Berechenbarkeit der Welt? pp 107-127 | Cite as
Our Thinking – Must it be Aligned only to the Given Data?
Zusammenfassung
New technological possibilities in Big Data allow finding unexpected structures and relations in datasets, provided by different realms and areas. This article distinguishes between signals, data, information and knowledge, and discusses ownership of data and information. Knowledge will be considered as the result of understanding information. The results of big data analyses cannot be adequately interpreted if the research question, i.e. the question of what to look for, has not been asked beforehand. Thus, a model is required to perform a satisfactory data analysis. A model, which allows a causal explanation, is better than a model, which delivers only extrapolations. The potential tendency to replace scientific models with merely numerical procedures will be discussed critically.
“True wisdom, as the fruit of self-examination, dialogue and generous encounter between persons, is not acquired by a mere accumulation of data which eventually leads to overload and confusion, a sort of mental pollution.” (Pope Francis (2015), IV, Sec. 47, p. 33)
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References
- *um & HP - The unbelievable Machine Company & Hewlett Packard. 2015. Hadoop2 – Big Data Projekte erfolgreich realisieren. White Paper, Berlin 2014. https://www.unbelievable-machine.com/hadoop2/downloads/um-hp-whitepaper2014.pdf.
- Anderson, Ch. 2008. The End of Theory. The Data Deluge Makes the Scientific Method Obsolete. WIRED Magazine June 23rd 2008, http://www.wired.com/2008/06/pb-theory/.
- Boeing, N. 2015. Wie berechenbar ist der Mensch? Technology Review 03/2015. http://www.heise.de/tr/artikel/Wie-berechenbar-ist-der-Mensch-2599601.html.
- Bullinger, H.-J., and K. Kornwachs. 1990. Expertensysteme – Anwendungen und Auswirkungen im Produktionsbetrieb. München: C.H. Beck.Google Scholar
- Grunwald, A., K. Kornwachs et al. 2012. Technikzukünfte. Vorausdenken – Erstellen – Bewerten. In acatech IMPULS, ed. Acatech. Berlin, Heidelberg: Springer 2012. http://www.acatech.de/de/publikationen/impuls/acatech-impuls/detail/artikel/technikzukuenfte-vorausdenken-erstellen-bewerten.html.
- Grzanna, M. 2015. Der Orwell’sche Bürger. Technology Review 2.7. 2015. http://www.heise.de/tr/artikel/Der-Orwell-sche-Buerger-2663947.html.
- Gumbrecht, H.-U. 2014. Das Denken muss nun auch den Daten folgen. Frankfurter Allgemeine Zeitung, Feuilleton 12.3. 2014. http://www.faz.net/aktuell/feuilleton/geisteswissenschaften/neue-serie-das-digitale-denken-das-denken-muss-nun-auch-den-daten-folgen-12840532.html
- Hofstetter, Y. 2014. Sie wissen alles – wie intelligente Maschinen in unser Leben eindringen und warum wir für unsere Freiheit kämpfen müssen. Gütersloh: Bertelsmann.Google Scholar
- Kant, I. 1956. Kritik der Reinen Vernunft. Hamburg: Meiner.Google Scholar
- Keller, U., and S. Kluge. 1999. Vom Einzelfall zum Typus – Fallvergleich und Fallkontrastierung in der qualitativen Sozialforschung. Opladen: Leske+Budrich.Google Scholar
- Klir, G.J. 1976. Identification of Generative Structures in Empirical Data. International Journal of General Systems 3, 89-104.Google Scholar
- Klir, G.J. 1985. The Architecture of Systems Problem Solving. New York: Plenum Press.Google Scholar
- Klir, G.J., M. Pitarelli, M. Mariano, and K. Kornwachs. 1988. The Potentiality of Reconstructability Analysis for Production Research. International Journal for Production Research 26, 629645.Google Scholar
- Kornwachs, K. 1999. Von der Information zum Wissen? In Gene, Neurone, Qubits & Co. Unsere Welten der Information/Forschung-Technik-Mensch. Verhandlungen der Gesellschaft Deutscher Naturforscher und Ärzte, 120. Versammlung, Berlin, 19.-22. September 1998, ed. D. Ganten et al., 35-44. Stuttgart: Hirzel.Google Scholar
- Kornwachs, K. 2001. Data - Information – Knowledge – A Trial for Technological Enlightenment. In Toward the Information Society – The Case of Central and Eastern European Countries. Wissenschaftsethik und Technikfolgenbeurteilung, Bd. 9., ed. G. Banse, C.J. Langenbach, P. Machleidt, 109-123. Berlin, Heidelberg: Springer.Google Scholar
- Kornwachs, K. 2012. Strukturen technologischen Wissens. Analytische Studien zu einer Wissenschaftstheorie der Technik. Berlin: Edition Sigma 2012.Google Scholar
- Kornwachs, K. 2015. Short Reports and Impressions on the 3rd Sino-German i-City Workshop, Oct 29-30, 2014, Wuhan, China; submitted to the National Academy of Science and Engineering (acatech), Berlin, March 2015.Google Scholar
- Kornwachs, K. et al. 2013. Technikwissenschaften. Erkennen – Gestalten – Verantworten. In acatech IMPULS, ed. acatech. Berlin, Heidelberg: Springer. http://www.acatech.de/de/publikationen/impuls.html. English: Technological Sciences. Discovery – Design – Responsibility (2014).http://www.acatech.de/de/publikationen/impuls/acatech-impuls/detail/artikel/technological-science-discovery-design-responsibility.html.
- Lütge, G. 2014. Schnüffeln verboten – Facebook, Google, Apple: Ignorieren sie den Datenschutz? DIE ZEIT 19, S. 22.Google Scholar
- Mainzer, K. 2014. Die Berechnung der Welt – Von der Weltformel zu Big Data. München: Beck.Google Scholar
- Mayer-Schönberger, V. und K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray.Google Scholar
- Mortensen C. D. (ed.). 2008. Communication theory. 2nded. New Brunswick, NJ: Transaction Publ.Google Scholar
- Neumann, W. L. 2005. Social Research Methods: Qualitative and Quantitative Approaches. 6thed. Boston: Allyn & Bacon.Google Scholar
- Norvig, P. 2008. All we want are the facts, ma’m. http://norvig.com/fact-check.html.
- Obama, B. 2014. Remarks on the Administration’s Review of Signals Intelligence from January 17, 2014. http://www.whitehouse.gov/the-pressoffice/2014/01/17/remarks-president-review-signals-intelligence. Accessed November 2015.
- Palace, B. 1996. Data Mining. Technology Note prepared for Management 274A. Anderson Graduate School of Management at University of California, Los Angeles (UCLA) Spring 1996. http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm.
- Podesta, J. et al. 2014. Big Data: Seizing Opportunities, preserving Values. Executive Office of the President. White House: Washington. http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf.
- Pope Francis. 2015. Encyclical Letter ‘Laudato Si’ of the Holy Father Francis on Care for our Common Home. Vatican Press, Vatican, Rome, May, 24th, 2015. http://w2.vatican.va/content/dam/francesco/pdf/encyclicals/documents/papa-francesco_20150524_enciclica-laudato-si_en.pdf.
- Rauner, M. 2006. Die Merkels von nebenan. ZEIT Wissen 4, 36-41.Google Scholar
- Schulzki-Hadouti, Chr. 2015. Behauptete Sicherheiten. VDI Nachrichten 29/30, 11.Google Scholar
- Shannon, C.E., and W. Weaver. 1949. The Mathematical Theory of Communication. 2nd ed. 1969. Chicago, London: Urbana. Deutsch: Mathematische Grundlagen der Informationstheorie. Munchen: R. Oldenbourg.Google Scholar
- Stolz, M., and J. Block. 2012. Deutschlandkarte – 102 neue Wahrheiten. München: Knaur.Google Scholar
- Strutz T. 2011. Data Fitting and Uncertainty – A practical introduction to weighted least squares and beyond. Wiesbaden: Vieweg+Teubner.Google Scholar
- Tal, E. 2015. Measurement Science. In The Stanford Encyclopedia of Philosophy (Summer Edition), ed. Edward N. Zalta. http://plato.stanford.edu/archives/sum2015/entries/measurement-science/.
- Techopedia. 2015. What is the difference between big data and data mining? http://www.techopedia.com/7/29678/technology-trends/what-is-the-difference-between-big-data-and-data-mining.
- Witten, I. H., E. Frank, and M. A. Hall. 2011. Data mining: practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann.Google Scholar
- Yin, R. K. 2003. Case Study Research – Design and Methods. Thousand Oaks: Sage Publications.Google Scholar
- Zwick, M. 2004. An Overview of Reconstructability Analysis. Kybernetes 33, 877-905. All web-page accesses checked on January, 10th 2016.Google Scholar