On Accelerations in Science Driven by Daring Ideas: Good Messages from Fallibilistic Rationalism

• Authors: Witold Marciszewski
• Languages of publication: EN

Abstracts

EN

The first good message is to the effect that people possess reason as a source of intellectual insights, not available to the senses, as e.g. axioms of arithmetic. The awareness of this fact is called rationalism. Another good message is that reason can daringly quest for and gain new plausible insights. Those, if suitably checked and confirmed, can entail a revision of former results, also in mathematics, and - due to the greater efficiency of new ideas - accelerate science’s progress. The awareness that no insight is secured against revision, is called fallibilism. This modern fallibilistic rationalism (Peirce, Popper, Gödel, etc. oppose the fundamentalism of the classical version (Plato, Descartes etc.), i.e. the belief in the attainability of inviolable truths of reason which would forever constitute the foundations of knowledge. Fallibilistic rationalism is based on the idea that any problem-solving consists in processing information. Its results vary with respect to informativeness and its reverse - certainty. It is up to science to look for highly informative solutions, in spite of their uncertainty, and then to make them more certain through testing against suitable evidence. To account for such cognitive processes, one resorts to the conceptual apparatus of logic, informatics, and cognitive science.

Keywords

EN

a priori; complexity; computability; fallibilism; fallibilistic rationalism; fallibility; fundamentalism; Gödel’s speedup strategy; guessing with clues; information processing; informativeness vs certainty; problem-solving; risk

• Publisher: Sciendo
• Journal: Studies in Logic, Grammar and Rhetoric
• Year: 2015
• Volume: 40
• Issue: 1
• Pages: 19-41

Dates

published

2015-03-01

online

2015-04-10

Contributors

author

Witold Marciszewski

• University of Bialystok

References

• 1. I regret that the obvious size limitations of this paper do not allow me to tell about hypercomputation as a fascinating case of information processing which is not utmcomputational. To give the taste of the problem, let me refer to Hector Zenil’s blog “Anima ex Machina”, the post: http://www.mathrix.org/liquid/category/recreation entitled “Hypercomputation in A Computable Universe”.
• 2. See: Alan Turing, “Systems of Logic Based on Ordinals”, Proc. London Math. Soc., ser. 2, 45 (1939).
• 3. See: G. J. Chaitin, Algorithmic Information Theory, Cambridge University Press, 1990 (2nd ed.), p. 62.
• 4. An extensive account of Chatin’s theory and its applications to the progress of sciences can be found in the book by Douglas S. Robertson Phase Change: The Computer Revolution in Science and Mathematics, Oxford University Press 2003. As for G. J. Chaitin’s original texts, for present purposes his Information, Randomness & Incompleteness: Papers on Algorithmic Information Theory (World Scientific, Singapore 1990) would be very useful.
• 5. This term appears in most recent discussions to take advantage of the explanatory merits of the idea of information with respect to the nature of the universe. See, e.g., Hector Zenil’s polemics with Seth Lloyd in the former’s blog “Anima ex Machina”: http://www.mathrix.org/liquid/archives/tag/quantum-computer.
• 6. A recent approach to the exponential growth of information is found in discussions inspired by Ray Kurzweil’s bold predictions. See, e.g., the blog discussion entitled “Why so slow” at the page http://sciencehouse.wordpress.com/2008/06/10/why-so-slow/. Also “Big and Small” by R. D. Ekers at http://arxiv.org/pdf/1004.4279.pdf.
• 7. Still in the first decades of the 20th century it was projected in the Vienna Circle to establish a logic of induction, able to grant such certainty to the natural sciences, as the logic of deduction does with respect to mathematics.
• 8. See http://pl.wikipedia.org/wiki/Max Planck, and http://en.wikipedia.org/wiki/ Special relativity.
• 9. See http://en.wikipedia.org/wiki/Initial singularity.
• 10. Nicholas Rescher, Satisfying Reason: Studies in the Theory of Knowledge (Kluwer, Dordrecht 1995). See chapter 3. Reason and Reality, section 6. The Burdens of Complexity, p. 38.
• 11. See the paper by Gordana Dodig-Crnkovic “Significance of Models of Computation, from Turing Model to Natural Computation”, Minds and Machines, May 2011, volume 21, issue 2, pp. 301-32. Available with Springer if addressed: http://link.springer.com/article/10.1007/s11023-011-9235-1.
• 12. Cp. http://www.mathrix.org/liquid/category/recreation - H. Zenil’s post: “Meaningful Math Proofs and ‘Math is not Calculation’”.
• 13. Available at https://mises.org/journals/jls/121/1219.pdf. Published in: Journal for Libertarian Studies, 12(1) (Spring 1996), pp. 179-192. Center for Libertarian Studies.
• 14. See http://dl.acm.org/citation.cfm?doid=2580723.2591012. Published in Communications of the ACM, April 2014, volume 57, issue 4, pp. 66-75. John Harrison belongs among the most renowned computer scientists in the field of automated theorem proving. Jeremy Avigad is a professor in the departments of philosophy and of mathematics at Carnegie Mellon University.
• 15. More on this subject, see chapter 25 in George Boolos’ book Logic, Logic, and Logic, Harvard University Press 1998. The proof of Gödel’s 1936 theorem is given in: Samuel R. Buss, “On Gödel’s Theorems on Lengths of Proofs I: Number of Lines and Speedups for Arithmetic”, Journal of Symbolic Logic, 39, 1994, pp. 737-756.
• 16. See http://logika.uwb.edu.pl/studies/index.php?page=search&vol=22, sections 1.1-1.5.

Identifiers

DOI

10.1515/slgr-2015-0002