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On Leaving as Little to Chance as Possible

  • Michael Kary

Abstract

Randomness was one of Mario Bunge’s earliest philosophical interests, and remains as one of his most persistent. Bunge’s view of the nature of randomness has been largely consistent over many decades, despite some evolution. For a long time now, he has seen chance as a purely ontological matter of contingency, something that does not result from either psychological uncertainty or epistemological indeterminacy, and that disappears once the die is cast. He considers the Bayesian school of probability and statistics to be pseudoscientific. Bunge upholds a fairly conventional view that chance is not any part of the purely mathematical theory of probability, and a thoroughly unconventional view that ontologically contingent processes are deterministic, though not classically so. This chapter examines Bunge’s views on probability by investigating what any of the following have to do with each other: chance or randomness, likelihood, the mathematical theory of probability, determinism, independence, belief, psychological uncertainty, and epistemological indeterminacy.

Notes

Acknowledgement

I thank Michael Matthews and Paul McColl for their respective contributions in shepherding the manuscript through its various stages.

References

  1. Bailey, D. H., & Borwein, J. (2014). Pi Day is upon us again and we still do not know if Pi is normal. American Mathematical Monthly, 121(3), 191–206.  https://doi.org/10.4169/amer.math.monthly.121.03.191.CrossRefGoogle Scholar
  2. Bayer, D., & Diaconis, P. (1992). Trailing the dovetail shuffle to its lair. Annals of Applied Probability, 2(2), 294–313.CrossRefGoogle Scholar
  3. Berger, J. (2006). The case for objective Bayesian analysis. Bayesian Analysis, 1(3), 385–402.CrossRefGoogle Scholar
  4. Borwein, J. M., Galway, W. F., & Borwein, D. (2004). Finding and excluding b-ary Machin-type BBP formulae. Canadian Journal of Mathematics, 56, 1339–1342.CrossRefGoogle Scholar
  5. Bunge, M. (1951). What is chance? Science & Society, 15(3), 209–231.Google Scholar
  6. Bunge, M. (1967). Foundations of physics (Springer tracts in natural philosophy, Vol. 10). New York: Springer.CrossRefGoogle Scholar
  7. Bunge, M. (1969). Corrections to foundations of physics: Correct and incorrect. Synthese, 19, 443–452.CrossRefGoogle Scholar
  8. Bunge, M. (1977). Ontology I: The furniture of the world (Treatise on basic philosophy, Vol. 3). Dordrecht: Reidel.Google Scholar
  9. Bunge, M. (1979). Ontology II: A world of systems (Treatise on basic philosophy, Vol. 4). Dordrecht: Reidel.Google Scholar
  10. Bunge, M. (1981). Four concepts of probability. Applied Mathematical Modelling, 5, 306–312.CrossRefGoogle Scholar
  11. Bunge, M. (1983). Epistemology & methodology I: Exploring the world (Treatise on basic philosophy, Vol. 5). Dordrecht: Reidel.CrossRefGoogle Scholar
  12. Bunge, M. (1985). Epistemology & methodology III: Philosophy of science and technology—Part I—formal and physical sciences (Treatise on basic philosophy, Vol. 7). Dordrecht: Reidel.Google Scholar
  13. Bunge, M. (2003). Emergence and convergence: Qualitative novelty and the Unity of knowledge. Toronto: University of Toronto Press.Google Scholar
  14. Bunge, M. (2006). Chasing reality: Strife over realism. Toronto: University of Toronto Press.CrossRefGoogle Scholar
  15. Bunge, M. (2008). Bayesianism: Science or pseudoscience? International Review of Victimology, 15, 165–178.CrossRefGoogle Scholar
  16. Bunge, M. (2010). Matter and mind: A philosophical inquiry (Boston studies in the philosophy of science, Vol. 287). Dordrecht/Heidelberg/London/New York: Springer.CrossRefGoogle Scholar
  17. Bunge, M. (2012). Evaluating philosophies (Boston studies in the philosophy of science, Vol. 295). Dordrecht/Heidelberg/New York/London: Springer.CrossRefGoogle Scholar
  18. Bunge, M. (2013). Medical philosophy: Conceptual issues in medicine. Singapore: World Scientific.CrossRefGoogle Scholar
  19. Carriquiry, A. L., & Pawlovich, M. (2004). From empirical Bayes to full Bayes: Methods for analyzing traffic safety data. Iowa Department of Transportation, Office of Traffic and Safety. https://www.iowadot.gov/crashanalysis/pdfs/eb_fb_comparison_whitepaper_october2004.pdf. Accessed 13 June 2018.
  20. Cox, D. R. (2006). Frequentist and Bayesian statistics: A critique (keynote address). In Statistical problems in particle physics, astrophysics and cosmology: Proceedings, PHYSTAT05, Oxford, UK, 12–15 September 2005, World Scientific, Singapore, pp. 3–6.  https://doi.org/10.1142/9781860948985_0001.
  21. Cox, D. R., & Brandwood, L. (1959). On a discriminatory problem connected with the works of Plato. Journal of the Royal Statistical Society, Series B (Methodological), 21(1), 195–200.CrossRefGoogle Scholar
  22. Cox, D. R., & Mayo, D. (2010). Objectivity and conditionality in frequentist inference. In D. Mayo & A. Spanos (Eds.), Error and inference: Recent exchanges on experimental reasoning, reliability and the objectivity and rationality of science (pp. 276–304). Cambridge: Cambridge University Press. http://www.phil.vt.edu/dmayo/personal_website/ch%207%20cox%20%26%20mayo.pdf. Accessed 11 Feb 2018.
  23. Diaconis, P., Holmes, S., & Montgomery, R. (2007). Dynamical bias in the coin toss. SIAM Review, 49(2), 211–235.CrossRefGoogle Scholar
  24. Efron, B. (2013). A 250-year argument: Belief, behavior, and the bootstrap. Bulletin (New Series) of the American Mathematical Society, 50(1), 129–146.CrossRefGoogle Scholar
  25. Hauer, E. (1997). Observational before—after studies in road safety: Estimating the effect of highway and traffic engineering measures on road safety. Amsterdam: Pergamon.Google Scholar
  26. Humphreys, P. (1985). Why propensities cannot be probabilities. The Philosophical Review, 94(4), 557–570.CrossRefGoogle Scholar
  27. Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine.  https://doi.org/10.1371/journal.pmed.0020124.CrossRefGoogle Scholar
  28. Kirschenmann, P. (1973). Concepts of randomness. In M. Bunge (Ed.), Exact philosophy: Problems, tools, and goals (pp. 129–148). Dordrecht: Reidel.CrossRefGoogle Scholar
  29. Kolmogorov, A. N. (1933/1956). Foundations of the theory of probability (Second English Edition) (N. Morrison, Trans.). New York: Chelsea Publishing Company.Google Scholar
  30. Laplace, P. S. (1814). Théorie Analytique des Probabilités (2nd ed.). Paris: Courcier.Google Scholar
  31. Matthews, R. (2003). QED: How to spot the pattern in a game of chance. The Telegraph, 17 July. https://www.telegraph.co.uk/technology/3310656/QED-How-to-spot-the-pattern-in-a-game-of-chance.html. Accessed 18 June 2018.
  32. Persaud, B., & Lyon, C. (2007). Empirical Bayes before–after safety studies: Lessons learned from two decades of experience and future directions. Accident Analysis and Prevention, 39, 546–555.  https://doi.org/10.1016/j.aap.2006.09.009.CrossRefGoogle Scholar
  33. Petrone, S., Rizzelli, S., Rousseau, J., & Scricciolo, C. (2014). Empirical Bayes methods in classical and Bayesian inference. Metron, 72, 201–215.CrossRefGoogle Scholar
  34. Reid, N. (2013). Aspects of likelihood inference. Bernoulli, 19(4), 1404–1418.CrossRefGoogle Scholar
  35. Reid, N., & Cox, D. R. (2015). On some principles of statistical inference. International Statistical Review, 83(2), 293–308.CrossRefGoogle Scholar
  36. Rubin, D. M. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Statistics in Medicine, 26, 20–36.  https://doi.org/10.1002/sim.2739.CrossRefGoogle Scholar
  37. Shafer, G., & Vovk, V. (2006). The sources of Kolmogorov’s Grundbegriffe. Statistical Science, 21(1), 70–98.CrossRefGoogle Scholar
  38. Tervo, D. G. R., Proskurin, M., Manakov, M., Kabra, M., Vollmer, A., Branson, K., & Karpova, A. Y. (2014). Behavioral variability through stochastic choice and its gating by anterior cingulate cortex. Cell, 159(1), 21–32.  https://doi.org/10.1016/j.cell.2014.08.037.CrossRefGoogle Scholar
  39. Volchan, S. B. (2002). What is a random sequence? American Mathematical Monthly, 109(1), 46–63.CrossRefGoogle Scholar
  40. vos Savant, M. (1990). Game show problem. http://marilynvossavant.com/game-show-problem/. Accessed 11 Feb 2018.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Kary
    • 1
  1. 1.MontrealCanada

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