simply referred to as a heuristic contribution. As
we said above, one arguably learns about the properties of the model by
constructing and manipulating it. In consequence, one comes to have jus-
tified beliefs in a host of true model propositions. One way by which this
initial process can contribute to real-world learning is when some propo-
sition about the model is transposed as a proposition about the world—
that is, as a hypothesis about a target system of interest—and that the
agent, in the end, comes to know this proposition. We would say in such
a situation that the agent comes to form a doxastic attitude for the real-
world hypothesis because of the model.
There are also three conditions to be met by a model to play this role.
First, the end state condition: real-world proposition p is true and the
agent justifiably believes it. The second condition is that there must be
a conceptual connection between the model proposition q and p. More
specifically, propositions q and p predicate the same, or sufficiently similar,
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Perspectives on Science
413
properties to their respective systems of interest. For instance, q could say
that employment protection is a positive cause of the unemployment rate
for model M, and p could say that the USA is such that employment pro-
tection and the unemployment rate are similarly causally related. We do not
want to be overly restrictive on the conceptual connection required since
some amount of interpretation is always necessary in order to take a prop-
erty in a model to be sufficiently similar to a real-world property. It should
however be clear that not any interpretation will do—e.g., propositions
true of Bohr’s model of the atom cannot legitimately be interpreted as
sufficiently close to hypotheses about whether Columbia will have peace
in the near future.
The final condition states a specific counterfactual dependence: if the agent
had not known q, she would not have formed a doxastic attitude for p. In
other words, if the agent had already an epistemic attitude with respect to
the real-world hypothesis or if this real-world hypothesis was bound to be
considered because of other developments—a case of overdetermination—
then the model would not be making an epistemic contribution.
Is there a link between the conceptual exploration discussed in many
commentaries on models (e.g., Hausman 1992, p. 79; Nersessian 2008;
Morgan 2012, pp. 270–72, 368–72) and this revealing role? It is to be
expected that many of the hypotheses revealed by a model come up
through the creation, exploration and clarification of some concepts
through modelling.7 After all, models are widely recognized for their role
in creating, exploring and clarifying concepts. For instance, the advent
of game theory brought many concepts to the forefront, one example
being the distinction between incomplete and imperfect information or,
more famously, a situation having the structure of a prisoner’s dilemma.
However, there is no necessity in the connection between conceptual
exploration and the revealing role; it might well be that some hypotheses
are revealed by further manipulation of a model using well-established
categories.
Stimulating Role: The Model as Stimulus for Empirical Research
Models can suggest more than hypotheses; they can also suggest ways to
increase the density of the evidential network for real-world hypotheses
that the agent cares about. In other words, models can stimulate empirical
7. As we stated above, here we restrict ourselves to propositional learning from models
and say only little about other potential types of learning from models. We already noted
the favourable prospects of an account also looking at procedural learning (i.e., resulting in
know-how). A full epistemological account will also include conceptual learning—the intro-
duction of vocabulary, see Audi (2011, p. 162–163).
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Epistemic Contributions of Models
research. This possibility forms the core of the last potential epistemic role
we want to emphasize.
Part of the purpose of doing empirical research is to come to form dox-
astic attitudes for more real-world propositions (e.g., experimental results)
that are evidentially related to propositions that the agent cares about.
Models can help increase the density of one’s evidential network because
the propositions to investigate in order to justify one’s doxastic attitude for
a hypothesis are not always evident. By toying with the model, researchers
can come to realize that some empirical research would be relevant to
conduct. The role of the model here is thus to stimulate pursuing novel
empirical research.
Again, three conditions need to be satisfied by a model to fulfil this role.
First, the end state condition: a real-world proposition p is true and the
agent justifiably believes it. Second, there is another real-world proposition
r for which the agent has a doxastic attitude, but the research that gener-
ated the agent’s doxastic attitude for r would not have been pursued had it
not been for the modelling exercise. In other words, the model has a causal
influence on the generation of a doxastic attitude for r and it has this
influence through stimulating research.
Finally, a third condition requires that the doxastic attitude for r is a
non-redundant element of the evidential network for p: if it were not for
the doxastic attitude for r, the agent would not be justified in believing p.
The evidential network would not reach the threshold for adequacy if the
agent did not entertain this attitude.
Like for the revealing role, the stimulating role is tightly linked to con-
ceptual exploration, although we should not see conceptual novelty as being
necessary to the revealing role. The link is tight because what makes model-
ling a particularly effective activity at coming up with new ways to inves-
tigate target systems is, perhaps, that modelling makes us conceptualize the
world differently.
An Illustration with the DMP Model
The previous section discussed potential epistemic roles of models. Now
we turn to looking at different claims made in the literature regarding
the epistemic contributions of the DMP model. The main goal of this
section is to illustrate that our epistemic roles neatly dissect various asser-
tions about this model and that they indicate what is required for these
assertions to be true. In addition, we will argue that the DMP model actu-
ally played specific epistemic roles while recognizing that our arguments,
being based on empirical propositions, are disputable.
The origins of the DMP model go back to the end of the 1960s when
many researchers were looking for new “microeconomic foundations of
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Perspectives on Science
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employment and inflation theory” (the title of Phelps et al. 1970). The
core idea embedded in this model is that the labor market is a matching
system with search frictions. There are frictions because, on the supply
side, job seekers are not instantaneously informed about all the job offers
and their associated advantages and, on the demand side, potential em-
ployers have no direct access to all job seekers and their wage expectations.
A match between a job seeker and an employer takes time since they must
find each other. When a match occurs, each side has some bargaining power
since it would be costly for the other side to break the match and go back
to search mode.8
The best way to see the peculiarity of the DMP model is to contrast it
to the main model of the labor market predating it. This earlier model—
still taught in introductory labor economics—depicts the labor market as
a standard neoclassical market with price-taking demand (i.e., firms)
and supply (i.e., potential workers). The two sides of the market are
summarized—as usual—in a downward-sloping demand and an upward-
sloping supply. The quantity of labor actually used and the associated
wage rate (if nothing interferes) is taken to be the intersection of these
two curves—the competitive equilibrium. In this model, unemployment
is interpreted as being caused by factors forcing the wage rate to be higher
than the equilibrium wage rate, thus implying an over-supply of labor at
the given wage. In contrast with the DMP model, there is no idea of time
necessary for a match to occur, and there is no idea of wage bargaining
between the two sides of a match.
Evidential Role
Many economists interpret the DMP model as providing evidence for real-
world claims. For example, the press release accompanying the announce-
ment of the 2010 Prize in Economic Sciences stated that:
The Laureates’ models help us understand the ways in which
unemployment, job vacancies, and wages are affected by regulation
and economic policy. […] One conclusion is that more generous
unemployment benefits give rise to higher unemployment[…]
(Nobelprize.org, 2010b)
This claim can plausibly be interpreted as asserting that the DMP model
played an evidential role with respect to the real-world proposition: “In
8. For a book-length exposition of the model, see Pissarides (2000); for shorter pre-
sentations, see Cahuc and Zylberberg (2004, pp. 517–536) and Nobelprize.org (2010a,
pp. 12–20).
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Epistemic Contributions of Models
real economies, more generous unemployment benefits give rise to higher
unemployment” (henceforth ‘p’).
Did the DMP model play an evidential role with respect to p? In other
words, are the conditions presented in the previous section met? Regarding
the end-state condition, there are reasons to grant that it is met: first, the
vast majority of economists believe the real-world proposition p; second, a rich
literature using different methods and data seems to provide adequate evi-
dence to justify this belief (for surveys, see Fredriksson and Holmlund 2006;
Boeri and van Ours 2008, ch. 11); third, since justification reliably—yet
fallibly—indicates the truth value of a proposition, p is likely to be true.
There are also some reasons to grant that at least one proposition about
the DMP model is part of the evidential network for p (the second condi-
tion of the evidential role). In this case, the most plausible proposition q is
“In the DMP model, more generous unemployment benefits give rise to
higher unemployment,” which is indeed a known property of the model.
Among economists, the argument for the view that q is part of the eviden-
tial network for believing p includes claims about the realisticness of the
model9 and, most importantly, about the fact that many results of the
DMP model concord with results independently obtained with empirical
methods (i.e., a claim about the degree of output validation of the model).
Since the model seems to track the world so well on many aspects, we can,
the argument goes, take truths about it as belonging to the evidential net-
work for real-world propositions like p. Note that this argument does not
imply the dubious claim that one would be justified in believing p on the
sole ground of knowing q. In the present case, q is one element among
many more propositions in the evidential network for p. Other important
propositions are concordant results from statistical analyses of various types
that do not rely on the DMP model (see Claveau 2011).
Finally, there is a compelling reason for why the justification of p coun-
terfactually depends on knowing q (the last condition). This model prop-
osition is a novel model result in the sense that, perhaps surprisingly, the
model of the previous generation (the standard supply and demand model,
see above) did not have the conceptual resources to produce a relationship
between unemployment benefits and unemployment.10 Since the DMP
9. For instance, Pissarides says in his Nobel lecture: “To me, search theory was appealing
as a foundation for a theory of unemployment because it appeared realistic.” (Nobelprize.
org, 2010c) See also Blanchard 2007, pp. 413–14.
10. The closest proposition to q one could get in this earlier model is: “In this model,
higher unemployment benefits decrease employment.” Indeed, generous unemployment
benefits were modelled as decreasing the labour supplied at any wage; thus decreasing
equilibrium employment, not increasing unemployment. See Boeri and van Ours 2008,
pp. 230–34.
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Perspectives on Science
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model has these conceptual resources, it would be pretty devastating if
there was no way to produce a positive relationship between benefits
and unemployment in the various versions of the model. This incapacity
could indicate that the statistical results are all artifacts. By contrast,
knowing q helps support the belief that the empirical results pointing
to a causal link from benefits to unemployment are not all spurious.
Although we side with most economists here in believing that the
DMP model played an evidential role with respect to this specific p, we
readily note that there is room for objections. It could be argued that
the end-state condition is not met because, for instance, the evidential net-
work for p is more sparse and incongruent than we are ready to admit (cf.
Howell 2005). One might also wonder why q should be taken as even a
mildly reliable guide to the truth-value of p given that some elements and
results of the DMP model are quite unlike the world.11 Finally, the ones
reacting against the centrality of the modelling culture in economics (e.g.,
Lawson 1997) might reply that q is redundant, that we have no need of a
model proposition in the evidential network for p. We think that these
objections can be satisfactorily answered, but these answers would require
developments unnecessary for the purpose of this paper.
Revealing Role
The DMP model has been praised for being a great platform to think
about the labor market. For instance, Olivier Blanchard (2006, p. 26),
current chief economist of the International Monetary Fund, wrote that,
compared to the earlier model, the DMP model is a “richer framework
to think about unemployment, a framework based on flows, matching
and bargaining.” Blanchard is here emphasizing the possibility of exten-
sive conceptual exploration through the DMP model. The concepts
brought to the forefront by this model include a clear distinction between
flows and stocks of workers, matching efficiency, search intensity, and wage
bargaining. One way by which this conceptual exploration can result in
propositional learning about the world is the revealing role.
There are many new questions that can be investigated inside the DMP
model but could not in the earlier model: what is the relationship between
the stock of unemployed people and the flows in and out of unemployment?
What determines the speed at which potential workers are matched to
firms? More specifically, what determines the search intensity of unemployed
11. For instance, even proponents of the model take its depiction of bargaining as being
“a very poor description of reality” (Blanchard 2007, p. 414) and recognize that it does not
manage to replicate even something as central as the cyclical fluctuations in unemployment
(Nobelprize.org 2010a, p. 23; Shimer 2005).
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Epistemic Contributions of Models
persons? What matters to the bargaining process between firms and their
potential employees? For the DMP model to play a revealing role, a necessary
condition is that some answers to these or similar questions with respect to
the model be transposed as hypotheses about the real world.
Take, for example, what economists call the entitlement effect of unem-
ployment benefits (Mortensen 1977; Boeri and van Ours, 2008, sec. 11.2.2).
While discussing the evidential role, we said that unemployment benefits
are believed by most economists to increase unemployment, but Mortensen
realised that, in one version of his model, one group of job seekers had shorter
spells of unemployment when unemployment benefits were higher.12 This
group is the one that is not covered by the unemployment benefit system,
but can expect to be covered during its next unemployment spell. Since
getting a job also involves a better future as unemployed, this group has
incentives to find a job faster.
Once this entitlement effect is shown to exist in the model, economists
might entertain a related hypothesis about the world: “Increasing unem-
ployment benefits in a real country will reduce the length of unemployment
spells for at least some uncovered job seekers” (henceforth ‘p’). The DMP
model seems to have played a revealing role with respect to learning p.
The second condition for the revealing role—the conceptual connection—
should be easy to grant in this case. Although the DMP model is highly
idealized, we can locate a group of agents in it corresponding to the real
individuals that are both jobless and unaided by the unemployment insur-
ance system. We can also easily associate a property of the model group to the
lengths of unemployment spells in our real group. The conceptual link
between the entitlement effect in the DMP model and p is thus hard to
question.
Is the end-state condition fulfilled? Many economists—especially
among the ones specialising in labor economics—believe p, which claims
only the existence of entitlement effects among some job seekers. Although
few empirical studies have tested the real-world existence of entitlement
effects, the existing results seem sufficient to justify p.13 And p, given this
evidence and given the weak requirement for the claim, is likely to be true.
Can we also grant the last condition that it would not have occurred to
economists to believe p had it not been for the development of the DMP
model? To the best of our knowledge, p was not entertained prior to the
12. It is because the entitlement effect is dominated by other effects at the aggregate
level that most economists believe that, for a whole economy, unemployment benefits in-
crease unemployment.
13. For instance, Bennmarker et al. (2007) find evidence of an entitlement effect for
men, but not for women.
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modelling work of Mortensen (1977) and there is no parallel literature
today talking about something like p without being aware of Mortensen’s
work. Though we cannot definitively rule out that p was bound to be
entertained soon enough independently of the development of the DMP
model, the available evidence points toward the fulfilment of this last con-
dition too.
Stimulating Role
A contribution of the DMP model might have been to stimulate empirical
research in epistemically valuable directions. The Economic Sciences Prize
Committee claims that the development of the DMP model had this effect.
According to this committee, the contribution was twofold. First, the
model stimulated data collection:
The early microeconomic models of job search initiated new data
collection efforts focusing on individual labour market transitions, in
particular transitions from unemployment to employment.
(Nobelprize.org 2010a, p. 20)
By changing the modelling focus from stocks to flows, the development of
the DMP model stimulated researchers to request (or, less frequently,
actually gather themselves) reliable data on flows.
Second, the DMP model gave impetus, according to the Prize Committee,
to the use and refinement of some empirical methods, most importantly
duration analysis14:
The methodological literature on econometric duration analysis has
expanded substantially over the past couple of decades, a
development that is to a large extent driven by the growth and
impact of microeconomic search theory. (Nobelprize.org 2010a, p. 23)
Can we thus say that the DMP model played a stimulating role? Take, for
instance, the following proposition p: “the [U.S.] private-sector (gross) job
creation rate began declining well before the 2001 recession and continued
to slide until the middle of 2003.” (Davis et al. 2006, p. 24) It can hardly
be doubted that the first and the last conditions hold with respect to p.
To start with, Davis et al. believe p. They base this belief on the analysis
of two data sources: the Job Openings and Labour Turnover Survey (JOLTS;
see Clark and Hyson 2001) and the Business Employment Dynamics (BED)
data (Pivetz et al. 2001). The authors put forward two propositions in their
14. Duration analysis as applied to labor markets empirically studies the length of
unemployment spells and the factors explaining it.
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Epistemic Contributions of Models
analysis. First, “[f]igures 2 and 3 [plotting BED data] show a long down-
ward slide in job creation rates before, during and well after the 2001
recession” (Pivetz et al 2001, 12). Second, “[t]he hires rate [from the
JOLTS] declines from 3.8 per cent of employment in December 2000 to
3.0 per cent in April 2003” (Pivetz et al. 2001, 13). We denote these
two propositions q1 and q2. Note that q1 and q2 are about patterns in data,
while p is directly about the United States. Propositions q1 and q2 constitute
the main evidential ground for p. It is thus hard to deny that believing them
is a necessary condition for being justified to believe p. Furthermore, q1 and
q2 seem to be sufficient to justify believing p. In particular, the fact that
both data sources produce a similar pattern makes it unlikely that this
pattern is driven by an artifact in the data. Finally, since believing p seems
to be justified, we should be tempted to grant the truth of p. In short, it is
highly plausible to affirm that Davis et al. know p (first condition) and that
they do so thanks to q1 and q2 (last condition).
For the DMP model to have played a stimulating role with respect to p,
the second condition must also hold: if the model had not been developed,
would economists be in a position to believe evidential propositions like q1
and q2? The opinion relayed by the Prize Committee (see above) is that the
model is responsible for the collection of new data like the JOLTS and
BED data, and thus, ultimately, for the beliefs in q1 and q2. We have
no substantial reason to reject this opinion—the data collection and the
active development of the methods started after the initial work on the
DMP model and the scholars involved in all these developments had sig-
nificant interactions during the period.
That the DMP model played a stimulating role with respect to learning
p might not be granting much. This proposition is descriptive and it per-
tains to a single country for a specific period of time. But the stimulating
role of the DMP model might become impressive if we can be convinced
that many other propositions were learned through this role. These prop-
ositions will not be necessarily local and descriptive; they could be descrip-
tive generalizations justified by pooling national surveys together or they
could be causal propositions justified by combining duration analysis and
natural experiments. We do not have space to explicitly argue for these
epistemic contributions of the DMP model. We simply note that, if we
grant these contributions, the DMP model would have stimulated a great
deal of learning about real economies.
The same point holds for the evidential and revealing roles. By focusing
on specific propositions, we could have given the impression that these
contributions amount to little. But the overall epistemic contribution of
the DMP model would be impressive if convincing arguments using a
wide array of important real-world propositions could be constructed.
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Conclusions
A model can make us learn in a variety of ways. This paper discussed several
ways by which propositional learning can occur with models. Manipulating
the model can obviously make us learn truths about the model itself. But,
more importantly, a model might also contribute in different ways to make us
learn about the world. We discussed three such ways. First, truths about the
model might be part of the evidence justifying one’s belief in a true real-world
proposition—the evidential role. Second, truths about the model might reveal
real-world hypotheses that turn out to be true and justifiable—the revealing
role. Third, the model might stimulate researchers to undertake new empirical
research, the result of which comes to justify beliefs in some true real-world
propositions—the stimulating role. For each of these roles, we provided and
discussed a list of conditions. We then used this framework to analyze the
praises given to the DMP model.
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