![]() Perfectly elastic bodies, perfectly uniform electric fields, and the like, Without idealizing and simplifying assumptions such asįrictionless planes, weightless strings, isolated thermodynamic systems, Mathematical-analytical or computational power, (iii) necessary auxiliary There mayīe the lack of, (i) necessary data to required accuracy, (ii) Resort to idealizations and approximations for several reasons. To Newton's law of gravitation and second law of motion. For instance, assuming that the universe contains only twoīodies is an idealization that may be employed in some contexts as input Sometimes idealizations take the form of assumptions conjoined to a theoryįrom outside. To point-masses, that law contains an idealization as part of its content. An idealization may be contained within the law or theory itself.įor example, insofar as Newton's second law is conceived as applying only Idealizations enter into scientific analysis or explanation in a couple ![]() Non-probabilistic analogue that I offer of the confirmation conditionĪbove avoids the "old evidence problem," which has been a headache for This formulation has the virtue of explicitly taking intoĪccount the essential use made of idealizations and approximations as wellĪs the fact that theoretically based predictions that utilize suchĪssumptions will not, in general, exactly fit the data. The prediction P T and the actual observation P, andĮntailment. Of idealizations and approximations used in derivingįrom T, P D expresses the discrepancy between Plausible way of incorporating idealizations and approximations into theīayesian condition for incremental confirmation: Theory T is confirmedīy observation P relative to background knowledge Reflect the role of idealizations and approximations in the confirmation Myįocus in this paper is on how the basic Bayesian model can be amended to Hypothetico-deductive, bootstrapping and Bayesian accounts ofĬonfirmation, idealizations and approximations are simply ignored. Neglected or misunderstood by philosophers. This aspect of theory testing has been long Underlying the difficulties is the fact that idealizingĪnd approximating assumptions are already known to beįalse statements, and yet they are often indispensable when testing Use of such simplifyingĪssumptions as catalysts in the process of deriving testable predictionsįrom theories complicates our picture of confirmation andĭisconfirmation. Spherical shapes, are commonplace in science. Infinity, assumptions of linearity, of "negligible" masses, of perfectly Point-masses, perfectly elastic springs, parallel conductors crossing at Of the confirmation condition above that I offer avoids the 'oldĮvidence problem,' which has been a headache for classical Will not, in general, exactly fit the data. Prediction P T from T, P D expresses the discrepancy between the prediction P T and theįormulation has the virtue of explicitly taking into account theĮssential use made of idealizations and approximations as well as theįact that theoretically based predictions that utilize such assumptions Theory T is confirmed by observation P relative to background knowledgeĬonjunction of idealizations and approximations used in deriving the I suggest theįollowing as a plausible way of incorporating idealizations andĪpproximations into the Bayesian condition for incremental confirmation: Middle East Technical University, Ankara, focus in this paper is on how the basic Bayesian model can beĪmended to reflect the role of idealizations and approximations in theĬonfirmation or disconfirmation of any hypothesis. The Bayesian Theory of Confirmation, Idealizations and Approximations in Science
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