, Volume 33, Issue 4, pp 467–485 | Cite as

Rethinking the experiment: necessary (R)evolution

  • Mihai NadinEmail author
Original Article


The current assumptions of knowledge acquisition brought about the crisis in the reproducibility of experiments. A complementary perspective should account for the specific causality characteristic of life by integrating past, present, and future. A “second Cartesian revolution,” informed by and in awareness of anticipatory processes, should result in scientific methods that transcend the theology of determinism and reductionism. In our days, science, itself an expression of anticipatory activity, makes possible alternative understandings of reality and its dynamics. For this purpose, the study advances G-complexity for defining and comparing decidable and undecidable knowledge. AI and related computational expressions of knowledge could benefit from the awareness of what distinguishes the dynamics of life from any other expressions of change.


Experiment Reproducibility Decidability Non-deterministic Anticipation 



This study is the outcome of a long-term endeavor. Interactions with distinguished colleagues and many young researchers helped in defining the foundation for this work. The author would like to acknowledge Robert Rosen for his pioneering work in defining the living, and colleagues from the University of California–Berkeley, Professors Harry Rubin (Biology) and Lotfi Zadeh (Electrical Engineering and Computer Science); Professor Solomon Marcus (mathematician, Member of the Romanian Academy), Aloisius H. Louie, Stuart Kauffman, Kalevi Küll (University of Tartu, Estonia), and more recently Arran Gare (Swinburne University of Technology, Melbourne, Australia) for their intellectual openness to new ideas and their encouragement. An anonymous reviewer suggested the inclusion of arguments pertinent to AI (and ALife). Luigi Longo took time to discuss in detail the arguments presented in a preprint version of this study. Both deserve acknowledgment and my gratitude.

Compliance with ethical standards

Conflict of interest

There are no conflicting interests to be reported.


  1. Artificial Intelligence (2016) Life in 2030 One Hundred Year Study on Artificial Intelligence. Report of the 2015 Study Panel, September 2016. Retrieved February 13, 2017
  2. Bailey R (2016) Most scientific findings are wrong or useless, reason. August 26, 2016 ( Scholar
  3. Baker M (2016) 1500 scientists lift the lid on reproducibility. Nature 533:452–454 (corrected 28 July 2016)CrossRefGoogle Scholar
  4. Ball P (2008) Cellular memory hints at the origins of intelligence. Nature 451:385. doi: 10.1038/451385a CrossRefGoogle Scholar
  5. Ball P (2016) The mathematics of science’s broken reward system. Nat News Comment. doi: 10.1038/nature.2016.2097
  6. Baluska F, Mancuso S, Volkmann D, Stefano M (2006) Communication in plants, in neuronal aspects of plant life. Springer, Berlin/HeidelbergGoogle Scholar
  7. Barabasi AL (2009) Scale-free networks: a decade and beyond. Science 325:5939, 412–413MathSciNetCrossRefGoogle Scholar
  8. Bassin PV, Bernstein NA, Latash LP (1966) Towards the problem of the relations between brain architecture and functions in its modern understanding. Physiology in Clinical Practice. Nauta (in Russian), Moscow, pp 38–49Google Scholar
  9. Beatty J (1995) The evolutionary contingency thesis. In: Wolter G, Lennox J (eds) Concepts, theories and rationality in the biological sciences. University of Pittsburgh Press, Pittsburgh, pp 45–81Google Scholar
  10. Beatty J (1997) Why do biologists argue like they do?Philos Sci 6(4 supp):S432–S443CrossRefGoogle Scholar
  11. Ben-Menachem Y (1997) Replaying life’s tape. J Philos 103(7):336–362Google Scholar
  12. Bernstein NA (1967) The coordination and regulation of movements. Pergamon Press, Oxford (see also: Nadin M (ed) Learning from the past. Early Soviet/Russian contributions to a science of anticipation. Cognitive Systems Monographs. Springer, Cham CH2015)Google Scholar
  13. Berry DK, Caplan ME, Horowitz CJ, Huber G, Schneider AS (2016) “Parking-garage” structures in nuclear astrophysics and cellular biophysics. Phys Rev C 94:0558901. doi: 10.1103/physrevc.94.055801
  14. Bostrum N (2003) Are you living in a computer simulation? Philos Q53(211):243–255CrossRefGoogle Scholar
  15. Brunton PJ, Russell JA (2008) The expectant brain: adapting for motherhood. Nat Rev Neurosci 9:11–25CrossRefGoogle Scholar
  16. Chomicki G, Renner SS (2016) Obligate plant farming by a specialized ant. Nat Plants 2:16181. doi: 10.1038/nplants.2016.181
  17. Clay R et al (2015) Reproducibility project: psychology. Science 349:6251Google Scholar
  18. Constantinople CM, Bruno RM (2013) Deep cortical layers are activated directly by thalamus. Science 340:6140, 1591–1594CrossRefGoogle Scholar
  19. Conway Morris S (2003) Life’s solution: inevitable humans in a lonely universe. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  20. Desjardins E (2011) Biology and philosophy 26(3):339–364Google Scholar
  21. Dutton L (2015) Nature’s marvelous machines. Research frontiers in bioinspired energy: molecular learning from natural systems. Washington DC: National Academies of Science, Engineering and Medicine. Accessed 9 Nov 2016
  22. Eberbach E, Goldin D, Wegner P (2004) Turing’s ideas and models of computation. In: Teuscher C (ed) Alan turing. Life and Legacy of a Great Thinker. Springer, Berlin/HeidelbergGoogle Scholar
  23. Eklund A, Nichols TE, Knutsson H (2016) Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. PNAS 113(28):7900–7905. doi: 10.1073/pnas.1602413113 (Epub 2016 Jun 28)
  24. Ellis GFR (2005) Physics, complexity and causality. Nature 435:743CrossRefGoogle Scholar
  25. Ellis GFR (2006) Physics in the real world. Found Phys 36(2):227–262CrossRefGoogle Scholar
  26. Elsasser W (1998) Reflections on a theory of organisms. Holism in biology. John Hopkins University Press, BaltimoreGoogle Scholar
  27. England J (2013) Statistical physics of self-replication. J Chem Phys 139:12. doi: 10.1063/1.4818538 CrossRefGoogle Scholar
  28. Fehr J, Heiland J, Himpe C, Saak J (2016) Best practices for replicability, reproducibility and reusability of computer-based experiments exemplified by model reduction software. 2016 (arXiv:1607.01191v)Google Scholar
  29. Gare A (2013) Overcoming the Newtonian paradigm: the unfinished project of theoretical biology from a Schellingian perspective. Prog Biophys Mol Biol 113(1):5–24CrossRefGoogle Scholar
  30. GelfandI M (2007) In: Borovik AV (ed) Mathematics Under the Microscope. Notes on cognitive aspects of mathematical practice. September 5, 2007. Creative Commons,
  31. GelfandI M, Tsetlin ML (1966) On mathematical modeling of the mechanisms of the central nervous system. In: Gelfand IM, Gurfinkel VS, Fomin SV, Tsetlin ML (eds) Models of the structural-functional organization of certain biological systems. Moscow: Nauka, 9–26 (In Russian; a translation is available in the 1971 edition by MIT. Press, Cambridge, MA)Google Scholar
  32. Goodell J (2016) Inside the artificial intelligence revolution: a special report (Pt 1), Rolling Stone, February 29, 2016. Retrieved February 13, 2017
  33. Goodstein D (2002) Scientific misconduct. Academe 88(1), 28–31CrossRefGoogle Scholar
  34. Gould SJ (1989) Wonderful life: the burgess shale and the nature of history. W.W. Norton, New YorkGoogle Scholar
  35. Handler P (ed) (1970) Biology and the future of man. National Academies Press, Washington DCGoogle Scholar
  36. Heisenberg W (1927) Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift für Physik (in German) 43(3–4):172–198Google Scholar
  37. Horrigan S et al (2017) Replication study: melanoma genome sequencing reveals frequent PREX2 mutations. eLife 6:e21634Google Scholar
  38. Horton R (1999) Scientific misconduct: exaggerated fear but still real and requiring a proportionate response. Lancet 354:9172, 7–8CrossRefGoogle Scholar
  39. Horton R (2015) Offline: what is medicine’s 5 sigma? Comment. Lancet 385:9976, 1380Google Scholar
  40. Jaegwon K (2009) Mental causation. In: McGlaughlin B, Beckermann A, Walter S (eds) The Oxford handbook of philosophy of mind, Oxford Handbooks Online, 40.
  41. Kandela I et al (2017) Replication study: discovery and preclinical validation of drug indications using compendia of public gene expression data. eLife 6:e17044Google Scholar
  42. Kauffman SA (2000) Emergence and story: beyond Newton, Einstein and Bohr? Investigations, Chap 6. Oxford University Press, OxfordGoogle Scholar
  43. Kauffman SA, Gare A (2015) A beyond descartes and newton: recovering life and humanity. Prog Biophys Mol Biol119:219–244CrossRefGoogle Scholar
  44. Kuintzle RC, Choq ES, Westby TN, Gvakharia BO, Giebultowicz JM, Hendrix DA (2017) Circadian deep sequencing reveals stress-response genes that adopt robust rhythmic expression during aging, Nat Commun. Accessed 24 Feb 2017. doi: 10.1038/ncomms14529 CrossRefGoogle Scholar
  45. Latash ML (2016) Towards physics of neural processes and behavior. Neurosci Biobehav Rev 69:136–146. doi: 10.1016/j.neubiorev.2016.08.005 CrossRefGoogle Scholar
  46. Leamer SE (2009) Macroeconomic patterns and stories. Springer, Berlin/HeidelbergzbMATHGoogle Scholar
  47. Lenski RE, Travisano M (1994) Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc Natl Acad Sci 91:15, 6808–6814CrossRefGoogle Scholar
  48. Li S, Stamfer M, Williams DR, VanderWeele TJ (2016) Association between religious service attendance and mortality among women. JAMA Intern Med 176(6):777–785CrossRefGoogle Scholar
  49. Libby T et al (2012) Tail-assisted pitch control in lizards, robots and dinosaurs. Nature Lett 481:7380, 181–184CrossRefGoogle Scholar
  50. Longo G (2017) How future depends on past and rare events in systems of life. Foundations of Science, 2017, IEA Nantes.
  51. Longo G, Montevil M (2013) Extended criticality, phase spaces and enablement in biology, Chaos, Solitons and Fractals, Emerg Crit Brain Dyn 55:64–79. doi: 10.1016/j.chaos.2013.03.008 CrossRefGoogle Scholar
  52. Longo G, Montévil M, Kauffman S (2012) No entailing laws, but enablement in the evolution of the biosphere. arXiv:1201.2069Google Scholar
  53. López-Suárez M, Neri I, Gammaitoni L (2016) Sub-kBT micro-electromechanical irreversible logic gate. Nat Commun 7CrossRefGoogle Scholar
  54. Losos JB, Jackman TR, Larson A, deQueiroz K, Rogriguez-Schettino L (1998) Contingency and determinism in replicated adaptive radiations of island lizards. Science 279:5359, 2115–2118. doi: 10.1126/science.279.5359.2115 CrossRefGoogle Scholar
  55. Mahon BZ, Anzellotti S, Schwarzbach J, Zampini M, Caramazza (2009) A category-specific organization in the human brain does not require visual experience. Neuron 63:397CrossRefGoogle Scholar
  56. Mallik A, Chanda ML, Levitin DJ (2017) Anhedonia to music and mu-opiods: evidence from the administration of naltrexone. Nat Sci Rep 7. Article number: 41952. Retrieved 23 Feb 2017
  57. Mantis C et al (2017) Replication study: coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. eLife 6:e17584Google Scholar
  58. Mogil JS, MacLeod MR (2017) No publication without confirmation. Nat Comment 542:7642. Accessed 23 Feb 2017CrossRefGoogle Scholar
  59. Nadin M (1997) The civilization of illiteracy. Dresden University Press, DresdenGoogle Scholar
  60. Nadin M (2003) Anticipation—the end is where we start from. Lars Müller Verlag, BaselGoogle Scholar
  61. Nadin M (2004) Project Seneludens.
  62. Nadin M (2011) The anticipatory profile. An attempt to describe anticipation as process. Int J General Syst (special edition), 41(1):43–75. Taylor and Francis, LondonGoogle Scholar
  63. Nadin M (2013a) Anticipation: a bridge between narration and innovation. In: Müller AP, Becker L (eds) Narrative and innovation, management—culture—interpretation. Springer Fachmedien, Wiesbaden, pp 239–263Google Scholar
  64. Nadin M (2013b) The intractable and the undecidable—computation and anticipatory processes. Int J Appl Res Inf Technol Comput 4(3):9–121MathSciNetCrossRefGoogle Scholar
  65. Nadin M (2014) G-complexity, quantum computation and anticipatory processes. Computer Communication and Collaboration, vol 2:1. BA Press, Toronto, pp 16–34Google Scholar
  66. Nadin M (2016a) Medicine: the decisive test of anticipation. In: Nadin M (ed) Anticipation and medicine. Springer, Cham, pp 1–27zbMATHGoogle Scholar
  67. Nadin M (2016b) Anticipation and the brain. In: Nadin M (ed) Anticipation and medicine. Springer, ChamGoogle Scholar
  68. Nadin M (2016c) Anticipation and the brain. In: Nadin M (ed) Anticipation and medicine. Springer, Berlin/HeidelbergGoogle Scholar
  69. Nadin M (2016d) Predictive and anticipatory computing. In: LaPlante P (ed) Encyclopedia of computer science and technology, 2nd edn. Taylor and Francis, London, pp 643–659. doi: 10.1081/E-ecst2-120054027 CrossRefGoogle Scholar
  70. Nadin M (2017) Predictive and anticipatory compuitng. In: Laplante P (ed) Encyclopedia of computer science and technology, 2nd edn. Taylor and Francis, London, pp 643–659Google Scholar
  71. National Research Council Board on Biology (1989) Opportunities in biology. National Academy Press,Washington DCGoogle Scholar
  72. Nature/Editorial (2016) Reality check on reproducibility. Nature 533:437Google Scholar
  73. Neuman J (2006) “Cryptobiosis.” A New Theoretical Perspective. Prog Biophys Mol Biol 92:66CrossRefGoogle Scholar
  74. Nosek B (2015) Estimating the reproducibility of psychological science. Science 349(6251):943Google Scholar
  75. Peirce CS (1932) The collected papers of Charles Sanders Peirce. In: Hartshorne C, Weiss P (eds) Cambridge MA: The Belknap Press of Harvard University Press (Following accepted practice, the reference refers to Vol 5, entry 145)Google Scholar
  76. Peirce CS (1992) On the algebra of logic. In: Houser N, Kloesel C (eds) The essential Peirce: selected philosophical writings, vol 1, 227. Indiana University Press, Bloomington, pp 1867–1893Google Scholar
  77. Pethel S, Hahs D (2011) Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow. Phys Rev Lett 107:128701. Accessed 24 Feb 2017
  78. Picollo S (2013a) Embracing mechanical forces in cell biology. Differentiation 86(3):75–76MathSciNetCrossRefGoogle Scholar
  79. Picollo S (2013b) Developmental biology: mechanics in the embryo. Nature 504:223–225CrossRefGoogle Scholar
  80. Powell R, Mariscal C (2015) Convergent evolution as natural experiment: the tape of life reconsidered. Interface Focus 5:1–13CrossRefGoogle Scholar
  81. Pritsker M (2012) Studies show only 10% of published science are reproducible. What is happening? J Vis Exp.
  82. Replication studies offer much more than technical details (2017) They demonstrate the practice of science at its best. Nature|Editorial 541:7637Google Scholar
  83. Report of Positive Psychology Center (2015)
  84. Rosen R (1991) Life Itself. A comprehensive inquiry into the nature, origin, and fabrication of life (complexity in ecological systems). Columbia University Press, New YorkGoogle Scholar
  85. Rosen R (1999) The Church-Pythagoras Thesis, in essays of life itself. Columbia University Press, New York, pp 63–81Google Scholar
  86. Sarewitz D (2016) Saving science. The New Atlantis 49:5–40 Spring/SummerGoogle Scholar
  87. Schrödinger E (1944) What is life? Macmillan, New YorkGoogle Scholar
  88. Shifferman E (2015) More than meets the fMRI: the unethical apotheosis of neuroimages. J Cognit Neuroethics 3(2):57–116Google Scholar
  89. Smaldino PE, McElreath R (2016) Royal open society. Science 3. doi: 10.1098/rsos.160384 MathSciNetCrossRefGoogle Scholar
  90. Sober E (2008) core questions in philosophy, 5th edn. Core Questions in philosophy: a text with readings, 5th edn. Pearson, LondonGoogle Scholar
  91. Symposium Report (2015) Reproducibility and reliability of biomedical research: improving research practice. Joint statement of the Academy of Medical Sciences, the Biotechnology and Biological Sciences Research Council (BBSRC), the Medical Research Council (MRC) and the Wellcome Trust. October 2015.
  92. Tegmark ME (2014) Our mathematical universe: my quest for the ultimate nature of reality. Knopf, New YorkzbMATHGoogle Scholar
  93. The Sequence of the Human Genome (2001) Science “The Human Genome” 291:5507. American Association for the Advancement of Science, Washington DCGoogle Scholar
  94. Turing AM (1948) Intelligent machinery [technical report]. Teddington: National Physical Laboratory (see also Copeland BJ (ed) 2004 The Essential Turing: seminal writings in Computing Logic, Philosophy, artificial Intelligence, and Artificial Life plus The Secrets of Enigma. Oxford University Press, Oxford)Google Scholar
  95. Turner DD (2009) How much can we know about the causes of evolutionary trends? Biol Philos 24:341. doi: 10.1007/s10539-008-9139-5 CrossRefGoogle Scholar
  96. Uexküll J (1934) Streifzuge durch die Umwelten von von Tieren und Menschen. Berlin: Julius von Springer Verlag (see also A Foray into the Worlds of Animals and Humans with A Theory of Meaning. (O’Neill, J.D., trans.). University of Minnesota Press, Minneapolis 2010)Google Scholar
  97. Vanderweele TJ (2016) Religion and health: a synthesis. In: Peteet JR, Balboni MJ (eds) Spirituality and religion within the culture of medicine: from evidence to practice. Oxford University Press, New YorkGoogle Scholar
  98. Venter JC et al (2001) The sequence of the human genome. Science 291(5507):1304–1351 CrossRefGoogle Scholar
  99. VonNeumann J (1951) The general and logical theory of automata. Cerebral mechanisms in behavior, 1–41. (This statement is cited in many texts, but no precise reference is ever given)Google Scholar
  100. Wheeler JA (1989) Information, physics, quantum: the search for links. In: Proc. 3rd Int. Symp. Foundations of Quantum Mechanics. Tokyo, pp 354–368Google Scholar
  101. Whitesides GM (2015) Reinventing chemistry. Angewandte Chemie Internationale 54(11):3196–3209. doi: 10.1002/anie.201410884 CrossRefGoogle Scholar
  102. Williams GC (1992) Natural Selection domains, levels, and challenges. Oxford University Press, New YorkGoogle Scholar
  103. Williams R (2017) Replication complications. An initiative to replicate key findings in cancer biology yields a preliminary conclusion: it’s difficult. The Scientist.
  104. Windelband W (1907) Geschichte und Naturwissenschaft. Rectoratsreden der Universität Strassburg (History and Natural Science, Rectoral Address). Mohr, Tübingen, pp 35–379Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Institute for Research in Anticipatory SystemsThe University of TexasDallasUSA

Personalised recommendations