Simulating many-body models in physics: Rigorous results, 'benchmarks', and cross-model justification
AbstractThis paper argues that, for a prospective philosophical analysis of models and simulations to be successful, it must accommodate an account of mathematically rigorous results. Such rigorous results are best thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results often provide new indirect ways of assessing the success of computer simulations of individual models. This is most obvious in cases where rigorous results map different models on to one another. Not only does this allow for the transfer of warrant across different models, it also puts constraints on the extent to which performance in specific empirical contexts may be regarded as the main touchstone of success in scientific modelling. Rigorous results and relations can thus come to be seen as giving cohesion and stability to actual practices of scientific modelling.
Similar books and articles
Isomorphism and equational equivalence of continuous λ-models.Rainer Kerth - 1998 - Studia Logica 61 (3):403-415.
Mathematical Rigor in Physics: Putting Exact Results in Their Place.Axel Gelfert - 2005 - Philosophy of Science 72 (5):723-738.
Scientific models, simulation, and the experimenter's regress.Axel Gelfert - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
Engineering rigor and its discontents: Philosophical reflection as curative to math-physics envy.David E. Goldberg - unknown
Tools or toys? On specific challenges for modeling and the epistemology of models and computer simulations in the social sciences.Eckhart Arnold - manuscript
Constructible models of subsystems of ZF.Richard Gostanian - 1980 - Journal of Symbolic Logic 45 (2):237-250.
Structure and Coherence of Two-Model-Descriptions of Technical Artefacts.Ulrich Krohs - 2009 - Techne 13 (2):150-161.
Added to PP
Historical graph of downloads
Citations of this work
Computing the uncomputable; or, The discrete charm of second-order simulacra.Matthew W. Parker - 2009 - Synthese 169 (3):447-463.
References found in this work
Models as Mediators: Perspectives on Natural and Social Science.Mary S. Morgan & Margaret Morrison (eds.) - 1999 - Cambridge University Press.
Model-Based Reasoning in Scientific Discovery.L. Magnani, Nancy Nersessian & Paul Thagard (eds.) - 1999 - Kluwer/Plenum.
Using models to represent reality.Ronald N. Giere - 1999 - In L. Magnani, N. J. Nersessian & P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 41--57.
Sanctioning Models: The Epistemology of Simulation.Eric Winsberg - 1999 - Science in Context 12 (2):275-292.
When scientific models represent.Daniela M. Bailer-Jones - 2003 - International Studies in the Philosophy of Science 17 (1):59 – 74.