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Two Conceptual Models for Aspects of Complex Systems Behavior

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How Nature Works

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 5))

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Abstract

This chapter presents two toy models dealing with trade-off issues arising in complex systems research. After comparison of modeling in classical physics and complex systems theorizing these models are discussed in detail. The first examines the trade-off between stability and flexibility in an environment subject to random fluctuations. The second compares possible response strategies in cases of potential risk and reward. The first model illustrates the general complex systems concept of virtual stability, defined as a condition in which a system maintains itself in an unstable state between attracting response states in order to gain flexibility in the face of random environmental fluctuations. The second model considers the trade-off between quickness and accuracy in cases of bounded decision time and information. Both models relate to decision processes in complex adaptive systems and some of their implications in this regard are discussed.

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Notes

  1. 1.

    This ignores the fact that a clock imposes a fixed unit of time. The actual length of a day, hence the position of the sun, depends on season. Abstraction from reality always introduces a lack of fit with reality that must be compensated in practice. This is the difference between theoretical science and engineering.

  2. 2.

    If the spectrum of external noise resonates with system dynamics then even small fluctuations can grow to macroscopic size in a process of fluctuation enhancement [e.g., 23, 24].

  3. 3.

    The technical questions that arise relate to how high this frequency needs to be. This, in turn, involves the time scale for falling out of the unstable state, and the energy required for corrective actions of varying strengths.

  4. 4.

    Such a model could use filtering and template matching of perceptions, for example, in order to find the best behavioral response fit.

References

  1. E.A. Jackson, A first look at the second “metamorphosis” of science. SFI Technical Report 95-01-001(1995a)

    Google Scholar 

  2. J. Casti, The Computer as Laboratory. Complexity 4(5) 12 (1999)

    Google Scholar 

  3. T.S. Kuhn, in The Essential Tension. ed. by T.S. Kuhn. The Essential Tension, (University of Chicago Press, Chicago, 1977), pp. 225–239

    Google Scholar 

  4. W. Brian Arthur , Inductive reasoning and bounded rationality (The El Farol problem). American Economical Review (Papers and Proceedings), vol. 84 (1994), p.406

    Google Scholar 

  5. B. Voorhees, J. Senez, T. Keeler, M. Connors, A population model of the stability-flexibility tradeoff. Adv. Complex Syst. 11(3), 443–470 (2008)

    Article  MathSciNet  Google Scholar 

  6. B. Voorhees, Virtual stability: constructing a simulation model. Complexity 15(2), 31–44 (2009)

    Article  MathSciNet  Google Scholar 

  7. F. Heylighen, in Principles of systems and cybernetics. ed. by R. Trapel Cybernetics and Systems 92 (World Scientific, Singapore, 1992)

    Google Scholar 

  8. R.M. May, Stability and Complexity of Model Ecosystems (Princeton University Press, Princeton, 1973)

    Google Scholar 

  9. S. Gavrilets, Fitness Landscapes and the Origin of Species (Princeton University Press, Princeton, 2004)

    Google Scholar 

  10. B. Voorhees, Axiomatic theory of hierarchical systems. Behav. Sci. 28, 24–34 (1982)

    Article  Google Scholar 

  11. W. Stefan, Wu Yihren, Automata with hierarchical control and evolutionary learning. BioSystems 21, 115–124 (1988)

    Article  Google Scholar 

  12. S. Kraut, U. Feudel, C. Grebogi, Preference of attractors in noisy multistable systems. Phys. Rev. E 59(5), 5253–5260 (1999)

    Article  Google Scholar 

  13. U. Feudel, C. Grebogi, Multistability and the control of complexity. Chaos 7(4), 597–604 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. C. Foster Glenn, A. W. Hubler, Robuse and efficient interactions with complex systems. Proceedings of IEEE International Conference on Systems, Man & Cybernetics 2029–2034, (2003)

    Google Scholar 

  15. U. Feudel, C. Grebogi, Multistability and the control of complexity. Chaos 7(4), 597–604 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  16. H. Touchette, S. Lloyd, Information-theoretic limits of control. Phys. Rev. Lett. 84, 1156–1159 (2000)

    Article  Google Scholar 

  17. Y. Asai, Y. Tasaka, K. Nomura, T. Nomura, M. Casadio, P. Morasso, A model of postural control in quiet standing: robust compensation of delay-induced instability using intermittent activation of feedback control. PLoS ONE 4(7), e169 (2009). doi:10.1371/journal.pone.ooo6169

    Article  Google Scholar 

  18. E.R. Kandel, J.H. Schwartz, T.M. Jessell, The Principles of Neural Science, 4th edn. (McGraw-Hill, NY, 2000)

    Google Scholar 

  19. Laura N. Borodinsky, How fast can you go? Nature 440, 158–159 (2006)

    Article  Google Scholar 

  20. W.R. Ashby, Introduction to Cybernetics (Wiley, London, 1956)

    Google Scholar 

  21. Juan M.R. Parrondo, Gregory P. Harmer, Derek Abbott, New paradoxical games based on Brownian ratchets. Phys. Rev. Lett. 85(24), 5226–5229 (2000)

    Article  Google Scholar 

  22. J.S. Nicolis, T. Bountis, K. Togias, The dynamics of self-referential paradoxical games. Dyn. Syst.: Int. J. 16(4), 319–332 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. Yu. Itoh, Kei-ichi Tainaka, Spatial enhancement of population uncertainty in model ecosystems. J. Phys. Soc. Jpn. 73(1), 53–59 (2004)

    Article  Google Scholar 

  24. B. Voorhees, C. Luxford, R. Arthur, Emergence of cellular automata rules through fluctuation enhancement. Int. J. Unconventional Comput. 1(1), 69–100 (2005)

    Google Scholar 

  25. Alfred W. Hubler, Predicting complex systems with a holistic approach. Complexity 10, 11–16 (2005)

    Article  Google Scholar 

  26. Suso Kraut, Ulrike Feudel, Celso Grebogi, Preference of attractors in noisy multistable systems. Phys. Rev. E 59(5), 5253–5260 (1999)

    Article  Google Scholar 

  27. R. Cressman, Evolutionary Dynamics and Extensive Form Games (MIT Press, Cambridge, 2003)

    MATH  Google Scholar 

  28. M.A. MacIver, N.A. Patankar, A.A. Shirgaonkar, Energy-information trade-offs between movement and sensing. PLoS Comput. Biol. 6(5), e1000769 (2010)

    Article  MathSciNet  Google Scholar 

  29. M.A. MacIver, Neuroethology: from morphological computation to planning, in The Cambridge Handbook of Situated Cognition, ed. by P. Robbins, M. Aydede (Cambridge University Press, NY, 2009), pp. 480–504

    Google Scholar 

  30. B.S. Grant, Fine tuning the peppered moth paradigm. Evolution 53(3), 980–984 (1999)

    Article  Google Scholar 

  31. R. Bogacz, E.J. Wagenmaker, B.U. Forstmann, S. Nieuwenhuis, The neural basis of the speed-accuracy tradeoff. Trends Neurosci. 33, 10–16 (2009)

    Article  Google Scholar 

  32. D.A. Winter, Human balance and posture control during standing and walking. Gait & Posture 3, 193–214 (1995)

    Article  Google Scholar 

  33. A. Pastor-Bernier, E. Tremblay, P. Cisek, Dorsal premotor cortex is involved in switching motor plans. Frontiers Neuroeng. 5, 1–15 (2012)

    Google Scholar 

  34. P. Gawthrop, I. Loram, M. Lakie, H. Golle, Intermittent control: a computational theory of human control. Biol. Cybern. 104, 31–51 (2011)

    Article  MATH  Google Scholar 

  35. H. Gintis, S. Bowles, R. Boyd, E. Fehr, Explaining altruistic behavior in humans. Evol. Hum. Behav. 24, 153–172 (2003)

    Article  Google Scholar 

  36. P. Richardson, R. Boyd, Not by Genes Alone: How Culture Transformed Human Evolution (University of Chicago Press, Chicago, 2004)

    Book  Google Scholar 

  37. M. Nowak, R. Highfield, Supercooperators (Free Press, NY, 2011)

    Google Scholar 

  38. D. Rinberg, A. Koulakov, A. Gelperin, Speed-accuracy tradeoff in olfaction. Neuron 51, 351–358 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Center for Science, Athabasca University, 1 University Drive, Athabasca, AB, T9S 3A3, Canada

    Burton Voorhees

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  1. Burton Voorhees
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Correspondence to Burton Voorhees .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and, VŠB-TUO, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic

    Ivan Zelinka

  2. , Institute for Theoretical Physics, University of Tübingen, Room D8 A32, Auf der Morgenstelle 14, Tübingen, 72076, Germany

    Ali Sanayei

  3. Kroto Research Institute, Department of Computer Science, The University of Sheffield, Regent Court, 211, Portobello, S1 4DP, United Kingdom

    Hector Zenil

  4. , Institute for Physical and, University of Tübingen, Auf der Morgenstelle 8, Tübingen, 72076, Germany

    Otto E. Rössler

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Voorhees, B. (2014). Two Conceptual Models for Aspects of Complex Systems Behavior. In: Zelinka, I., Sanayei, A., Zenil, H., Rössler, O. (eds) How Nature Works. Emergence, Complexity and Computation, vol 5. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00254-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-00254-5_6

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