117 research outputs found
From Models to Simulations
This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s.
Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations
Agent-Based Models and Simulations in Economics and Social Sciences: from conceptual exploration to distinct ways of experimenting
Now that complex Agent-Based Models and computer simulations
spread over economics and social sciences - as in most sciences of complex
systems -, epistemological puzzles (re)emerge. We introduce new
epistemological tools so as to show to what precise extent each author is right
when he focuses on some empirical, instrumental or conceptual significance of
his model or simulation. By distinguishing between models and simulations,
between types of models, between types of computer simulations and between
types of empiricity, section 2 gives conceptual tools to explain the rationale of
the diverse epistemological positions presented in section 1. Finally, we claim
that a careful attention to the real multiplicity of denotational powers of
symbols at stake and then to the implicit routes of references operated by
models and computer simulations is necessary to determine, in each case, the
proper epistemic status and credibility of a given model and/or simulation
Agent-Based Models and Simulations in Economics and Social Sciences
Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility.Agent-Based Models and Simulations ; Epistemology ; Economics ; Social Sciences ; Conceptual Exploration ; Model World ; Credible World ; Experiment ; Denotational Hierarchy
The Nature of Computational Things
Architecture often relies on mathematical models, if only to anticipate the physical behavior of structures. Accordingly, mathematical modeling serves to find an optimal form given certain constraints, constraints themselves translated into a language which must be homogeneous to that of the model in order for resolution to be possible. Traditional modeling tied to design and architecture thus appears linked to a topdown vision of creation, of the modernist, voluntarist and uniformly normative type, because usually (mono)functionalist. One available instrument of calculation/representation/prescription orders this conception of architecture: indeed the search for an optimal solution through mathematical calculation of a model itself mathematical, thus homogeneous and simple, is only possible when one or two functions or functional constraints are formulated, never more, and this, on a global level, therefore starting from a unique and homogenizing viewpoint. It is essential to grasp that, even applied to material and its properties or towards a particular esthetic or functional dimension, this viewpoint is thus abstractive and generalizing: disregarding singularity of context, insertion and relationship to the environment or local, social behavior. It leaves aside functional specificity and heterogeneousness â re-contextualized each time â of functions that the object or edifice are required to fulfill and optimize under diverse constraints, in their different parts.
The computational turning point todayâs digital design and computational architecture embody modifies these instrumental, original prescriptions, rendering them more flexible. Perhaps in light of this turnabout we should retrospectively interpret 20th century calls for modernism, functionalism and even biomorphism as being just as many ex post rationalizations in respect to techniques of strongly prescriptive modeling since our only instrument is a monolithic language, and so being, incites a top-down conception, (naturally weakly reactive to contexts), including forms whose overall appearance resembles in fine a living form. In order to liberate oneself from this and despite everything, emerge as its initiators, one has constructed from ideology and philosophy (of object, habitat, the urban) ex post, even while it is the instrument of modeling and conception that largely determines, normalizes and dictates ex ante, the possibilities and limitations of the creation of forms and living experiments4 in a given time
InterprĂ©tabilitĂ© et explicabilitĂ© pour lâapprentissage machine : entre modĂšles descriptifs, modĂšles prĂ©dictifs et modĂšles causaux. Une nĂ©cessaire clarification Ă©pistĂ©mologique
Le dĂ©ficit dâexplicabilitĂ© des techniques dâapprentissage machine (AM) pose des problĂšmes opĂ©rationnels, juridiques et Ă©thiques. Un des principaux objectifs de notre projet est de fournir des explications Ă©thiques des sorties gĂ©nĂ©rĂ©es par une application fondĂ©e sur de lâAM, considĂ©rĂ©e comme une boĂźte noire. La premiĂšre Ă©tape de ce projet, prĂ©sentĂ©e dans cet article, consiste Ă montrer que la validation de ces boĂźtes noires diffĂšre Ă©pistĂ©mologiquement de celle mise en place dans le cadre dâune modĂ©lisation mathĂ©matique et causale dâun phĂ©nomĂšne physique. La diffĂ©rence majeure est quâune mĂ©thode dâAM ne prĂ©tend pas reprĂ©senter une causalitĂ© entre les paramĂštres dâentrĂ©es, qui peuvent ĂȘtre de plus de haute dimensionnalitĂ©, et ceux de sortie. Nous montrons dans cet article lâintĂ©rĂȘt de mettre en Ćuvre les distinctions Ă©pistĂ©mologiques entre les diffĂ©rentes fonctions Ă©pistĂ©miques dâun modĂšle, dâune part, et entre la fonction Ă©pistĂ©mique et lâusage dâun modĂšle, dâautre part. Enfin, la derniĂšre partie de cet article prĂ©sente nos travaux en cours sur lâĂ©valuation dâune explication, qui peut ĂȘtre plus persuasive quâinformative, ce qui peut ainsi causer des problĂšmes dâordre Ă©thique
La surprise comme mesure de l'empiricité des simulations computationnelles
This chapter elaborates and develops the thesis originally put forward by Mary Morgan (2005) that some mathematical models may surprise us, but that none of them can completely confound us, i.e. let us unable to produce an ex post theoretical understanding of the outcome of the model calculations. This chapter intends to object and demonstrate that what is certainly true of classical mathematical models is however not true of pluri-formalized simulations with multiple axiomatic bases. This chapter thus proposes to show that - and why - some of these computational simulations that are now booming in the sciences not only surprise us but also confound us. To do so, it shows too that it is needed to elaborate and articulate with some new precision the concept of weak emergence initially due, for its part, to Mark A. Bedau (1997)
Simulation informatique et pluriformalisation des objets composites
A recent evolution of computer simulations has led to the emergence of complex computer simulations. In particular, the need to formalize composite objects (those objects that are composed of other objects) has led to what the author suggests to call pluriformalizations, i.e. formalizations that are based on distinct sub-models which are expressed in a variety of heterogeneous symbolic languages. With the help of four case-studies, he shows that such pluriformalizations enable to formalize distinctly but simultaneously either different aspects or different parts or different scales of the same object or system. From an epistemological standpoint, he suggests that this kind of computer-aided complex formalization of composite objects renew the traditional relations between computer simulations, theoretical models and operational models
Préface à "La diffusion de la Covid-19 - Que peuvent les modÚles ?"
VoilĂ un livre comme on pouvait lâespĂ©rer. CentrĂ© sur la Covid-19 et sur sa diffusion, il sâinstalle au cĆur de questions brĂ»lantes, encore urgentes pour tout un chacun, mais il garde aussi la tĂȘte froide, prend du recul, informe, enseigne et questionne, qui plus est de façon pĂ©dagogique. Davantage : au-delĂ du bilan critique, il propose des perspectives inĂ©dites, voire quelques suggestions solides. Il nous donne Ă rĂ©flĂ©chir sur des chemins moins balisĂ©s. Ă le lire, on comprendra, par lâexemple, pourquoi un modĂšle ne peut assurer simultanĂ©ment toutes les fonctions de connaissance quâon en peut attendre (dĂ©crire, prĂ©dire, expliquer, etc.). On comprendra pourquoi il faut souvent des modĂšles simples mais aussi, dans certains cas, des modĂšles nĂ©cessairement plus compliquĂ©s. On comprendra Ă©galement pourquoi il faut cultiver une pluralitĂ© de modĂšles, toujours dans la collĂ©gialitĂ©, souvent dans lâinterdisciplinaritĂ© : non seulement pour que ces modĂšles prennent en compte plusieurs aspects, mais aussi pour que leurs diverses fonctions puissent entrer en dialogue, et enfin pour quâils sâĂ©paulent, convergent ou se rectifient, en particulier lĂ oĂč lâon attend dâeux quâils mettent au jour des propriĂ©tĂ©s structurelles robustes, les seules offrant en retour une prise Ă lâaction et Ă la dĂ©cision lĂ©gitimes. On comprendra enfin que la disponibilitĂ© de donnĂ©es Ă la fois pertinentes et stabilisĂ©es est un point dâachoppement majeur, dans les circonstances, tant il est discutable de paramĂ©trer un modĂšle de diffusion de la Covid-19 avec les paramĂštres de la grippe simplement parce quâils sont mieux connus
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