Philippe Huneman, Mael Lemoine. Introduction: the plurality of modeling

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History and Philosophy of the LifeSciences ISSN 0391-9714 HPLSDOI 10.1007/s40656-014-0002-5

Introduction: the plurality of modeling

Philippe Huneman & Maël Lemoine

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IN TRO DUCT IO N

Introduction: the plurality of modeling

Philippe Huneman • Mael Lemoine

Received: 28 November 2013 / Accepted: 16 March 2014

� Springer International Publishing AG 2014

Abstract Philosophers of science have recently focused on the scientific activity

of modeling phenomena, and explicated several of its properties, as well as the

activities embedded into it. A first approach to modeling has been elaborated in

terms of representing a target system: yet other epistemic functions, such as pro-

ducing data or detecting phenomena, are at least as relevant. Additional useful

distinctions have emerged, such as the one between phenomenological and mech-

anistic models. In biological sciences, besides mathematical models, models now

come in three forms: in vivo, in vitro and in silico. Each has been investigated

separately, and many specific problems they raised have been laid out. Another

relevant distinction is disciplinary: do models differ in significant ways according to

the discipline involved—medicine or biology, evolutionary biology or earth sci-

ence? Focusing on either this threefold distinction or the disciplinary boundaries

reveals that they might not be sufficient from a philosophical perspective. On the

contrary, focusing on the interaction between these various kinds of models, some

interesting forms of explanation come to the fore, as is exemplified by the papers

included in this issue. On the other hand, a focus on the use of models, rather than

on their content, shows that the distinction between biological and medical models

is theoretically sound.

P. Huneman (&) � M. Lemoine

Institut d’Histoire et de Philosophie des Sciences et des Techniques, 13 rue du Four,

75006 Paris, France

e-mail: [email protected]

M. Lemoine

e-mail: [email protected]

M. Lemoine

Universite de Tours, 10 Bd Tonnelle, 37000 Tours, France

M. Lemoine

INSERM U930, avenue Monge, batiment D, 37100 Tours, France

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HPLS

DOI 10.1007/s40656-014-0002-5

Author's personal copy

Keywords Models � Explanation � Simulation � Model organisms � Biology �Medicine � In vivo, in vitro, in silico � Simulation � Disciplinary boundaries

As mathematician, economist and physicist John von Neumann put it in a

provocative way 60 years ago: ‘‘The sciences do not try to explain, they hardly even

try to interpret, they mainly make models’’ (quoted in Brody and Vamos 1995,

p. 628). Leaving aside the more controversial first part of this verdict, it is today

almost unanimously acknowledged that all sciences indeed make models of the

phenomena, in order to describe them in a way which makes salient explanatory

features regarding at least the questions we are asking. Models can be analytical, as

sets of equations (e.g. the Navier–Stokes equations in hydrodynamics) or, since two

decades, computer models, in the form of cellular automata, agent based models,

etc.—i.e., in silico models. Clearly, the latter have been pervasively diffusing

through scientific culture in the past 60 years, and it is arguably this feature by

which the 21st century differs from earlier scientific practice.

But in the life sciences there are also ‘‘in vivo models’’, which are organisms

whose study is supposed to provide an understanding of some living phenomena

common across several species or taxa: most famous is Drosophila, the fruit fly used

by geneticists, then there is also the mouse Mus musculus, the nematode

Caenorhabditis elegans, the plant Arabidopsis thaliana, and all the bacteria such

as Escherichia coli. All these organisms are used, for example, in experimenting on

the molecular mechanisms of cellular metabolism, in testing candidate pharmaco-

logical treatments of diseases ranging from cancer to depression, in understanding

how normal and pathological processes differ, or in investigating long term

evolutionary dynamics, which would not be possible with larger organisms since

they have longer life cycles. Those organisms are called ‘‘model organisms’’ and

raise specific epistemological problems. What exactly is ‘‘modeling’’, and what does

‘‘modeling’’ mean in the case of in vivo models? How reliable is the knowledge of

the target class of organisms—often taken to encompass all living beings—gained

through experimenting on the model species? What are the uses of such models?

Finally, biological and medical sciences are also relying on ‘‘in vitro’’ studies,

which isolate specific living processes and reproduce them in laboratory conditions,

a method regularly used in biochemistry, cell biology, molecular genetics, or

screening processes in early phases of the development of a new drug. Here, what is

going on in test tubes is supposed to model the real processes taking place in

organisms or in nature in general, whereas many conditions of these real processes

are simply neglected, at least provisionally; above all, one neglects the fact that they

take place in a living organism rather than in test tubes. This method stimulated

criticisms from the beginnings of physiology in the eighteenth century, where

vitalist physicians accused experimental biologists of studying mere artefacts, since

those phenomena are part of the whole organism in their natural environment, from

which the scientist abstracts away. For instance the entry ‘‘Observation’’ in the

Encyclopedie, written by the physician Jean-Joseph Menuret de Chambaud, makes a

detailed case that biomedical sciences should resort to ‘‘observation’’ rather than

P. Huneman, M. Lemoine

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‘‘experimentation’’ (Menuret de Chambaud 1765); and hundred and seventy years

later the book Aufbau des Organismus, an influential treatise of holistic physiology

written by the neurologist Kurt Goldstein, provided a sophisticated version of this

argument against direct inference from in vitro models as well as laboratory

organisms (Goldstein 1939). However, countless are the results acquired since two

centuries thanks to in vitro modeling.

For a long time philosophy of science focused on theories—and whether they

uncover laws of nature—according to a framework defined by logical positivism in

the 1930s. Since the 1970s increasing doubts have been cast upon this framework,

alternative views of science flourished, such as the semantic view of theories, that saw

scientific theories as sets of models rather than logical constructs based on general

statements capturing general laws of nature and allowing for nomological-deductive

explanations in the style favored by Hempel (1965), Suppes (1960, 1989), van

Fraassen (1980). Subsequently, philosophers of science turned to studying the

epistemological nature of scientific modeling. They made significant advances about

the kind of knowledge models provide (Wimsatt 1987; Frigg 2010; Nersessian 2008;

Morgan and Morrison 1999), their relationships with theories, laws of nature or

experiments (Giere 1988) and causal inference (Woodward 2003) as well as the

consequences of adopting a conception of models upon the debates about realism

versus instrumentalism, or the criteria and importance of reasonable assumptions in

designing models (Morrison 2000; Strevens 2009). They distinguished mathematical

and simulated models, identified epistemic values such as genericity, realism or

precision (Levins 1966), and strove to characterize the kinds of epistemic trade-offs

proper to different types of models (Matthewson and Weisberg 2009). They

questioned cases where there are several different models for the same phenomenon,

and what this means for explanatory pluralism (Mitchell 2003; Giere 2006), but also

considered inverse cases where several different phenomena have a common model,

as is the case, for example, in behavioral ecology and microeconomics, which can

both be modeled through game theory (Maynard-Smith 1982). Robustness analysis,

as a way to cope with the plurality of models has been scrutinized (Wimsatt 1987).

The specificities of computer models have stimulated an important literature on their

characterization, their construction and their validation (Winsberg 2010; Grune-

Yanoff and Weirich 2010). The study of models has become a rich and ever-growing

field in philosophy of science, especially since a decade, and conferences of societies

such as the Society for the Study of Science in Practice are almost wholly devoted to

such issues.

The very concept of model acquired therefore several layers of complexity, and it

is clear now that the various models scientists refer to pertain in fact to different

concepts. Moreover, considering the scientific talk on models in the light of recent

philosophical discussions, it becomes salient that several conceptual distinctions are

needed to account for the wealth of meanings this term can have in the sciences.

Among them, one important distinction holds between what is usually called

‘‘phenomenological models’’, concerning patterns, and what one often calls

‘‘mechanistic models’’, that rather aim at capturing causes or processes. The

question of the explanatory role of models may therefore differ for each of these

kinds. Moreover, general issues such as the nature of the representative virtues of

Plurality of modeling

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models, confirmation and validation, therefore have to be addressed against the

background of this distinction, which holds across all natural and social sciences,

including life sciences. This is obviously meaningful for epistemological issues, as

well as for metaphysical issues such as instrumentalism versus realism in

interpreting models.

However, the use of the aforementioned three different kinds of models—

in vitro, in vivo, in silico—in the life sciences brings to the fore many particular

issues, since scientific knowledge in this case is usually produced through the

entanglement of all three kinds of models. To be sure, model organisms in particular

raise many specific problems, such as: how do we choose in practice, and how

should we choose in principle, our model organisms (Leonelli and Ankeny 2012)?

In the medical sciences, animal models are traditionally evaluated through their

validity, mostly ‘‘face validity’’, ‘‘predictive validity’’ and ‘‘construct validity’’, an

informal distinction raising as many questions as it solves (Belzung and Lemoine

2011); while face validity addresses the overall resemblance of the model organism

to the manifested phenomenon—usually, a syndrome, predictive validity addresses

the power of the model to predict what will happen in the target—this often

corresponds to the therapeutic promises of a tested treatment, and construct validity

is about what resemblance between the two is the right one, i.e., the relevant and

explanatory one—usually the resemblance of both the model and the target to an

hypothesis on the pathophysiology of the disease at hand. Another typical problem

concerns how we can generalize from experiments on this organism, to teachings

about the whole species, a genus, a family, a clade, or even (as it is the case in

molecular genetics) all living beings. Schaffner (1993) has emphasized the

polytypic-paradigmatic nature of such models in the life science—an exemplar

defines a class of individual organisms by a given set of features, such that no

individual presents all features, and no feature is present in all organisms. Greek

physicians had coined terms like ‘idiopathy’—to refer to the singular form a disease

can take in an individual organism—and ‘idiosyncrasy’—to refer to the singular

mix of the humors characterizing the temperament or constitution of an individual.

Interestingly, Claude Bernard used the term in the experimental context to refer to

uncontrolled particularities of one organism (Bernard 1865).

Given that the living world has been shaped by evolution, and that the key

properties of beings which evolve under natural selection are diversity and variety,

it would not be reasonable to expect many universally shared properties across

extant taxonomic groups; therefore, the issue of the scope of what we can learn from

a model organisms is crucial. Philosopher Richard Burian drew attention to these

questions on model organisms in a seminal paper in (1993), and research has been

done ever since (e.g. Weber 2005; Gayon 2006). LaFolette and Shanks (1995)

famously distinguished between causal analogical models and hypothetical

analogical models, and argued against the possibility of animal models being the

former, hence limiting dramatically the possibility of drawing any conclusion; a

conclusion much criticized in Steel (2008), who sets forth the condition of a well-

formed extrapolation from animal organisms in the context of biomedical research.

But a lot of work remains to be done in order to understand how model organisms

interact with in vitro models, as well as with computer simulation and mathematical


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frameworks, in order to produce knowledge. Moreover, new emerging roles for

models, an increasing pressure of society against experimentation on animals, the

development of GMOs for research purposes, or new types of chemical treatments,

are all rapidly modifying the conditions for their relevant use. To take one example,

the appearance of biopharmaceuticals—i.e., macromolecules obtained by bioengi-

neering rather than molecules obtained through traditional chemistry—is challeng-

ing the world of animal experimentation. Experiments on TGN1412, a monoclonal

antibody destined to treat leukemia, that passed every test on animal models but

produced dramatic multi-organ failure in all humans it was tested on even at a much

lower dose, have raised interesting questions on the design of experimentation on

animal models. Such macromolecules are generally designed in vitro through

bioengineering, in such a way as to conform to the best possible affinity with their

human target, calculated in silico and chosen among billions of other molecules. Yet

as this target in the human body does not have the same role as, or even does not

exist, in mice, the model organism has to be ‘‘humanized’’ for the experiment to

have any effect (cf. Maugeri and Blasimme 2011). Now, where has this argument

failed in the case of TGN1412? Has the wrong target been chosen in silico or is it

that the mice were not humanized in a proper way? How can we draw conclusions

from testing on a genetically modified, ‘‘humanized’’ mouse? What is the level of

‘‘absence of effects’’ in animals necessary to warrant safe testing on humans?

These are all questions about what is called ‘‘translational medicine’’ that

philosophers can contribute to significantly. Above all, understanding the nature,

good use and integration of models will enhance our ability to unify the kinds of

knowledge acquired in various ways in different biomedical fields, a very important

task given the increasing level of specialization and diversification of current

biomedical sciences. The rough overview we provided gives an idea of the

relevance and timeliness of the topic of modeling in the life sciences. Not only

philosophers of science, but also theoretical and experimental biologists, as well as

clinical researchers, will benefit from a deeper and richer understanding of the

variety, connections, overlaps and antagonisms of models in life sciences. Having a

firmer grasp on the role, scope and limits of our various models, building bridges

between fields or even setting criteria for clinical research are expected outcomes of

these investigations.

The papers in this special issue of History and Philosophy of the Life Sciences

result from a selection presented at the Second European Advanced Seminar in the

Philosophy of Life Sciences that was held at the Brocher Foundation (Geneva) in

September 2012. This seminar, entitled ‘‘In Vivo, ex Vivo, in Vitro, in Silico:

Models in the Life Sciences’’, was organized by six European Institutions in the

field of philosophy of science: the ESRC Centre for Genomics in Society,

University of Exeter (Exeter), the European School for Molecular Medicine

(Milan), the Institut d’Histoire de la Medicine et de la Sante (Geneva), the Institut

d’Histoire et de Philosophie des Sciences et des Techniques, Paris 1-Pantheon

Sorbonne (Paris), the Konrad Lorenz Institute for Evolution and Cognition Research

(Altenberg), and the Max-Planck-Institut for the History of Science (Berlin).

In ‘‘What good are abstract and what-if models? Lessons from the Gaia

hypothesis’’, Sebastien Dutreuil investigates the various modeling practices that are


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used to implement the Gaia Hypothesis—initially proposed by Lovelock and

Margulis in the 1980s—in ecology and geology, especially when it comes to the

recent field called Earth System Sciences, whose general program has been inspired

by aspects of the Gaia hypothesis. He characterizes the kind of knowledge provided

by the so-called Daisyworld computer models, as well as their scope and limits; one

of the important lessons he draws from this examination is that these models can

answer questions of the sort ‘‘What if Life had not taken place on Earth?’’, and that

such questions can be—like this example—very abstract, but also quite specific.

With regard to the Gaia Hypothesis, they are used to test one claim that would

otherwise be difficult to put to test, namely the claim that Life conditions the

habitability of Earth. Dutreuil shows against numerous critiques that the fact that

Daisyworld is not modeling the actual world cannot be seen as an objection against

its scientific relevance.

After having considered the way some modeling practices in Earth System

Sciences resemble some practices in evolutionary biology, and how, taken together,

they differ from other classical models that are untouched by the Gaia Hypothesis,

the next paper, while questioning the choice of model organisms, also makes a case

for more domain-specific differentiations. ‘‘Model organisms in evo-devo: promises

and pitfalls of the comparative approach’’, by Alessandro Minnelli and Jan Baedke,

handles the case of model organisms in evolutionary developmental biology (evo-

devo). It first argues that, given that this field is more concerned with commonalities

in life than with variation (as compared to evolutionary biology), the choice of

model organisms and the inferences based from them raise issues that are distinct

from the ones met by researchers in evolutionary biology or in molecular biology.

Especially, this paper takes into account a recent turn in evo-devo, in the course of

which researchers stopped considering interspecific and intraspecific variation as

noise and started to view it as intrinsically interesting and relevant to the phenomena

under focus. Considering this individual variation—and not only inter-clade

commonalities—connects the research agenda to general issues about evolvability,

and for the authors, the issue of choosing model organisms in evo-devo has to take

this larger research program into account. The paper finally suggests that choice of

evo-devo’s model organisms should be integrated within the practice of epigenetic

landscapes, and benefit from some insights that these provide about the effects of

variation.

A third paper considers evolutionary biology, but, whereas the first two papers

are mostly considering one area of this huge scientific field—ecology and its links to

Earth sciences, or evo-devo—the third one is rather driven by a concern for a kind

of modeling across several domains within evolutionary biology and ecology.

‘‘Mapping an expanding territory: computer simulations in evolutionary biology’’,

by Philippe Huneman, questions the use of computer models in evolutionary

biology, providing a taxonomy of the various simulations, mostly distinguishing

between those that are used to test hypotheses, and those that are mostly

exploratory. The latter are pervasive in the field called Artificial Life. The paper

argues that these simulations raise specific issues that can best be dealt with within

the framework proposed by Levins (1966) for model building in ecology, namely,


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the conception according to which models have to make trade-offs between

different epistemic values they could aim at.

Then, according to Mayr’s famous dichotomy, we switch from ‘‘evolutionary’’ to

‘‘functional’’ biology, since the last two papers in this special issue do not consider

evolutionary biology at all, but rather physiology and medicine. The first one, ‘‘The

Role of Models in the Process of Epistemic Integration: The Case of the Reichardt

Motion Detector’’, by Daniel Brooks, appears as a case study, which allows the

author to investigate how various kinds of models can produce knowledge through

their integration. In his seminal 1966 paper, Levins famously claimed that the truth

stands at the ‘‘intersection of independent lies’’—these ‘‘lies’’ being the various

models, since each of them conceals (through idealization, simplifications,

parameter choices, etc.) an aspect of the reality in order to be tractable and

representative. Even if Levins’ paper has been quite influential in the methodology

of ecology and, beyond that, in the philosophical debates about the epistemology of

mathematical modeling, this notion of ‘‘intersection’’ sounded quite obscure. In the

recent literature the concept of ‘‘integration’’ (Mitchell 2003; O’Malley and Soyer

2011) came to the fore, and in a way it promises some understanding of the nature of

this truthful ‘‘intersection’’ of lies. Brooks’ paper pursues such examination in the

specific case study of the research on motion detectors. It investigates the various

tasks a model can perform in a constantly changing ‘‘problem agenda’’ that makes

for integration in science: instead of being what collaborating scientists complete

progressively, models are rather epistemic resources addressing empirical problems

like prediction as well as conceptual problems like allowing simpler representations

of the phenomenon.

The last paper, ‘‘From Replica to Instruments: Animal Models in Biomedical

Research’’ by Pierre-Luc Germain, turns to medical sciences. He contrasts the

traditional view on the role of animal models, i.e., as surrogates for experimentation

on humans, which should therefore resemble the target as much as possible, with an

instrumental view of the role of models, in which observed changes in animal

systems serve to detect or measure something. Although these are distinct and not

exclusive roles, the prevalence of the latter is underestimated by philosophers.

Focusing on the latter, Germain provides a ‘‘lab coat’’ view of what models are for,

that is, mainly, for testing, counterintuitive as this provocative suggestion may

seem. Experimenting on models is less and less about reasoning analogically, it is

more and more about investigating interactions at the molecular level, and thereby

supposed to be valid whatever the organism they occur in. Besides, models tend to

be explored for themselves, notwithstanding their potential use. In the end, Germain

argues for the idea that ‘‘replica’’ and ‘‘instrument’’ are just different roles of

models, raising the question whether they are essential to the definition of models at

all.

These five papers cover a set of research fields that range from earth sciences and

ecology through evolutionary biology to medical research; they also focus on

various aspects of the three kinds of models—in vivo, in vitro, in silico; and they

often question the intertwining between them. The papers differ not only regarding

the discipline they consider; functional as well as evolutionary biology sensu Mayr

are represented, and medicine is also under focus in one paper. What joins these


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papers is that they adopt a perspective on the plurality of modeling practices in the

life sciences. Some of them mostly focus on one kind of model in a rather large

discipline, e.g. Dutreuil on in silico models in ecology and earth sciences, or Minelli

and Baedke or Germain on in vivo models in evo-devo and in medicine. Others

consider several kinds of models at once, such as in vitro and in vivo used together

when scientists investigate a specific phenomenon, like Brooks on the case study of

motion detectors.

Even though the papers are very different in method and focus, it is worth

concluding this introduction by emphasizing some common threads between them,

as well as the conceptual issues on which they may differ or conflict—possibly

revealing deep differences between fields or disciplines regarding the practice of

modeling. There is indeed a growing literature in philosophy about models, and the

philosophy of life sciences itself is getting more and more interested in the topic.

These remarks only intend to indicate to what extent the present special issue may

positively contribute to this literature.

A first common thread throughout these papers is the idea that conceiving of

models in terms of representations and target systems—the idea being that models

represent in some way a target system, and the philosophical question concerns the

nature of this relationship—may not be sufficient to capture all types of models in

the life sciences and their uses. In some cases, as made clear by Dutreuil and

Huneman, there is no actual target system, since the model as simulation produces

the data; or, in other cases, in medical contexts the model functions rather as a

detector, as Germain argues. It is also clear from Minelli and Baedke’s paper that

model organisms in Evo-Devo are not supposed to represent some definite system

that would for example be the vertebrate developmental scheme—because they are

also intended to account for inter-specific and intraspecific variation.

Second, the typology that guided us in organizing this special issue—which is the

usual tripartition expressed by the Latin expressions in vivo, in vitro, in silico—

appears in the end to be too coarse-grained to make sense of the plurality of

modeling practices in the various fields of life science. Especially, a diversification

of in silico models seems to result from these investigations: Dutreuil as well as

Huneman show that computer modeling is multifaceted in many areas of

evolutionary biology and ecology—not only because they implement very different

kinds of organisms, but above all because they are used in very different roles: they

may either implement hypotheses, test them, or even provide data, and they can

either be surrogates to analytic models, or simply allow modeling where no

equations are available. On the other hand, comparing the papers of Minelli and

Baedke with that of Germain, which both deal with model organisms, gives the idea

that the very category of model organism should undergo further specifications.

Germain shows that in the case study he handles, model organisms are rather

instruments than representations, in the sense that they may function rather like

pregnancy tests than like diagrams or equations; in evo-devo, argue Minelli and

Baedke, model organisms are not only used to represent a specific type of

developmental process, but also should make salient variations and the effects of

variations, which is a different use than model organisms such as Escherichia coli or

Drosophila melanogaster in evolutionary biology.


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This leads to a third theme that surfaces across several papers, namely, the fact

that some explanations in the life sciences may require the articulation and merging

of distinct types of modeling. Minelli and Baedke make a strong case for the

singularity of model organisms in evo-devo, since they argue that, in order to

answer the challenges of inter- and intraspecific variability faced by model

organisms in this field, theorists should embed model organisms in epigenetic

landscapes, which is another, more formal or mathematical, type of modeling.

Concerning neurosciences, Brooks also shows the complex and hybrid epistemic

nature of actual models built by those who investigate motion detection. Hence, this

special issue invites at the same time to a fine-graining of the typology of models

themselves (whether in vivo, in silico, or in vitro) and to an increased attention paid

to the hybrid nature of modeling practices through which these models are used in

the production of science. Hence what is going on is not only a conceptual

sharpening of the distinction between various types of models, but also, an

invitation to not take these terms at face value, so that interesting questions can

emerge from accounts of the multifarious usage of modeling in the life science.

Complexity and interaction of various scientific practices need common ground: are

not models this common area? This could be the reason why the focus is so much on

the use rather than the content of models, or at least, on the articulation between the

use and the content of models.

Finally, in the life sciences there seems to exist an important distinction between

models for biological and models for medical purpose. This is not to say that there is

no common, general philosophical question of models in the life sciences in general;

yet this raises the question of an early bifurcation between the philosophy of

biological models and the philosophy of medical models. Examples on which

philosophical analysis is carried out need to be either medical or biological for a

simple reason: these are distinct scientific activities in the field. It is not about the

so-called distinction between theoretical and practical knowledge. As preoccupa-

tions differ, so do stringency and demands. Along with the use of models, the

content needs to differ. For instance, predictive validity matters more than construct

validity when model organisms are used for screening potential new treatments. On

the one hand, models of this kind seem very rough or rudimentary to many

biologists, because no underlying mechanism needs to be clarified first. It so

happens that this test is predictive, independently of any resemblance. In this field,

animal models, especially (but not only) GMOs, are commonly considered artificial

systems instead of individuals of another species sharing properties because of

common descent. On the other hand, this same model has to provide very reliable

data. In medical research, scientists are also far more wary of interspecific claims

and more sensitive to the difficulties of extrapolation, since the cost of an erroneous

hypothesis is high. Moreover, the problem of intraspecific variability is not studied

by itself—questioning its causes—but rather, integrated into the problem—namely,

by asking to what extent it can be concluded that the output of A is B in the (human)

species, given individual variability. The reason is that knowledge is indeed

practice-oriented. In short, confronting papers that deal with life sciences with those

that deal with medicine makes salient the fact that models in medicine can be in


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principle part of an intervention strategy. This important distinction adds a layer of

complexity to the initial partitioning of models in life sciences.

Hence, here are five contributions focusing on various aspects of the topic; we

hope that their confrontation can also bring out more general lessons about

modeling in the life sciences, and about where the current and difficult issues stand

for philosophers.

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Documents

Philippe Huneman, Mael Lemoine. Introduction: the plurality of modeling