Skip to main content

Epistemological Constraints When Evaluating Ontological Emergence with Computational Complex Adaptive Systems

Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Natural complex adaptive systems are of particular scientific interest in many domains, as they may produce something new, like structures, patterns, or properties, that arise from the rules of self-organization. These novelties are emergent if they cannot be understood as any property of the components, but as a new property of the system. One of the leading methods to better understand complex adaptive systems is the use of their computational representation. In this paper, we make the case that emergence in computational complex adaptive systems can only be epistemological, as the constraints of computer functions do not allow for the creation of something new, as required for ontological emergence. As such, computer representations of complex adaptive systems are limited in producing emergence, but nonetheless useful to better understand the relationship between emergence and complex adaptive systems.

Keywords

  • Complexity
  • Epistemological emergence
  • Ontological emergence

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-96661-8_1
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-96661-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Axtell, R.: Why Agents? on the Varied Motivations for Agent Computing in the Social Sciences. Center on Social and Economic Dynamics Brookings Institution, Washington, DC (2000)

    Google Scholar 

  2. Bankes, S.C.: Agent-based modeling: a revolution? Proc. Natl. Acad. Sci. 99(suppl 3), 7199–7200 (2002)

    ADS  CrossRef  Google Scholar 

  3. Beckner, C., Blythe, R., Bybee, J., Christiansen, M.H., Croft, W., Ellis, N.C., Holland, J., Ke, J., Larsen-Freeman, D., Schoenemann, T.: Language is a complex adaptive system: position paper. Lang. Learn. 59(s1), 1–26 (2009)

    Google Scholar 

  4. Boden, M.A.: Computer models of creativity. AI Mag. 30(3), 23 (2009)

    CrossRef  Google Scholar 

  5. Brownlee, J.: Complex adaptive systems. Complex Intelligent Systems Laboratory, Centre for Information Technology Research, Faculty of Information Communication Technology, Swinburne University of Technology, Melbourne, Australia (2007)

    Google Scholar 

  6. Buckley, W.: Society as a complex adaptive system. In: Modern Systems Research for the Behavioral Scientist. Aldine Publishing Company (1968)

    Google Scholar 

  7. Buss, S., Papadimitriou, C.H., Tsitsiklis, N.: On the predictability of coupled automata: an allegory about chaos. In: 1990 Proceedings of the 31st Annual Symposium on Foundations of Computer Science, pp. 788–793. IEEE (1990)

    Google Scholar 

  8. Choi, T.Y., Dooley, K.J., Rungtusanatham, M.: Supply networks and complex adaptive systems: control versus emergence. J. Oper. Manage. 19(3), 351–366 (2001)

    CrossRef  Google Scholar 

  9. Dooley, K.: Complex adaptive systems: a nominal definition. Chaos Netw. 8(1), 2–3 (1996)

    Google Scholar 

  10. Dreyfus, H.L.: What Computers Can’t Do: The Limits of Artificial Intelligence. Harper & Row, New York (1972)

    Google Scholar 

  11. Dreyfus, H.L.: What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press, Boston (1992)

    Google Scholar 

  12. Godschalk, D.R.: Urban hazard mitigation: creating resilient cities. Nat. Hazards Rev. 4(3), 136–143 (2003)

    CrossRef  Google Scholar 

  13. Hébert-Dufresne, L., Pellegrini, A.F., Bhat, U., Redner, S., Pacala, S.W., Berdahl, A.M.: Edge fires drive the shape and stability of tropical forests. Ecol. Lett. 21(6), 794–803 (2018)

    CrossRef  Google Scholar 

  14. Holland, J.H.: Complex adaptive systems. Daedalus 17–30 (1992)

    Google Scholar 

  15. Holland, J.H.: Hidden Order: How Adaptation Builds Complexity. Addison Wesley Publishing Company, Boston (1995)

    Google Scholar 

  16. Humphreys, P.: Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford University Press, Oxford (2004)

    CrossRef  Google Scholar 

  17. Ingwersen, W.W., Garmestani, A.S., Gonzalez, M.A., Templeton, J.J.: A systems perspective on responses to climate change. Clean Technol. Environ. Policy 16(4), 719–730 (2014)

    CrossRef  Google Scholar 

  18. Kauffman, S.A.: The origins of order: self-organization and selection in evolution. In: Spin Glasses and Biology, pp. 61–100. World Scientific (1992)

    Google Scholar 

  19. Levin, S.A.: Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1(5), 431–436 (1998)

    CrossRef  Google Scholar 

  20. Lewes, G.H.: Problems of Life and Mind. Trübner & Company, London (1877)

    Google Scholar 

  21. Maier, M.W.: The role of modeling and simulation in system of systems development. In: Rainey, L.B., Tolk, A. (eds.) Modeling and Simulation Support for System of Systems Engineering Applications. Wiley (2015)

    Google Scholar 

  22. McCarthy, I.P., Tsinopoulos, C., Allen, P., Rose-Anderssen, C.: New product development as a complex adaptive system of decisions. J. Prod. Innov. Manage. 23(5), 437–456 (2006)

    CrossRef  Google Scholar 

  23. Miller, J.H., Page, S.E.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life: An Introduction To Computational Models of Social Life. Princeton University Press, Princeton (2009)

    CrossRef  Google Scholar 

  24. Norman, M.D., Koehler, M.T., Pitsko, R.: Applied complexity science: enabling emergence through heuristics and simulations. In: Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach, pp. 201–226 (2018)

    Google Scholar 

  25. O’Connor, T., Wong, H.Y.: Emergent properties. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy, Summer 2015 edn. Metaphysics Research Lab, Stanford University (2015)

    Google Scholar 

  26. Payne, J.L., Khalid, F., Wagner, A.: RNA-mediated gene regulation is less evolvable than transcriptional regulation. Proc. Natl. Acad. Sci. 201719138 (2018)

    Google Scholar 

  27. Rouse, W.B.: Engineering complex systems: implications for research in systems engineering. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 33(2), 154–156 (2003)

    CrossRef  Google Scholar 

  28. Rouse, W.B.: Health care as a complex adaptive system: implications for design and management. Bridge Wash. Natl. Acad. Eng. 38(1), 17 (2008)

    Google Scholar 

  29. Sheard, S.A., Mostashari, A.: Principles of complex systems for systems engineering. Syst. Eng. 12(4), 295–311 (2009)

    CrossRef  Google Scholar 

  30. Silberstein, M., McGeever, J.: The search for ontological emergence. Philos. Q. 49(195), 201–214 (1999)

    CrossRef  Google Scholar 

  31. Tolk, A.: Simulation and modeling as the essence of computational science. In: Proceedings of the 50th Summer Computer Simulation Conference (2018)

    Google Scholar 

  32. Tolk, A., Diallo, S., Mittal, S.: Complex systems engineering and the challenge of emergence. In: Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach, pp. 79–97. Wiley (2018)

    Google Scholar 

  33. Zeigler, B.P., Mittal, S.: System theoretic foundations for emerging behavior modeling: the case of emergence of human language in a resource-constrained complex intelligent dynamical system. In: Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach, pp. 35–57. Wiley (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas Tolk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tolk, A., Koehler, M.T.K., Norman, M.D. (2018). Epistemological Constraints When Evaluating Ontological Emergence with Computational Complex Adaptive Systems. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_1

Download citation