Synthese

, Volume 178, Issue 2, pp 237–270 | Cite as

Information theory, evolutionary computation, and Dembski’s “complex specified information”

Article

Abstract

Intelligent design advocate William Dembski has introduced a measure of information called “complex specified information”, or CSI. He claims that CSI is a reliable marker of design by intelligent agents. He puts forth a “Law of Conservation of Information” which states that chance and natural laws are incapable of generating CSI. In particular, CSI cannot be generated by evolutionary computation. Dembski asserts that CSI is present in intelligent causes and in the flagellum of Escherichia coli, and concludes that neither have natural explanations. In this paper, we examine Dembski’s claims, point out significant errors in his reasoning, and conclude that there is no reason to accept his assertions.

Keywords

Information theory Evolutionary computation Artificial life Pseudomathematics Complex specified information 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adami C., Ofria C., Collier T.C. (2000) Evolution of biological complexity. Proceedings of the National Academy of Sciences of the United States of America 97: 4463–4468CrossRefGoogle Scholar
  2. Apostolico, A., & Lonardi, S. (2000). Compression of biological sequences by greedy off-line textual substitution. In Proceedings of the IEEE data compression conference (DCC), pp. 143–152.Google Scholar
  3. Berlekamp E.R., Conway J.H., Guy R.K. (1982) Winning ways, for your mathematical plays. Academic Press, LondonGoogle Scholar
  4. Boyer P. (2001) Religion explained. Basic Books, New YorkGoogle Scholar
  5. Byl J. (1989) Self-reproduction in small cellular automata. Physica D 34: 295–299CrossRefGoogle Scholar
  6. Catania A.C., Cutts D. (1963) Experimental control of superstitious responding in humans. Journal of Experimental Analysis of Behavior 6: 203–208CrossRefGoogle Scholar
  7. Chaitin G. (1974) Information-theoretic limitations of formal systems. Journal of the Association for Computing Machinery 21: 403–424Google Scholar
  8. Channon, A. (2001). Passing the ALife test: Activity statistics classify evolution in Geb as unbounded. In J. Kelemen & P. Sosík (Eds.), Proceedings of the 6th European conference on advances in artificial life (ECAL 2001), Vol. 2159 of Lecture notes in artificial intelligence (pp. 417–426). Berlin: Springer.Google Scholar
  9. Chen, X., Kwong, S., & Li, M. (1999). A compression algorithm for DNA sequences and its applications in genome comparison. In Proceedings of the 10th workshop on genome informatics, pp. 52–61.Google Scholar
  10. Dembski W.A. (1998) The design inference: Eliminating chance through small probabilities. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  11. Dembski W.A. (1999) Intelligent design: The bridge between science & theology. InterVarsity Press, IllinoisGoogle Scholar
  12. Dembski W.A. (2002) No free lunch: Why specified complexity cannot be purchased without intelligence. Rowman & Littlefield, Ianham, MDGoogle Scholar
  13. Dembski W.A. (2004) The design revolution: Answering the toughest questions about intelligent design. InterVarsity Press, IllinoisGoogle Scholar
  14. Dinsmore A.D., Wong D.T., Nelson P., Yodh A.G. (1998) Hard spheres in vesicles: Curvature-induced forces and particle-induced curvature. Physical Review Letters 80: 409–412CrossRefGoogle Scholar
  15. Edis T. (2001) Darwin in mind: ‘Intelligent design’ meets artificial intelligence. Skeptical Inquirer 25(2): 35–39Google Scholar
  16. Elsberry W., Shallit J. (2003) Eight challenges for intelligent design advocates. Reports of the NCSE 23(5–6): 23–25Google Scholar
  17. Elsberry W., Shallit J. (2004) Playing games with probability: Dembski’s complex specified information. In: Young M., Edis T. (eds) Why intelligent design fails. Rutgers University Press, Piscataway, NJ, pp 121–138Google Scholar
  18. Fitelson B., Stephens C., Sober E. (1999) How not to detect design—critical notice: William A. Dembski, the design inference. Philosophy of Science 66: 472–488CrossRefGoogle Scholar
  19. Forrest B., Gross P.R. (2004) Creationism’s Trojan horse: The wedge of intelligent design. Oxford University Press, New YorkCrossRefGoogle Scholar
  20. Gajardo A., Moreira A., Goles E. (2002) Complexity of Langton’s ant. Discrete Applied Mathematics 117: 41–50CrossRefGoogle Scholar
  21. Glicksman E. (1984) Free dendritic growth. Materials Science and Engineering 65: 45CrossRefGoogle Scholar
  22. Göbel, F. (1979). On the number of Hamiltonian cycles in product graphs. Technical report # 289. Technische Hogeschool Twente, Netherlands.Google Scholar
  23. Godfrey-Smith P. (2001) Information and the argument from design. In: Pennock R.T. (eds) Intelligent design creationism and its critics. The MIT Press, Cambridge, MA, pp 577–596Google Scholar
  24. Goles E., Margenstern M. (1996) Sand pile as a universal computer. International Journal of Modern Physics C 7(2): 113–122CrossRefGoogle Scholar
  25. Goles E., Schulz O., Markus M. (2001) Prime number selection of cycles in a predator-prey model. Complexity 6(4): 33–38. http://www3.interscience.wiley.com/cgi-bin/fulltext?ID=84502365&PLACEBO=IE.pdf.CrossRefGoogle Scholar
  26. Grumbach S., Tahi F. (1994) A new challenge for compression algorithms: Genetic sequences. Information Processing and Management 30: 875–886CrossRefGoogle Scholar
  27. Heeren F. (2000) The deed is done. American Spectator 33(10): 28–29Google Scholar
  28. Heltzer R.A., Vyse S.A. (1994) Intermittent consequences and problem solving: The experimental control of “superstitious” beliefs. Psychological Record 44: 155–169Google Scholar
  29. Hewish A., Bell S.J., Pilkington J.D.H., Scott P.F., Collins R.A. (1968) Observation of a rapidly pulsating radio source. Nature 217: 709–713CrossRefGoogle Scholar
  30. Hirvensalo M. (2001) Quantum computing. Springer, BerlinGoogle Scholar
  31. Kahneman D., Slovic P., Tversky A. (1982) Judgment under uncertainty: Heuristics and biases. Cambridge University Press, CambridgeGoogle Scholar
  32. Kaplan P.D., Rouke J.L., Yodh A.G., Pine D.J. (1994) Entropically driven surface phase separation in binary colloidal mixtures. Physical Review Letters 72: 582–585CrossRefGoogle Scholar
  33. Kari L. (1997) DNA computing: Arrival of biological mathematics. Mathematical Intelligencer 19(2): 9–22CrossRefGoogle Scholar
  34. Kessler M.A., Werner B.T. (2003) Self-organization of sorted patterned ground. Science 299: 380–383CrossRefGoogle Scholar
  35. Kestenbaum D. (1998) Gentle force of entropy bridges disciplines. Science 279: 1849CrossRefGoogle Scholar
  36. Keynes J.M. (1957) A treatise on probability. Macmillan, LondonGoogle Scholar
  37. Kirchherr W., Li M., Vitányi P. (1997) The miraculous universal distribution. Mathematical Intelligencer 19(4): 7–15. http://www.cwi.nl/paulv/papers/mathint97.ps.CrossRefGoogle Scholar
  38. Koons R.C. (2001) Remarks while introducing Dembski’s talk at the conference Design, self-organization and the integrity of creation. Calvin College, Grand Rapids, MichiganGoogle Scholar
  39. Koza J.R. (1994) Artificial life: Spontaneous emergence of self-replicating and evolutionary self-improving computer programs. In: Langton C.G. (eds) Artificial life III.. Addison-Wesley, Redwood City, CA, pp 225–262Google Scholar
  40. Kuhnert L., Agladze K.I., Krinsky V.I. (1989) Image processing using light-sensitive chemical waves. Nature 337: 244–247CrossRefGoogle Scholar
  41. Lanctot, J. K., Li, M., & Yang, E. (2000). Estimating DNA sequence entropy. In Proceedings of the 11th ACM-SIAM symposium on discrete algorithms (SODA), pp. 409–418.Google Scholar
  42. Laplace P.S. (1952) A philosophical essay on probabilities. Dover, New YorkGoogle Scholar
  43. Lenski R.E., Ofria C., Pennock R.T., Adami C. (2003) The evolutionary origin of complex features. Nature 423: 139–145CrossRefGoogle Scholar
  44. Li M. (2002) Compressing DNA sequences. In: Jiang T., Xu Y., Zhang M.Q. (eds) Current topics in computational molecular biology. The MIT Press, Cambridge, MA, pp 157–171Google Scholar
  45. Lipson H., Pollack J.B. (2000) Automatic design and manufacture of robotic lifeforms. Nature 406: 974–978CrossRefGoogle Scholar
  46. Medawar P.B. (1984) The limits of science. Harper & Row, New YorkGoogle Scholar
  47. Meyer S.C. (2000) DNA and other designs. First Things 102: 30–38Google Scholar
  48. Olofsson P. (2007) Intelligent design and mathematical statistics: a troubled alliance. Biology and Philosophy 23(4): 545–553CrossRefGoogle Scholar
  49. Pallen M.J., Matzke N.J. (2006) From the origin of species to the origin of bacterial flagella. Nature Reviews Microbiology 4(10): 784–790CrossRefGoogle Scholar
  50. Perakh M. (2004) Unintelligent design. Prometheus, New YorkGoogle Scholar
  51. Pigliucci M. (2000) Chance, necessity, and the new holy war against science. A review of W. A. Dembski’s the design inference. BioScience 50: 79–81Google Scholar
  52. Pigliucci M. (2001) Design yes, intelligent no: A critique of intelligent design theory and neocreationism. Skeptical Inquirer 25(5): 34–39Google Scholar
  53. Rambidi N.G., Yakovenchuk D. (2001) Chemical reaction-diffusion implementation of finding the shortest paths in a labyrinth. Physical Review E 63: 026607CrossRefGoogle Scholar
  54. Ray T. (1994) Evolution, complexity, entropy, and artificial reality. Physica D 75: 239–263CrossRefGoogle Scholar
  55. Ray, T. (2001). Evolution of complexity: Tissue differentiation in network Tierra. http://www.isd.atr.co.jp/ray/pubs/atrjournal/index.html.
  56. Rizzotti M. (2000) Early evolution: From the appearance of the first cell to the first modern organisms. Birkhäuser, BostonGoogle Scholar
  57. Roche D. (2001) A bit confused: Creationism and information theory. Skeptical Inquirer 25(2): 40–42Google Scholar
  58. Rothemund, P. W. K., & Winfree, E. (2000). The program-size complexity of self-assembled squares. In Proceedings of the thirty-second annual ACM symposium on theory of computing, pp. 459–468. ACM.Google Scholar
  59. Rudski J.M., Lischner M.I., Albert L.M. (1999) Superstitious rule generation is affected by probability and type of outcome. Psychological Record 49: 245–260Google Scholar
  60. Sainsbury R.M. (1995) Paradoxes (2nd ed). Cambridge University Press, CambridgeGoogle Scholar
  61. Schmitt A.O., Herzel H. (1997) Estimating the entropy of DNA sequences. Journal of Theoretical Biology 188: 369–377CrossRefGoogle Scholar
  62. Schneider, T. D. (2001). Rebuttal to William A. Dembski’s posting. http://www.lecb.ncifcrf.gov/toms/paper/ev/dembski/rebuttal.html.
  63. Shallit J. (2002) Review of William Dembski, no free lunch. BioSystems 66: 93–99CrossRefGoogle Scholar
  64. Shallit, J. (2004). Dembski’s mathematical achievements. Retrieved May 12 2004, from http://www.pandasthumb.org/pt-archives/000207.html.
  65. Shannon C. (1950) Prediction and entropy of printed English. Bell System Technical Journal 3: 50–64Google Scholar
  66. Sims K. (1994) Evolving 3D morphology and behavior by competition. In: Brooks R.A., Maes P. (eds) Artificial life IV: Proceedings of the fourth international workshop on the synthesis and simulation of living systems. MIT Press, Cambridge, MA, pp 28–39Google Scholar
  67. Steinbock O., Tóth A., Showalter K. (1995) Navigating complex labyrinths: Optimal paths from chemical waves. Science 267: 868–871CrossRefGoogle Scholar
  68. Wein, R. (2000). What’s wrong with the design inference. http://www.metanexus.net/metanexus_online/show_article2.asp?id=2654.
  69. Wilkins J., Elsberry W. (2001) The advantages of theft over toil: The design inference and arguing from ignorance. Biology and Philosophy 16: 711–724. ftp://ftp.wehi.edu.au/pub/wilkinsftp/dembski.pdf.CrossRefGoogle Scholar
  70. Yelen D.R. (1971) The acquisition and extinction of superstitious behavior. Journal of Experimental Research in Personality 5: 1–6Google Scholar
  71. Young M., Edis T. (eds) (2004) Why intelligent design fails. Rutgers University Press, Piscataway, NJGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Lyman Briggs CollegeMichigan State UniversityEast LansingUSA
  2. 2.National Center for Science EducationOaklandUSA
  3. 3.School of Computer ScienceUniversity of WaterlooWaterlooCanada

Personalised recommendations