Mind & Society 7 (2):157 (2008)
|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.|
|Keywords||Modeling Information Semantics Validation and Verification|
|Through your library||Configure|
Similar books and articles
Ruth Berger (1998). Understanding Science: Why Causes Are Not Enough. Philosophy of Science 65 (2):306-332.
Gordana Dodig Crnkovic & Wolfgang Hofkirchner (2011). Floridi’s “Open Problems in Philosophy of Information”, Ten Years Later. Information 2 (2):327-359.
Gordana Dodig Crnkovic (2006). Investigations Into Information Semantics and Ethics of Computing. Dissertation, Mälardalen University
Luciano Floridi (2005). Is Semantic Information Meaningful Data? Philosophy and Phenomenological Research 70 (2):351-370.
Timothy R. Colburn (1998). Information Modeling Aspects of Software Development. Minds and Machines 8 (3):375-393.
Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.
Added to index2009-04-06
Total downloads46 ( #24,329 of 556,803 )
Recent downloads (6 months)1 ( #64,847 of 556,803 )
How can I increase my downloads?