Advertisement

Software Error as a Limit to Inquiry for Finite Agents: Challenges for the Post-human Scientist

  • John F. Symons
  • Jack K. Horner
Chapter
Part of the Philosophical Studies Series book series (PSSP, volume 128)

Abstract

Finite agents must build rule-governed processes of some kind in order to extend the reach of inquiry beyond their limitations in a non-arbitrary manner. The clearest and most pervasive example of a rule-governed process that can be deployed in inquiry is a piece of scientific software. In general, the error distribution of all but the smallest or most trivial software systems cannot be characterized using conventional statistical inference theory, even if those systems are not subject to the halting problem. In this paper we examine the implications of this fact for the conditions governing inquiry generally. Scientific inquiry involves trade-offs. We show how increasing use of software (or any other rule-governed procedure for that matter) leads to a decreased ability to control for error in inquiry. We regard this as a fundamental constraint for any finite agent.

Keywords

Software error Limits of science Post-human agent Conventional statistical inference theory Halting problem Path complexity Software correctness Model checking 

References

  1. Amdahl, G. M. (1967). Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conference Proceedings, 30, 483–485. doi:10.1145/1465482.1465560.Google Scholar
  2. Baier, C., & Katoen, J. P. (2008). Principles of model checking. Cambridge, MA: MIT Press.Google Scholar
  3. Bernays, P. (1968). Axiomatic set theory. Dover, 1991.Google Scholar
  4. Bevington, P., & Robinson, D. K. (2002). Data reduction and rrror analysis for the physical sciences. Boston: McGraw-Hill.Google Scholar
  5. Boehm, B. W., Abts, C., Brown, A. W., Chulani, S., Clark, B. K., Horowitz, E., Madachy, R., Reifer, D. J., & Steece, B. (2000). Software cost estimation with COCOMO II. Upper Saddle River: Prentice Hall.Google Scholar
  6. Boolos, G. S., Burgess, J. P., & Jeffrey, R. C. (2007). Computability and logic (5th ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  7. Chang, C., & Keisler, H. J. (1990). Model theory. Amsterdam: North-Holland.Google Scholar
  8. Chung, K. L. (2001). A course in probability theory (3rd ed.). New York: Academic.Google Scholar
  9. Deutsch, D. (1997). The fabric of reality. Allan Lane.Google Scholar
  10. Domski, M., & Dickson, M. (Eds.). (2010). Discourse on a new method: Reinvigorating the marriage of history and philosophy of science. Chicago: Open Court.Google Scholar
  11. Emerson, E. A. (2008). The beginning of model checking: a personal perspective. In O. Grumberg & H. Veith (Eds.), 25 years of model checking. Guildford: Springer. https://www7.in.tum.de/um/25/pdf/Emerson.pdf
  12. Hogg, R., McKean, J., & Craig, A. (2005). Introduction to mathematical statistics (6th ed.). Boston: Pearson.Google Scholar
  13. Horner, J. K., & Symons, J. F. (2014). Reply to Primiero and Angius on software intensive science. Philosophy and Technology, 27, 491–494.CrossRefGoogle Scholar
  14. Hunter, G. (1971). Metalogic: An introduction to the metatheory of standard first-order logic. Berkeley: University of California Press.CrossRefGoogle Scholar
  15. Lloyd, S. (2000). Ultimate physical limits to computation. http://arxiv.org/abs/quant-ph/9908043v3
  16. Nielson, F., Nielson, H. R., & Hankin, C. (1999). Principles of program analysis. Berlin: Springer.CrossRefGoogle Scholar
  17. Nimtz, G. (2006). Do evanescent modes violate relativistic causality? Lecture Notes in Physics, 702, 509.Google Scholar
  18. Nimtz, G., & Stahlhofen, A. (2007). Macroscopic violation of special relativity. arXiv:0708.0681 [quant-ph].Google Scholar
  19. Peirce, C. S. (1931). In C. Hartshorne & P. Weiss (Eds.), Collected papers of Charles Sanders Peirce (Vol. 1–6). Cambridge, MA: Harvard University Press.Google Scholar
  20. Peirce, C. S. (1957). How to make our ideas clear. In V. Tomas (Ed.), Essays in the philosophy of science. New York: The Liberal Arts Press.Google Scholar
  21. Reichenbach, H. (1957). The philosophy of space and time. New York: Dover.Google Scholar
  22. Spinoza, B. (1677). The ethics. Trans. by R. H. M. Elwes (1883). Dover edition (1955).Google Scholar
  23. Susskind, L. (2014). Computational complexity and black hole horizons. http://arxiv.org/abs/1402.5674.
  24. Symons, J. F., & Horner, J. K. (2014). Software intensive science. Philosophy and Technology, 27(3), 461–477.CrossRefGoogle Scholar
  25. Turing, A. (1937). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42(Series 2), 230–265. doi: 10.1112/plms/s2-42.1.230.
  26. Turing, A. (1938). On computable numbers, with an application to the Entscheidungsproblem. A correction. Proceedings of the London Mathematical Society, 43(Series 2), 544–546. doi: 10.1112/plms/s2-43.6.544.
  27. Valmari, A. (1988). The state-explosion problem. Lectures on Petri Nets I: Basic models. Lectures in Computer Science, 1491, 429–528. Springer.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of KansasLawrenceUSA
  2. 2.Independent ResearcherLawrenceUSA

Personalised recommendations