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Empirical modeling and information semantics

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Abstract

This paper investigates the relationship between reality and model, information and truth. It will argue that meaningful data need not be true in order to constitute information. Information to which truth-value cannot be ascribed, partially true information or even false information can lead to an interesting outcome such as technological innovation or scientific breakthrough. In the research process, during the transition between two theoretical frameworks, there is a dynamic mixture of old and new concepts in which truth is not well defined. Instead of veridicity, correctness of a model and its appropriateness within a context are commonly required. Despite empirical models being in general only truthlike, they are nevertheless capable of producing results from which conclusions can be drawn and adequate decisions made.

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Acknowledgments

This article is a revised version of the paper presented at the International Conference Model-Based Reasoning in Science and Engineering (MBR04), held at the University of Pavia, Italy (16–18 December 2004) and chaired by Lorenzo Magnani.

I wish to thank Lorenzo Magnani for organizing MBR 2004, with great efficiency and unadulterated enthusiasm. I also wish to thank Lorenzo Magnani for organizing E-CAP 2004, the conference that created so much interest for the field of Computing and Philosophy.

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  1. Department of Computer Science and Electronics, Mälardalen University, Vasteras, Sweden

    Gordana Dodig-Crnkovic

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  1. Gordana Dodig-Crnkovic
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Correspondence to Gordana Dodig-Crnkovic.

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Dodig-Crnkovic, G. Empirical modeling and information semantics. Mind Soc 7, 157–166 (2008). https://doi.org/10.1007/s11299-007-0035-5

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  • DOI: https://doi.org/10.1007/s11299-007-0035-5

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