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This is a file in the archives of the Stanford Encyclopedia of Philosophy.
Models in Science
First published Mon Feb 27, 2006
Models are of central importance in many scientific contexts. The
centrality of models such as the billiard ball model of a gas, the Bohr
model of the atom, the MIT bag model of the nucleon, the Gaussian-chain
model of a polymer, the Lorenz model of the atmosphere, the
Lotka-Volterra model of predator-prey interaction, the double helix
model of DNA, agent-based and evolutionary models in the social
sciences, or general equilibrium models of markets in their respective
domains are cases in point. Scientists spend a great deal of time
building, testing, comparing and revising models, and much journal
space is dedicated to introducing, applying and interpreting these
valuable tools. In short, models are one of the principal instruments
of modern science.
Philosophers are acknowledging the importance of models with
increasing attention and are probing the assorted roles that models
play in scientific practice. The result has been an incredible
proliferation of model-types in the philosophical literature. Probing
models, phenomenological models, computational models, developmental
models, explanatory models, impoverished models, testing models,
idealized models, theoretical models, scale models, heuristic models,
caricature models, didactic models, fantasy models, toy models,
imaginary models, mathematical models, substitute models, iconic
models, formal models, analogue models and instrumental models are but
some of the notions that are used to categorize models. While at first
glance this abundance is overwhelming, it can quickly be brought under
control by recognizing that these notions pertain to different problems
that arise in connection with models. For example, models raise
questions in semantics (what is the representational function that
models perform?), ontology (what kind of things are models?),
epistemology (how do we learn with models?), and, of course, in
philosophy of science (how do models relate to theory?; what are the
implications of a model based approach to science for the debates over
scientific realism, reductionism, explanation and laws of nature?).
●1. Semantics: Models and Representation
●1.1 Representational models I: models of phenomena
●1.2 Representational models II: models of data
●1.3 Models of theory
●2. Ontology: What Are Models?
●2.1 Physical objects
●2.2 Fictional objects
●2.3 Set-theoretic structures
●2.4 Descriptions
●2.5 Equations
●2.6 Gerrymandered ontologies
●3. Epistemology: Learning with Models
●3.1 Learning about the model: experiments, thought experiments and simulation
●3.2 Converting knowledge about the model into knowledge about the target
●4. Models and Theory
●4.1 The two extremes: the syntactic and the semantic view of theories
●4.2 Models as independent of theories
●5. Models and Other Debates in the Philosophy of Science
●5.1 Models and the realism versus antirealism debate
●5.2 Model and reductionism
●5.3 Models and laws of nature
●5.4 Models and scientific explanation
●6. Conclusion
●Bibliography
●Other Internet Resources
●Related Entries
Models can perform two fundamentally different representational
functions. On the one hand, a model can be a representation of a
selected part of the world (the ‘target system’). Depending
on the nature of the target, such models are either models of phenomena
or models of data. On the other hand, a model can represent a theory in
the sense that it interprets the laws and axioms of that theory. These
two notions are not mutually exclusive as scientific models can be
representations in both senses at the same time.
Many scientific models represent a phenomenon, where
‘phenomenon’ is used as an umbrella term covering all
relatively stable and general features of the world that are
interesting from a scientific point of view. Empiricists like van
Fraassen (1980) only allow for observables to qualify as such, while
realists like Bogen and Woodward (1988) do not impose any such
restrictions. The billiard ball model of a gas, the Bohr model of the
atom, the double helix model of DNA, the scale model of a bridge, the
Mundell-Fleming model of an open economy, or the Lorenz model of the
atmosphere are well-known examples for models of this kind.
A first step towards a discussion of the issue of scientific
representation is to realize that there is no such thing as
the problem of scientific representation. Rather, there are
different but related problems. It is not yet clear what specific set
of questions a theory of representation has to come to terms with, but
whatever list of questions one might put on the agenda of a theory of
scientific representation, there are two problems that will occupy
center stage in the discussion (Frigg 2006). The first problem is to
explain in virtue of what a model is a representation of something
else. To appreciate the thrust of this question we have to anticipate
a position as regards the ontology of models (which we discuss in the
next section). It is now common to construe models as non-linguistic
entities rather than as descriptions. This approach has wide-ranging
consequences. If we understand models as descriptions, the above
question would be reduced to the time-honored problem of how language
relates to reality and there would not be any problems over and above
those already discussed in the philosophy of language. However, if we
understand models as non-linguistic entities, we are faced with the
new question of what it is for an object (that is not a word or a
sentence) to scientifically represent a phenomenon.
Somewhat surprisingly, until recently this question has not
attracted much attention in twentieth century philosophy of science,
despite the fact that the corresponding problems in the philosophy of
mind and in aesthetics have been discussed extensively for decades
(there is a substantial body of literature dealing with the question of
what it means for a mental state to represent a certain state of
affairs; and the question of how a configuration of flat marks on a
canvass can depict something beyond this canvass has puzzled
aestheticians for a long time). However, some recent publications
address this and other closely related problems (Bailer-Jones 2003,
Frigg 2006, Giere 2004, Suárez 2004, van Fraassen 2004), while
others dismiss it as a non-issue (Callender and Cohen 2006, Teller
2001).
The second problem is concerned with representational styles. It is
a commonplace that one can represent the same subject matter in
different ways. This pluralism does not seem to be a prerogative of the
fine arts as the representations used in the sciences are not all of
one kind either. Weizsäcker's liquid drop model represents
the nucleus of an atom in a manner very different from the shell model,
and a scale model of the wing of an air plane represents the wing in a
way that is different from how a mathematical model of its shape does.
What representational styles are there in the sciences?
Although this question is not explicitly addressed in the literature
on the so-called semantic view of theories, some answers seem to emerge
from its understanding of models. One version of the semantic view, one
that builds on a mathematical notion of models (see Sec. 2), posits
that a model and its target have to be isomorphic (van Fraassen 1980;
Suppes 2002) or partially isomorphic (Da Costa and French 2003) to each
other. Formal requirements weaker than these have been discussed by
Mundy (1986) and Swoyer (1991). Another version of the semantic view
drops formal requirements in favor of similarity (Giere 1988 and 2004,
Teller 2001). This approach enjoys the advantage over the isomorphism
view that it is less restrictive and also can account for cases of
inexact and simplifying models. However, as Giere points out, this
account remains empty as long as no relevant respects and degrees of
similarity are specified. The specification of such respects and
degrees depends on the problem at hand and the larger scientific
context and cannot be made on the basis of purely philosophical
considerations (Teller 2001).
Further notions that can be understood as addressing the issue of
representational styles have been introduced in the literature on
models. Among them, scale models, idealized models, analogical models
and phenomenological models play an important role. These categories
are not mutually exclusive; for instance, some scale models would also
qualify as idealized models and it is not clear where exactly to draw
the line between idealized and analogue models.
Scale models. Some models are basically down-sized or
enlarged copies of their target systems (Black 1962). Typical examples
are wooden cars or model bridges. The leading intuition is that a
scale model is a naturalistic replica or a truthful mirror image of
the target; for this reason scale models are sometimes also referred
to as ‘true models’ (Achinstein 1968, Ch. 7). However,
there is no such thing as a perfectly faithful scale model;
faithfulness is always restricted to some respects. The wooden model
of the car, for instance, provides a faithful portrayal of the
car's shape but not its material. Scale models seem to be a
special case of a broader category of representations that Peirce
dubbed icons: representations that stand for something else because
they closely resemble it (Peirce 1931-1958 Vol. 3, Para. 362). This
raises the question of what criteria a model has to satisfy in order
to qualify as an icon. Although we seem to have strong intuitions
about how to answer this question in particular cases, no theory of
iconicity for models has been formulated yet.
Idealized models. An idealization is a deliberate
simplification of something complicated with the objective of making
it more tractable. Frictionless planes, point masses, infinite
velocities, isolated systems, omniscient agents, or markets in perfect
equilibrium are but some well-know examples. Philosophical debates
over idealization have focused on two general kinds of idealizations:
so-called Aristotelian and Galilean idealizations.
Aristotelian idealization amounts to ‘stripping away’,
in our imagination, all properties from a concrete object that we
believe are not relevant to the problem at hand. This allows us to
focus on a limited set of properties in isolation. An example is a
classical mechanics model of the planetary system, describing the
planets as objects only having shape and mass, disregarding all other
properties. Other labels for this kind of idealization include
‘abstraction’ (Cartwright 1989, Ch. 5),
‘negligibility assumptions’ (Musgrave 1981) and
‘method of isolation’ (Mäki 1994).
Galilean idealizations are ones that involve deliberate distortions.
Physicists build models consisting of point masses moving on
frictionless planes, economists assume that agents are omniscient,
biologists study isolated populations, and so on. It was characteristic
of Galileo's approach to science to use simplifications of this
sort whenever a situation was too complicated to tackle. For this
reason it is common to refer to this sort of idealizations as
‘Galilean idealizations’ (McMullin 1985); another common
label is ‘distorted models’.
Galilean idealizations are beset with riddles. What does a model
involving distortions of this kind tell us about reality? How can we
test its accuracy? In reply to these questions Laymon (1991) has put
forward a theory which understands idealizations as ideal limits:
imagine a series of experimental refinements of the actual situation
which approach the postulated limit and then require that the closer
the properties of a system come to the ideal limit, the closer its
behavior has to come to the behavior of the ideal limit (monotonicity).
But these conditions need not always hold and it is not clear how to
understand situations in which no ideal limit exists. We can, at least
in principle, produce a series of table tops that are ever more
slippery but we cannot possibly produce a series of systems in which
Planck's constant approaches zero. This raises the question of
whether one can always make an idealized model more realistic by
de-idealizing it. We will come back to this issue in section 5.1.
Galilean and Aristotelian idealizations are not mutually exclusive.
On the contrary, they often come together. Consider again the
mechanical model of the planetary system: the model only takes into
account a narrow set of properties and distorts these, for instance by
describing planets as ideal spheres with a rotation-symmetric mass
distribution.
Models that involve substantial Galilean as well as Aristotelian
idealizations are sometimes referred to as ‘caricatures’
(Gibbard and Varian 1978). Caricature models isolate a small number of
salient characteristics of a system and distort them into an extreme
case. A classical example is Ackerlof's (1970) model of the car
market, which explains the difference in price between new and used
cars solely in terms of asymmetric information, thereby disregarding
all other factors that may influence prices of cars. However, it is
controversial whether such highly idealised models can still be
regarded as informative representations of their target systems (for a
discussion of caricature models, in particular in economics, see Reiss
2006).
At this point we would like to mention a notion that seems to be
closely related to idealization, namely approximation. Although the
terms are sometimes used interchangeably, there seems to be a
substantial difference between the two. Approximations are introduced
in a mathematical context. One mathematical item is an approximation of
another one if it is close to it in some relevant sense. What this item
is may vary. Sometimes we want to approximate one curve with another
one. This happens when we expand a function into a power series and
only keep the first two or three terms. In other situations we
approximate an equation by another one by letting a control parameter
tend towards zero (Redhead 1980). The salient point is that the issue
of physical interpretation need not arise. Unlike Galilean
idealization, which involves a distortion of a real system,
approximation is a purely formal matter. This, of course, does not
imply that there cannot be interesting relations between approximations
and idealization. For instance, an approximation can be justified by
pointing out that it is the ‘mathematical pendant’ to an
acceptable idealization (e.g. when we neglect a dissipative term in an
equation because we make the idealizing assumption that the system is
frictionless).
Analogical models. Standard examples of analogical models
include the hydraulic model of an economic system, the billiard ball
model of a gas, the computer model of the mind or the liquid drop
model of the nucleus. At the most basic level, two things are
analogous if there are certain relevant similarities between
them. Hesse (1963) distinguishes different types of analogies
according to the kinds of similarity relations in which two objects
enter. A simple type of analogy is one that is based on shared
properties. There is an analogy between the earth and the moon based
on the fact that both are large, solid, opaque, spherical bodies,
receiving heat and light from the sun, revolving around their axes,
and gravitating towards other bodies. But sameness of properties is
not a necessary condition. An analogy between two objects can also be
based on relevant similarities between their properties. In this more
liberal sense we can say that there is an analogy between sound and
light because echoes are similar to reflections, loudness to
brightness, pitch to color, detectability by the ear to detectability
by the eye, and so on.
Analogies can also be based on the sameness or resemblance of
relations between parts of two systems rather than on their monadic
properties. It is this sense that some politicians assert that the
relation of a father to his children is analogous to the relation of
the state to the citizens. The analogies mentioned so far have been
what Hesse calls ‘material analogies’. We obtain a more
formal notion of analogy when we abstract from the concrete features
the systems possess and only focus on their formal set-up. What the
analogue model then shares with its target is not a set of features,
but the same pattern of abstract relationships (i.e. the same
structure, where structure is understood in the formal sense). This
notion of analogy is closely related to what Hesse calls ‘formal
analogy’. Two items are related by formal analogy if they are
both interpretations of the same formal calculus. For instance, there
is a formal analogy between a swinging pendulum and an oscillating
electric circuit because they are both described by the same
mathematical equation.
A further distinction due to Hesse is the one between positive,
negative and neutral analogies. The positive analogy between two items
consists in the properties or relations they share (both gas molecules
and billiard balls have mass), the negative analogy in the ones they do
not share (billiard balls are colored, gas molecules are not). The
neutral analogy comprises the properties of which it is not known yet
whether they belong to the positive or the negative analogy (do gas
molecules obeying Newton's laws of collision exhibit an approach
to equilibrium?). Neutral analogies play an important role in
scientific research because they give rise to questions and suggest new
hypotheses. In this vein, various authors have emphasized the heuristic
role that analogies play in theory construction and in creative thought
(Bailer-Jones and Bailer-Jones 2002; Hesse 1974, Holyoak and Thagard
1995, Kroes 1989, Psillos 1995, and the essays collected in Hellman
1988).
Phenomenological models. Phenomenological models have been
defined in different, though related, ways. A traditional definition
takes them to be models that only represent observable properties of
their targets and refrain from postulating hidden mechanisms and the
like. Another approach, due to McMullin (1968), defines
phenomenological models as models that are independent of
theories. This, however, seems to be too strong. Many phenomenological
models, while failing to be derivable from a theory, incorporate
principles and laws associated with theories. The liquid drop model of
the atomic nucleus, for instance, portrays the nucleus as a liquid
drop and describes it as having several properties (surface tension
and charge, among others) originating in different theories
(hydrodynamics and electrodynamics, respectively). Certain aspects of
these theories—though usually not the complete theory—are
then used to determine both the static and dynamical properties of the
nucleus.
Concluding remarks. Each of these notions is still somewhat
vague, suffering from internal problems, and much work needs to be
done to tighten them. But more pressing than these is the question of
how the different notions relate to each other. Are analogies
fundamentally different from idealizations, or do they occupy
different areas on a continuous scale? How do icons differ from
idealizations and analogies? At the present stage we do not know how
to answer these questions. What we need is a systematic account of the
different ways in which models can relate to reality and of how these
ways compare to each other.
Another kind of representational models are so-called ‘models
of data’ (Suppes 1962). A model of data is a corrected,
rectified, regimented, and in many instances idealized version of the
data we gain from immediate observation, the so-called raw data.
Characteristically, one first eliminates errors (e.g. removes points
from the record that are due to faulty observation) and then present
the data in a ‘neat’ way, for instance by drawing a smooth
curve through a set of points. These two steps are commonly referred to
as ‘data reduction’ and ‘curve fitting’. When
we investigate the trajectory of a certain planet, for instance, we
first eliminate points that are fallacious from the observation records
and then fit a smooth curve to the remaining ones. Models of data play
a crucial role in confirming theories because it is the model of data
and not the often messy and complex raw data that we compare to a
theoretical prediction.
The construction of a data model can be extremely complicated. It
requires sophisticated statistical techniques and raises serious
methodological as well as philosophical questions. How do we decide
which points on the record need to be removed? And given a clean set
of data, what curve do we fit to it? The first question has been dealt
with mainly within the context of the philosophy of experiment (see
for instance Galison 1997 and Staley 2004). At the heart of the latter
question lies the so-called curve fitting problem, which is that the
data themselves do not indicate what form the fitted curve should
take. Traditional discussions of theory choice suggest that this issue
is settled by background theory, considerations of simplicity, prior
probabilities, or a combination of these. Forster and Sober (1994)
point out that this formulation of the curve fitting problem is a
slight overstatement because there is a theorem in statistics due to
Akaike which shows (given certain assumptions) that the data
themselves underwrite (though not determine) an inference concerning
the curve's shape if we assume that the fitted curve has to be chosen
such that it strikes a balance between simplicity and goodness of fit
in a way that maximizes predictive accuracy. (Further discussions of
data models can be found in Chin and Brewer 1994, Harris 2003, and
Mayo 1996).
In modern logic, a model is a structure that makes all sentences of
a theory true, where a theory is taken to be a (usually deductively
closed) set of sentences in a formal language (see Bell and Machover
1977 or Hodges 1997 for details). The structure is a
‘model’ in the sense that it is what the theory represents.
As a simple example consider Euclidean geometry, which consists of
axioms—e.g. ‘any two points can be joined by a straight
line’—and the theorems that can be derived from these
axioms. Any structure of which all these statements are true is a model
of Euclidean geometry.
A structure S= <U, O, R> is
a composite entity consisting of (i) a non-empty set Uof
individuals called the domain (or universe) of S, (ii) an
indexed set O(i.e. an ordered list) of operations on
U (which may be empty), and (iii) a non-empty indexed set
R of relations on U. It is important to note that
nothing about what the objects are matters for the definition of a
structure—they are mere dummies. Similarly, operations and
functions are specified purely extensionally; that is,
n-place relations are defined as classes of
n-tuples, and functions taking narguments are
defined as classes of (n+1)-tuples. If all sentences of a
theory are true when its symbols are interpreted as referring to
either objects, relations, or functions of a structure S,
then Sis a model of this theory.
Many models in science carry over from logic the idea of being the
interpretation of an abstract calculus. This is particularly pertinent
in physics, where general laws—such as Newton's equation
of motion—lie at the heart of a theory. These laws are applied
to a particular system—e.g. a pendulum—by choosing a
special force function, making assumptions about the mass distribution
of the pendulum etc. The resulting model then is an interpretation (or
realization) of the general law.
There is a variety of things that are commonly referred to as
models: physical objects, fictional objects, set-theoretic structures,
descriptions, equations, or combinations of some of these. However,
these categories are neither mutually exclusive nor jointly exhaustive.
Where one draws the line between, say, fictional objects and
set-theoretical structures may well depend on one's metaphysical
convictions, and some models may fall into yet another class of things.
What models are is, of course, an interesting question in its own
right, but, as briefly indicated in the last section, it has also
important implications for semantics and, as we will see below, for
epistemology.
Some models are straightforward physical objects. These are commonly
referred to as ‘material models’. The class of material
models comprises anything that is a physical entity and that serves as
a scientific representation of something else. Among the members of
this class we find stock examples like wooden models of bridges,
planes, or ships, analogue models like electric circuit models of
neural systems or pipe models of an economy, or Watson and
Crick's model of DNA. But also more cutting edge cases,
especially from the life sciences, where certain organisms are studied
as stand-ins for others, belong to this category.
Material models do not give rise to any ontological difficulties over
and above the well-known quibbles in connection with objects, which
metaphysicians deal with (e.g. the nature of properties, the identity
of objects, parts and wholes, and so on).
Many models are not material models. The Bohr model of the atom, a
frictionless pendulum, or isolated populations, for instance, are in
the scientist's mind rather than in the laboratory and they do
not have to be physically realized and experimented upon to perform
their representational function. It seems natural to view them as
fictional entities. This position can be traced back to the German
neo-Kantian Vaihinger (1911), who emphasized the importance of
fictions for scientific reasoning. Giere has recently advocated the
view that models are abstract entities (1988, 81). It is not entirely
clear what Giere means by ‘abstract entities’, but his
discussion of mechanical models seems to suggest that he uses the term
to designate fictional entities.
This view squares well with scientific practice, where scientists
often talk about models as if they were objects, as well as with
philosophical views that see the manipulation of models as an
essential part of the process of scientific investigation (Morgan
1999). It is natural to assume that one can manipulate something only
if it exists. Furthermore, models often have more properties than we
explicitly attribute to them when we construct them, which is why they
are interesting vehicles of research. A view that regards models as
objects can easily explain this without further ado: when we introduce
a model we use an identifying description, but the object itself is
not exhaustively characterized by this description. Research then
simply amounts to finding out more about the object thus
identified.
The drawback of this suggestion is that fictional entities are
notoriously beset with ontological riddles. This has led many
philosophers to argue that there are no such things as fictional
entities and that apparent ontological commitments to them must be
renounced. The most influential of these deflationary accounts goes
back to Quine (1953). Building on Russell's discussion of
definite descriptions, Quine argues that it is an illusion that we
refer to fictional entities when we talk about them. Instead, we can
dispose of these alleged objects by turning the terms that refer to
them into predicates and analyse sentences like ‘Pegasus does
not exist’ as ‘nothing pegasizes’. By eliminating
the troublesome term we eschew the ontological commitment they seem to
carry. This has resulted in a glaring neglect of fictional entities,
in particular among philosophers of science. Fine (1993), in a
programmatic essay, draws attention to this neglect but does not offer
a systematic account of how fictions are put to use in
science.
An influential point of view takes models to be set-theoretic
structures. This position can be traced back to Suppes (1960) and is
now, with slight variants, held by most proponents of the semantic
view of theories. Needless to say, there are differences between
different versions of the semantic view (van Fraassen, for instance,
emphasizes that models are state-space structures); a survey of the
different positions can be found in Suppe (1989, Ch. 1). However, on
all these accounts models are structures of one sort or another (Da
Costa and French 2000). As models of this kind are often closely tied
to mathematized sciences, they are sometimes also referred to as
‘mathematical models’. (For a discussion of such models in
biology see Lloyd 1984 and 1994.)
This view of models has been criticized on different grounds. One
pervasive criticism is that many types of models that play an important
role in science are not structures and cannot be accommodated within
the structuralist view of models, which can neither account for how
these models are constructed nor for how they work in the context of
investigation (Cartwright 1999, Downes 1992, Morrison 1999). Another
charge held against the set-theoretic approach is that it is not
possible to explain how structures represent a target system which
forms part of the physical world without making assumptions that go
beyond what the approach can afford (Frigg 2006).
A time-honored position has it that what scientists display in
scientific papers and textbooks when they present a model are more or
less stylized descriptions of the relevant target systems (Achinstein
1968, Black 1962).
This view has not been subject to explicit criticism. However, some
of the criticisms that have been marshaled against the syntactic view
of theories equally threaten a linguistic understanding of models.
First, it is a commonplace that we can describe the same thing in
different ways. But if we identify a model with its description, then
each new description yields a new model, which seems to be
counterintuitive. One can translate a description into other languages
(formal or natural), but one would not say that one hereby obtains a
different model. Second, models have different properties than
descriptions. On the one hand, we say that the model of the solar
system consists of spheres orbiting around a big mass or that the
population in the model is isolated from its environment, but it does
not seem to make sense to say this about a description. On the other
hand, descriptions have properties that models do not have. A
description can be written in English, consist of 517 words, be printed
in red ink, and so on. None of this makes any sense when said about a
model. The descriptivist faces the challenge to either make a case that
these arguments are mistaken or to show how to get around these
difficulties.
Another group of things that is habitually referred to as
‘models’, in particular in economics, is equations (which
are then also termed ‘mathematical models’). The
Black-Scholes model of the stock market or the Mundell-Fleming model
of an open economy are cases in point.
The problem with this suggestion is that equations are syntactic
items and as such they face objections similar to the ones put forward
against descriptions. First, one can describe the same situation using
different co-ordinates and as a result obtain different equations; but
we do not seem to obtain a different model. Second, the model has
properties different from the equation. An oscillator is
three-dimensional but the equation describing its motion is not.
Equally, an equation may be inhomogeneous but the system it describes
is not.
The proposals discussed so far have tacitly assumed that a model
belongs to one particular class of objects. But this assumption is not
necessary. It might be the case that models are a mixture of elements
belonging to different ontological categories. In this vein Morgan
(2001) suggests that models involve structural as well as narrative
elements (‘stories’, as she calls them).
Models are vehicles for learning about the world. Significant parts
of scientific investigation are carried out on models rather than on
reality itself because by studying a model we can discover features of
and ascertain facts about the system the model stands for; in brief,
models allow for surrogative reasoning (Swoyer 1991). For instance, we
study the nature of the hydrogen atom, the dynamics of populations, or
the behavior of polymers by studying their respective models. This
cognitive function of models has been widely acknowledged in the
literature, and some even suggest that models give rise to a new style
of reasoning, so-called ‘model based reasoning’ (Magnani
and Nersessian 2002, Magnani, Nersessian and Thagard 1999). This leaves
us with the question of how learning with a model is possible.
Hughes (1997) provides a general framework for discussing this
question. According to his so-called DDI account, learning takes place
in three stages: denotation, demonstration, and interpretation. We
begin by establishing a representation relation
(‘denotation’) between the model and the target. Then we
investigate the features of the model in order to demonstrate certain
theoretical claims about its internal constitution or mechanism; i.e.
we learn about the model (‘demonstration’). Finally, these
findings have to be converted into claims about the target system;
Hughes refers to this step as ‘interpretation’. It is the
latter two notions that are at stake here.
Learning about a model happens at two places, in the construction
and the manipulation of the model (Morgan 1999). There are no fixed
rules or recipes for model building and so the very activity of
figuring out what fits together and how it does so affords an
opportunity to learn about the model. Once the model is built, we do
not learn about its properties by looking at it; we have to use and
manipulate the model in order to elicit its secrets.
Depending on what kind of model we are dealing with, building and
manipulating a model amounts to different activities demanding a
different methodology. Material models seem to be unproblematic as they
are commonly used in the kind of experimental contexts that have been
discussed extensively by philosophers of science (e.g. we put the model
of a car in the wind tunnel and measure its air resistance).
Not so with fictional models. What constraints are there to the
construction of fictional models and how do we manipulate them? The
natural response seems to be that we answer these questions by
performing a thought experiment. Different authors (e.g. Brown 1991,
Gendler 2000, Norton 1991, Reiss 2003, Sorensen 1992) have explored
this line of argument but they have reached very different and often
conflicting conclusions as to how thought experiments are performed and
what the status of their outcomes is (for details see the entry on
thought experiments).
An important class of models is of mathematical nature. In some
cases it is possible to derive results or solve equations analytically.
But quite often this is not the case. It is at this point where the
invention of the computer had a great impact, as it allows us to solve
equations which are otherwise intractable by making a computer
simulation. Many parts of current research in both the natural and
social sciences rely on computer simulations. The formation and
development of stars and galaxies, the detailed dynamics of high-energy
heavy ion reactions, aspects of the intricate process of the evolution
of life as well as the outbreak of wars, the progression of an economy,
decision procedures in an organization and moral behavior are explored
with computer simulations, to mention only a few examples (Hegselmann
et al. 1996, Skyrms 1996).
What is a simulation? Simulations characteristically are used in
connection with dynamic models, i.e. models that involve time. The aim
of a simulation is to solve the equations of motion of such a model,
which is designed to represent the time-evolution of its target system.
So one can say that a simulation imitates a (usually real) process by
another process (Hartmann 1996, Humphreys 2004).
It has been claimed that computer simulations constitute a genuinely
new methodology of science or even a new scientific paradigm (Humphreys
2004, Rohrlich 1991, Winsberg 2001 and 2003, and various contributions
to Sismondo and Gissis 1999). Although this contention may not meet
with univocal consent, there is no doubt about the practical
significance of computer simulations. When standard methods fail,
computer simulations are often the only way to learn something about a
dynamical model; they help us to ‘extend ourselves’
(Humphreys 2004), as it were. In situations in which the underlying
model is well confirmed and understood, computer experiments may even
replace real experiments, which has economic advantages and minimizes
risk (as, for example, in the case of the simulation of atomic
explosions). Computer simulations are also heuristically important.
They may suggest new theories, models and hypotheses, for example based
on a systematic exploration of a model's parameter space
(Hartmann 1996).
But computer simulations also bear methodological perils. They may
provide misleading results because due to the discrete nature of the
calculations carried out on a digital computer they only allow for the
exploration of a part of the full parameter space; and this subspace
may not reveal certain important features of the model. The severity
of this problem is somehow mitigated by the increasing power of modern
computers. But the availability of more computational power also may
have adverse effects. It may encourage scientists to swiftly come up
with increasingly complex but conceptually premature models, involving
poorly understood assumptions or mechanisms and too many additional
adjustable parameters (for a discussion of a related problem in the
context of individual actor models in the social sciences see Schnell
1990). This may lead to an increase in empirical adequacy—which
may be welcome when it comes, for example, to forecasting the
weather—but not necessarily to a better understanding of the
underlying mechanisms. As a result, the use of computer simulations
may change the weight we assign to the various goals of science. So it
is important not to be carried away with the means that new powerful
computers offer and to thereby place out of sight the actual goals of
research.
Once we have knowledge about the model, this knowledge has to be
‘translated’ into knowledge about the target system. It is
at this point that the representational function of models becomes
important again. Models can instruct us about the nature of reality
only if we assume that (at least some of) the model's aspects
have counterparts in the world. But if learning is tied to
representation and if there are different kinds of representation
(analogies, idealizations, etc.), then there are also different kinds
of learning. If, for instance, we have a model we take to be a
realistic depiction, the transfer of knowledge from the model to the
target is accomplished in a different manner than when we deal with an
analogue, or a model that involves idealizing assumptions.
What are these different ways of learning? Although numerous case
studies have been made of how certain specific models work, there do
not seem to be any general accounts of how the transfer of knowledge
from a model to its target is achieved (this with the possible
exception of theories of analogical reasoning, see references above).
This is a difficult question, but it is one that deserves more
attention than it has gotten so far.
One of the most perplexing questions in connection with models is
how they relate to theories. The separation between models and theory
is a very hazy one and in the jargon of many scientists it is often
difficult, if not impossible, to draw a line. So the question is: is
there a distinction between models and theories and if so how do they
relate to one another?
In common parlance, the terms ‘model’ and
‘theory’ are sometimes used to express someone's
attitude towards a particular piece of science. The phrase
‘it's just a model’ indicates that the hypothesis at
stake is asserted only tentatively or is even known to be false, while
something is awarded the label ‘theory’ if it has acquired
some degree of general acceptance. However, this way of drawing a line
between models and theories is of no use to a systematic understanding
of models.
The syntactic view of theories, which is an integral part of the
logical positivist picture of science, construes a theory as a set of
sentences in an axiomatized system of first order logic. Within this
approach, the term model is used in a wider and in a narrower sense. In
the wider sense, a model is just a system of semantic rules that
interpret the abstract calculus and the study of a model amounts to
scrutinizing the semantics of a scientific language. In the narrower
sense, a model is an alternative interpretation of a certain calculus
(Braithwaite 1953, Campbell 1920, Nagel 1961, Spector 1965). If, for
instance, we take the mathematics used in the kinetic theory of gases
and reinterpret the terms of this calculus in a way that they refer to
billiard balls, the billiard balls are a model of the kinetic theory of
gases. Proponents of the syntactic view believe such models to be
irrelevant to science. Models, they hold, are superfluous additions
that are at best of pedagogical, aesthetical or psychological value
(Carnap 1938, Hempel 1965; see also Bailer-Jones 1999).
The semantic view of theories (see e.g. van Fraassen 1980, Giere
1988, Suppe 1989, and Suppes 2002) reverses this standpoint and
declares that we should dispense with a formal calculus altogether and
view a theory as a family of models. Although different version of the
semantic view assume a different notion of model (see above) they all
agree that models are the central unit of scientific theorizing.
One of the most perspicuous criticisms of the semantic view is that
it mislocates the place of models in the scientific edifice. Models are
relatively independent from theory, rather than being constitutive of
them; or to use Morrison's (1998) slogan, they are
‘autonomous agents’. This independence has two aspects:
construction and functioning (Morgan and Morrison 1999).
A look at how models are constructed in actual science shows that
they are neither derived entirely from data nor from theory. Theories
do not provide us with algorithms for the construction a model; they
are not ‘vending machines’ into which one can insert a
problem and a models pops out (Cartwright 1999, Ch. 8). Model building
is an art and not a mechanical procedure. The London model of
superconductivity affords us with a good example of this relationship.
The model's principal equation has no theoretical justification
(in the sense that it could be derived from electromagnetic or any
other fundamental theory) and is motivated solely on the basis of
phenomenological considerations (Cartwright et al. 1995). Or, to put it
another way, the model has been constructed ‘bottom up’ and
not ‘top down’ and therefore enjoys a great deal of
independence from theory.
The second aspect of the independence of models is that they perform
functions which they could not perform if they were a part of, or
strongly dependent on, theories.
Models as complements of theories. A theory may be
incompletely specified in the sense that it imposes certain general
constraints but remains silent about the details of concrete
situations, which are provided by a model (Redhead 1980). A special
case of this situation is when a qualitative theory is known and the
model introduces quantitative measures (Apostel 1961). Redhead's
example for a theory that is underdetermined in this way is axiomatic
quantum field theory, which only imposes certain general constraints
on quantum fields but does not provide an account of particular
fields.
While Redhead and others seem to think of cases of this sort as
somehow special, Cartwright (1983) has argued that they are the rule
rather than the exception. On her view, fundamental theories such as
classical mechanics and quantum mechanics do not represent anything at
all as they do not describe any real world situation. Laws in such
theories are schemata that need to be concretized and filled with the
details of a specific situation, which is a task that is accomplished
by a model.
Models stepping in when theories are too complex to
handle. Theories may be too complicated to handle. In such a case
a simplified model may be employed that allows for a solution (Apostel
1961, Redhead 1980). Quantum chromodynamics, for instance, cannot
easily be used to study the hadron structure of a nucleus, although it
is the fundamental theory for this problem. To get around this
difficulty physicists construct tractable phenomenological models
(e.g. the MIT bag model) that effectively describes the relevant
degrees of freedom of the system under consideration (Hartmann
1999). The advantage of these models is that they yield results where
theories remain silent. Their drawback is that it is often not clear
how to understand the relationship between the theory and the model as
the two are, strictly speaking, contradictory.
A more extreme case is the use of a model when there are no theories
at all available. We encounter this situation in all domains, but it is
particularly rampant in biology and economics where overarching
theories are often not to be had. The models that scientists then
construct to tackle the situation are sometimes referred to as
‘substitute models’ (Groenewold 1961).
Models as preliminary theories. The notion of models as
substitutes for theories is closely related to the notion of a
developmental model. This term has been coined by Leplin (1980), who
pointed out how useful models were in the development of early quantum
theory and is now used as an umbrella notion covering cases in which
models are some sort of a preliminary exercises to theory.
A closely related notion is the one of probing models (also
‘study models’ or ‘toy models’). These are
models which do not perform a representational function and which are
not expected to instruct us about anything beyond the model itself. The
purpose of these models is to test new theoretical tools that are used
later on to build representational models. In field theory, for
instance, the so-called φ4-model has been studied
extensively not because it represents anything real (it is well-known
that it doesn't) but because it serves several heuristic
functions. The simplicity of the φ4-model allows
physicist to ‘get a feeling’ for what quantum field
theories are like and to extract some general features that this simple
model shares with more complicated ones. One can try complicated
techniques such as renormalization in a simple setting and it is
possible to get acquainted with mechanisms—in this case
symmetry breaking—that can be used later on (Hartmann 1995).
This is true not only for physics. As Wimsatt (1987) points out, false
models in genetics can perform many useful functions, among them the
following: the false model can help to answer questions about more
realistic models, provide an arena for answering questions about
properties of more complex models, ‘factor out’ phenomena
that would not otherwise be seen, serve as a limiting case of a more
general model (or two false models may define the extreme of a
continuum of cases in which the real case is supposed to lie), or it
can lead to the identification of relevant variables and the estimation
of their values.
The debate over scientific models has important repercussions for
other debates in the philosophy of science. The reason for this is that
traditionally the debates over scientific realism, reductionism,
explanation, and laws of nature were couched in terms of theories,
because only theories were acknowledged as carriers of scientific
knowledge. So the question is whether, and if so how, discussions of
these matters change when we shift the focus from theories to models.
Up to now, no comprehensive model-based accounts of any of these issues
have been developed; but models did leave some traces in the
discussions of these topics.
It has been claimed that the practice of model building favors
antirealism over realism. Antirealists point out that truth is not the
main goal of scientific modeling. Cartwright (1983), for instance,
presents several case studies illustrating that good models are often
false and that supposedly true theories might not help much when it
comes to understanding, say, the working of a laser.
Realists deny that the falsity of models renders a realist approach to
science impossible by pointing out that a good model, thought not
literally true, is usually at least approximately true. Laymon (1985)
argues that the predictions of a model typically become better when we
relax idealizations (i.e. de-idealize the model), which he takes to
support realism (see also McMullin 1985, Nowak 1979 and Brzezinski and
Nowak 1992).
Apart from the usual complaints about the elusiveness of the notion
of approximate truth, antirealists have taken issue with this reply for
two (related) reasons. First, as Cartwright (1989) points out, there is
no reason to assume that one can always improve a model by adding
de-idealizing corrections. Second, it seems that the outlined procedure
is not in accordance with scientific practice. It is unusual that
scientists invest work in repeatedly de-idealizing an existing model.
Rather, they shift to a completely different modeling framework once
the adjustments to be made get too involved (Hartmann 1998). The
various models of the atomic nucleus are a case in point. Once it has
been realized that shell effects are important to understand various
phenomena, the (collective) liquid drop model has been put aside and
the (single-particle) shell model has been developed to account for
these findings. A further difficulty with de-idealization is that most
idealizations are not ‘controlled’. It is, for example, not
clear in what way one has to de-idealize the MIT-Bag Model to
eventually arrive at quantum chromodynamics, the supposedly correct
underlying theory.
A further antirealist argument, the ‘incompatible models
argument’, takes as its starting point the observation that
scientists often successfully use several incompatible models of
one and the same target system for predictive purposes
(Morrison 2000). These models seemingly contradict each other as they
ascribe different properties to the same target system. In nuclear
physics, for instance, the liquid drop model explores the analogy of
the atomic nucleus with a (charged) fluid drop, while the shell model
describes nuclear properties in terms of the properties of protons and
neutrons, the constituents of an atomic nucleus. This practice appears
to cause a problem for scientific realism. Realists typically hold
that there is a close connection between the predictive success of a
theory and its being at least approximately true. But if several
theories of the same system are predictively successful and if these
theories are mutually inconsistent, they cannot all be true, not even
approximately.
Realists can react to this argument in various ways. First, they can
challenge the claim that the models in question are indeed predictively
successful. If the models aren't good predictors, the argument is
blocked. Second, they can defend a version of perspectival realism
(Giere 1999, Rueger 2005) according to which each model reveals one
aspect of the phenomenno in question, and when taken together a full
(or fuller) account emerges. Third, realists can deny that there is a
problem in the first place because scientific models, which are always
idealized in one way or another and therefore strictly speaking false,
are just the wrong vehicle to make a point about realism. Finally, one
can urge that all representation, the everyday no less than the
scientific, involves idealization so that trading in suitable
idealizations is what it is to know about an independent objective
world (Teller 2004).
The multiple-models problem mentioned in the last section raises the
question of how different models are related. Evidently, multiple
models for the same target system do not generally stand in a deductive
relation as they often contradict each other. Given that most of these
models seem to be indispensable to the practice of science, a simple
picture of the organization of science along the lines of Nagel's
(1961) model of reduction or Oppenheim and Putnam's (1958)
pyramid picture does not seem plausible.
Some have suggested (Cartwright 1999, Hacking 1983) a picture of
science according to which there are no systematic relations that hold
between different models. Some models are tied together because they
represent the same target system, but this does not imply that they
enter into any further relationships (deductive or otherwise). We are
confronted with a patchwork of models, all of which hold ceteris
paribus in their specific domains of applicability (see also the
papers collected in Falkenburg and Muschik 1998).
Some argue that this picture is at least partially incorrect because
there are various interesting relations that hold between different
models or theories. These relations range from controlled
approximations over singular limit relations (Batterman 2004) to
structural relations (Gähde 1997) and rather loose relations
called stories (Hartmann 1999; see also Bokulich 2003). These
suggestions have been made on the basis of cases studies (for instance
of so-called effective quantum field theories, see Hartmann 2001) and
it remains to be seen whether a more general account of these relations
can be given and whether a deeper justification for them can be
provided (e.g. within a Bayesian framework).
It is widely held that science aims at discovering laws of nature.
Philosophers, in turn, have been faced with the challenge of
explicating what laws of nature are. According to the two currently
dominant accounts, the best systems approach and the universals
approach, laws of nature are understood to be universal in scope,
meaning that they apply to everything that there is in the world. This
take on laws does not seem to square with a view that assigns models a
center stage in scientific theorizing. What role do general laws play
in science if it is models that represent what is happening in the
world and how are models and laws related?
One possible response is to argue that laws of nature govern
entities and processes in a model rather than in the world. Fundamental
laws, on this approach, do not state facts about the world but hold
true of entities and processes in the model. Different variants of this
view have been advocated by Cartwright (1983, 1999), Giere (1999), and
van Fraassen (1989). Surprisingly, realists about laws do not seem to
have responded to this challenge and so it remains an open question
whether (and if so how) a realistic understanding of laws and a
model-based approach to science can be made compatible.
Laws of nature play an important role in many accounts of
explanation, most prominently in the deductive-nomological model and
the unification approach. Unfortunately, these accounts inherit the
problems that beset the relationship between models and laws. This
leaves us with two options. Either one can argue that laws can be
dispensed with in explanations, an idea which is employed in both van
Fraassen's (1980) pragmatic theory of explanation and approaches
to causal explanation such as Woodward's (2003). According to the
latter, models are tools to find out about the causal relations that
hold between certain facts or processes and it is these relations that
do the explanatory job. Or one can shift the explanatory burden on
models. A positive suggestion along these lines is Cartwright's
so-called ‘simulacrum account of explanation’, which
suggests that we explain a phenomenon by constructing a model that fits
the phenomenon into the basic framework of a grand theory (1983, Ch.
8). On this account, the model itself is the explanation we seek. This
squares well with basic scientific intuitions but leaves us with the
question of what notion of explanation is at work (see also Elgin and
Sober 2002).
Models play an important role in science. But despite the fact that
they have generated considerable interest among philosophers, there
remain significant lacunas in our understanding of what models are and
of how they work.
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[Please contact the author with suggestions.]
laws of nature |
scientific explanation |
scientific realism |
scientific unity |
thought experiments
Acknowledgments
We would like to thank Nancy Cartwright, Paul Humphreys, Julian Reiss,
Elliott Sober, Chris Swoyer, and Paul Teller for helpful comments and
suggestions on a draft of this entry.
Copyright © 2006by
Roman Frigg
<r.p.frigg@lse.ac.uk>
Stephan Hartmann
<s.hartmann@uvt.nl>