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Epistemology

The black swan symbolizes one of the historical problems of epistemology: if all the swans we have seen so far are white, can we decide that all the swans are white?
Really?
Black Swan (Cygnus atratus) RWD.jpg
 
Duck-Rabbit illusion.jpg
Kuhn used optical illusion to demonstrate how a paradigm shift can cause a person to see the same information in a completely different way: which animal is the one here aside?
Sure?


Epistemology (from the Greek ἐπιστήμη, epistēmē, meaning "certain knowledge" or "science", and λόγος, logos, "discourse") represents that branch of philosophy dedicated to the study of the necessary conditions for acquiring scientific knowledge and the methods through which such knowledge can be achieved.[1] This term specifically refers to that section of gnoseology that investigates the foundations, the validity, and the limits of scientific knowledge. In English-speaking countries, the concept of epistemology is commonly employed almost as a synonym for gnoseology or theory of knowledge, that is, the discipline that examines the study of knowledge in general.

It is important to emphasize that the central problem of epistemology, today as in the times of Hume, is the issue of verifiability.[2][3]

The Hempel's paradox asserts that the observation of every white swan provides support to the statement that all ravens are black;[4] in other words, every example that does not contradict the theory confirms a part of it. According to this paradox:

According to the criterion of falsifiability, no theory can be considered definitively true, as although there is only a finite number of experiments that can confirm it, theoretically there is an infinite number of experiments that could refute it.[5]

But it’s not all so obvious...

...because the very concept of epistemology meets continuous implementations, like in medicine:

  • : In medicine, for example, to confirm an experiment or validate a series of data collected through laboratory instruments or surveys, reliance is placed on "Statistical Inference," and in particular on a well-known value called "significance test" (P-value). However, even this concept, now rooted in the practice of researchers, is being questioned. A recent study has focused attention on a campaign conducted in the journal "Nature" against the use of the "significance test."[6] With over 800 signatories, including eminent scientists, this campaign can be seen as an important turning point and a "Silent Revolution" in the field of statistics, touching logical and epistemological aspects.[7][8][9] The critique is aimed at overly simplified statistical analyses, still present in numerous publications. This has stimulated a debate, sponsored by the American Statistical Association, which led to the creation of a special issue of "The American Statistician Association" titled "Statistical Inference in the 21st Century: A World Beyond p < 0.05", containing 43 articles by statisticians looking towards the future[16]. This special issue proposes new ways to communicate the significance of research findings beyond the arbitrary threshold of a P-value and offers guidelines for research that accepts uncertainty, is reflective, open, and modest in claims.[10] The future will reveal whether these attempts to provide more solid statistical support to science, beyond traditional significance tests, will find resonance in future publications.[11] This evolution aligns with Kuhn's concept of scientific progress, reflecting a reworking of some descriptive statistical content within the discipline.
  • Interdisciplinarity:
    In the field of science policy, it is universally recognized that solving science-based problems requires an interdisciplinary research approach (IDR), as highlighted by the European Union's Horizon 2020 project.[12] Recent studies have explored the reasons for the cognitive and epistemic difficulties that researchers encounter in conducting IDR. One identified cause is the decline of philosophical interest towards the epistemology of IDR, attributed to the dominant "Physical Paradigm of Science." This paradigm limits the recognition of significant developments in IDR, both in the context of the philosophy of science and in the practice of research itself. In response, an alternative philosophical paradigm has been proposed, called the "Engineering Paradigm of Science," which offers alternative philosophical perspectives on fundamental aspects such as the purpose of science, the nature of knowledge, the epistemic and pragmatic criteria for the acceptance of knowledge, and the role of technological tools. Consequently, it highlights the need for researchers to make use of metacognitive support structures, called metacognitive scaffolds, to facilitate the analysis and reconstruction of the processes by which knowledge is constructed across different disciplines. In the context of IDR, such metacognitive scaffolds are essential for promoting effective communication between disciplines, allowing scholars to analyze and articulate how each discipline contributes to the construction of knowledge.[13][14]
  1. The term is believed to have been coined by the Scottish philosopher James Frederick Ferrier, in his Institutes of Metaphysic (p.46), of 1854; see Internet Encyclopedia of Philosophy, James Frederick Ferrier (1808—1864).
  2. David Hume (Edimburgo, 7 maggio 1711[1] – Edimburgo, 25 agosto 1776) was a Scottish philosopher. He is considered the third and perhaps the most radical of the British Empiricists, after the Englishman John Locke and the Anglo-Irish George Berkeley.
  3. Srivastava S, «Verifiability is a core principle of science», in Behav Brain Sci, Cambridge University Press, 2018, Cambridge».
    DOI:10.1017/S0140525X18000869 
    Jan;41:e150.
  4. Here we obviously refer to the well-known paradox called "of the crows", or "of the black crows", formulated by the philosopher and mathematician Carl Gustav Hempel, better explained in Wikipedia's article Raven paradox:
    See Good IJ, «The Paradox of Confirmation», in Br J Philos Sci, 1960 – in «Vol. 11». 
  5. Evans M, «Measuring statistical evidence using relative belief», in Comput Struct Biotechnol J, 2016».
    DOI:10.1016/j.csbj.2015.12.001 
    Jan 7;14:91-6.
  6. Amrhein V, Greenland S, McShane B, «Scientists rise up against statistical significance», in Nature, 2019».
    DOI:10.1038/d41586-019-00857-9 
    Mar;567(7748):305-307.
  7. Rodgers JL, «The epistemology of mathematical and statistical modeling: a quiet methodological revolution», in Am Psychol, 2010».
    DOI:10.1037/a0018326 
    Jan;65(1):1-12.
  8. Meehl P, «The problem is epistemology, not statistics: replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions», 1997». , in eds Harlow L. L., Mulaik S. A., Steiger J. H., What If There Were No Significance Tests? - editors. (Mahwah: Erlbaum, 393–425. [Google Scholar]
  9. Sprenger J, Hartmann S, «Bayesian Philosophy of Science. Variations on a Theme by the Reverend Thomas Bayes», Oxford University Press, 2019, Oxford». 
  10. Wasserstein RL, Schirm AL, Lazar NA, «Moving to a World Beyond p < 0.05», in Am Stat, 2019».
    DOI:10.1080/00031305.2019.1583913 
    73, 1–19.
  11. Dettweiler Ulrich, «The Rationality of Science and the Inevitability of Defining Prior Beliefs in Empirical Research», in Front Psychol, 2019».
    DOI:10.3389/fpsyg.2019.01866 
    Aug 13;10:1866.
  12. European Union, Horizon 2020
  13. Boon M, Van Baalen S, «Epistemology for interdisciplinary research - shifting philosophical paradigms of science», in Eur J Philos Sci, 2019».
    DOI:10.1007/s13194-018-0242-4 
    9(1):16.
  14. Boon M, «An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool», in Prog Biophys Mol Biol, 2017».
    DOI:10.1016/j.pbiomolbio.2017.04.001 
    Oct;129:25-39.