Literature DB >> 16701318

Simulation as experiment: a philosophical reassessment for biological modeling.

Steven L Peck1.   

Abstract

Some scientific modelers suggest that complex simulation models that mimic biological processes should have a limited place in ecological and evolutionary studies. However, complex simulation models can have a role that is different from that of simpler models that are designed to be fit to data. Simulation can be viewed as another kind of experimental system and should be analyzed as such. Here, I argue that current discussions in the philosophy of science and in the physical sciences fields about the use of simulation as an experimental system have important implications for biology, especially complex sciences such as evolution and ecology. Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, can offer hypotheses about the way the world works and can give guidance as to which data are most important to gather experimentally.

Year:  2004        PMID: 16701318     DOI: 10.1016/j.tree.2004.07.019

Source DB:  PubMed          Journal:  Trends Ecol Evol        ISSN: 0169-5347            Impact factor:   17.712


  27 in total

1.  Navigating the perfect storm: research strategies for socialecological systems in a rapidly evolving world.

Authors:  John A Dearing; Seth Bullock; Robert Costanza; Terry P Dawson; Mary E Edwards; Guy M Poppy; Graham M Smith
Journal:  Environ Manage       Date:  2012-03-15       Impact factor: 3.266

2.  Artificial neural networks and the study of evolution of prey coloration.

Authors:  Sami Merilaita
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2007-03-29       Impact factor: 6.237

3.  In silico evolution of functional morphology: A test on bone tissue biomechanics.

Authors:  Emmanuel de Margerie; Paul Tafforeau; Lalaonirina Rakotomanana
Journal:  J R Soc Interface       Date:  2006-10-22       Impact factor: 4.118

4.  Individual-based modelling: an essential tool for microbiology.

Authors:  Jordi Ferrer; Clara Prats; Daniel López
Journal:  J Biol Phys       Date:  2008-07-19       Impact factor: 1.365

5.  Evolution of social learning when high expected payoffs are associated with high risk of failure.

Authors:  Michal Arbilly; Uzi Motro; Marcus W Feldman; Arnon Lotem
Journal:  J R Soc Interface       Date:  2011-04-20       Impact factor: 4.118

6.  Model First and Ask Questions Later: Confessions of a Reformed Experimentalist.

Authors:  Jeffrey W Holmes
Journal:  J Biomech Eng       Date:  2019-04-08       Impact factor: 2.097

7.  Drivers of geographical patterns of North American language diversity.

Authors:  Marco Túlio Pacheco Coelho; Elisa Barreto Pereira; Hannah J Haynie; Thiago F Rangel; Patrick Kavanagh; Kathryn R Kirby; Simon J Greenhill; Claire Bowern; Russell D Gray; Robert K Colwell; Nicholas Evans; Michael C Gavin
Journal:  Proc Biol Sci       Date:  2019-03-27       Impact factor: 5.349

8.  Towards reproducible descriptions of neuronal network models.

Authors:  Eilen Nordlie; Marc-Oliver Gewaltig; Hans Ekkehard Plesser
Journal:  PLoS Comput Biol       Date:  2009-08-07       Impact factor: 4.475

9.  Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents.

Authors:  C J Boyle; A B Lennon; M Early; D J Kelly; C Lally; P J Prendergast
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2010-06-28       Impact factor: 4.226

10.  The effects of spatial and temporal heterogeneity on the population dynamics of four animal species in a Danish landscape.

Authors:  Richard M Sibly; Jacob Nabe-Nielsen; Mads C Forchhammer; Valery E Forbes; Christopher J Topping
Journal:  BMC Ecol       Date:  2009-06-23       Impact factor: 2.964

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