Abstract
Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological change in this area that has three methodological and epistemic consequences: methodological opacity, reproducibility issues, and altered forms of justification.
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Notes
Cf. Poppers reconstruction: “Thus I shall try to establish the rules, or if you will the norms, by which the scientist is guided when he is engaged in research or in discovery, in the sense here understood.” (Popper 2002, p. 29).
We refer mainly to his manuscripts The Crisis of European Sciences and Transcendental Phenomenology (Husserl 1970). The manuscripts on which Husserl had worked in the mid-1930s until his death in 1938 appeared posthumously. However, preliminary studies of this can already be found in the articles on renewal published in early 1920 (cf. Husserl 1989), initially in a Japanese journal.
Cf. also the informative Supplement III to the Crisis-manuscript about the origin of geometry. See also Kaminski (2013).
For a foundation of this understanding of technology see Hubig (2006).
This means that our distinction between, intellectual, social and material technology is not intended to isolate them in separate classes. Rather, they are different aspects from which we can look at technology. Cf. Hubig (2006).
For the following see chapters 4, 6–7 in Foerster (2003).
Feynman suggested what it means to understand an equation. He distinguishes two ways of behaving towards mathematical equations. One is to understand them analytically, through which we gain insight into them. The second is to do the math, to compute the equation. In this case we look at the dynamics of the equations but we don’t have an insight into their structure. Cf. Feynman (2010): 2–1. Lenhard (2015, p. 99) has drawn attention to this important distinction made by Feynman.
Humphreys thus opposes the position of Frigg and Reiss (2009), who argue that the questions of the philosophy of simulation can be traced back to classical questions of the philosophy of science. These considerations have been further developed by Kuorikoski (2012), Saam (2017), Grüne-Yanoff (2017), Barberousse and Vorms (2014), Symons and Alvarado (2016), Newman (2016), Lenhard (2011, 2015).
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Resch, M., Kaminski, A. The Epistemic Importance of Technology in Computer Simulation and Machine Learning. Minds & Machines 29, 9–17 (2019). https://doi.org/10.1007/s11023-019-09496-5
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DOI: https://doi.org/10.1007/s11023-019-09496-5