On Modelling

  • P. Viviani
Part of the NATO ASI Series book series (ASID, volume 85)


There is little agreement — both across, and within disciplines — on what it takes to have a model. In psychology and, to some extent, in economics it is not uncommon to call models any doctrine with a debatable amount of empirical support. Understandably, predictions in these domains are not a major concern. Instead, the ability to derive empirical predictions from first principles has a paramount importance in physics where the concept of model is almost coextensive with that of post-Galilean natural science. Virtually all systems of explanation in physics, from Newtonian mechanics to string theory, fall under such an all-encompassing definition. Of course, also in physics predictive power is limited in more than one way. For instance, even well established models — such as quantum mechanics and relativity theory — are not parameter-free as one would like them to be. Moreover, we have to deal with the disturbing fact that sometimes first principles clash almost unbearably with our intuitions. Despite all this, however, there is a general consensus that there can be no natural science without models. Indeed, some physicists hold that modelling is not merely necessary but, ultimately, also sufficient for understanding natural phenomena, and that empirical evidence should be relegated to a confirmatory role.


Brain Science Motor Control System Historical Accident Phonological Rule Linguistic Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1996

Authors and Affiliations

  • P. Viviani
    • 1
    • 2
  1. 1.Department of PsychobiologyFAPSE University of GenevaSwitzerland
  2. 2.Department of Cognitive ScienceFondazione San RaffaeleMilanItaly

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