Determining Optimization-Risk Profiles for Individual Decision Makers

  • Stephen J. Guastello
  • Anthony F. Peressini
Part of the Evolutionary Economics and Social Complexity Science book series (EESCS, volume 13)


Investment funds typically vary with regard to the emphasis that the managers place on acceptable risk and expected returns on investment. This chapter highlight a nonlinear analytic strategy, orbital decomposition (ORBDE) for identifying and extracting patterns of categorical events from time series data. The contributing constructs from symbolic dynamics, chaos, and entropy are described in conjunction with the central ORBDE algorithm. A study in task switching, which can alleviate or induce cognitive fatigue, is used an illustrative example of the basic mode of analysis. The aggregate more of ORBDE allows category codes from multiple variables to be assigned to each event in a time series. An illustrative example of the aggregate mode is presented for risk profile analysis in financial decisions. The results open up many possibilities for studying sequences of decisions made by fund managers and individual investors to determine profiles of risk acceptance, expected returns, and other features of portfolio management.


Intimate Partner Violence Shannon Entropy Task Switching Couple Oscillator Topological Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Guastello, S. J. (2000). Symbolic dynamic patterns of written exchange: Hierarchical structures in an electronic problem solving group. Nonlinear Dynamics, Psychology, and Life Sciences, 4, 169–188.CrossRefGoogle Scholar
  2. Guastello, S. J., Hyde, T., & Odak, M. (1998). Symbolic dynamic patterns of verbal exchange in a creative problem solving group. Nonlinear Dynamics, Psychology, and Life Sciences, 2, 35–58.CrossRefGoogle Scholar
  3. Guastello, S. J., Peressini, A. F., & Bond, R. W., Jr. (2011). Orbital decomposition for ill-behaved event sequences: Transients and superordinate structures. Nonlinear Dynamics, Psychology, and Life Sciences, 15, 465–476.Google Scholar
  4. Guastello, S. J., Gorin, H., Huschen, S., Peters, N. E., Fabisch, M., & Poston, K. (2012). New paradigm for task switching strategies while performing multiple tasks: Entropy and symbolic dynamics analysis of voluntary patterns. Nonlinear Dynamics, Psychology, and Life Sciences, 16, 471–497.Google Scholar
  5. Guastello, S. J., Gorin, H., Huschen, S., Peters, N. E., Fabisch, M., Poston, K., & Weinberger, K. (2013). The minimum entropy principle and task performance. Nonlinear Dynamics, Psychology, and Life Sciences, 17, 405–424.Google Scholar
  6. Haken, H. (1984). The science of structure: Synergetics. New York: Van Nostrand Reinhold.Google Scholar
  7. Heath, R. A. (2000). Nonlinear dynamics: Techniques and applications in psychology. Mahwah: Erlbaum.Google Scholar
  8. Jiménez-Montaño, M. A., Feistel, R., & Diez-Martínez, O. (2004). Information hidden in signals and macromolecules I. Symbolic time-series analysis. Nonlinear Dynamics, Psychology, and Life Sciences, 8, 445–478.Google Scholar
  9. Katerndahl, D. A., & Parchman, M. L. (2010). Dynamical differences in patient encounters involving uncontrolled diabetes. Journal of Evaluation in Clinical Practice, 16, 211–219.CrossRefGoogle Scholar
  10. Katerndahl, D., Ferrer, R., Burge, S., Becho, J., & Wood, R. (2010). Recurrent patterns of daily intimate partner violence and environment. Nonlinear Dynamics, Psychology, and Life Sciences, 14, 511–524.Google Scholar
  11. Katerndahl, D., Burge, S., Ferrer, R., Becho, J., & Wood, R. (2015). Recurrent multi-day patterns of intimate partner violence and alcohol intake in violent relationships. Nonlinear Dynamics, Psychology, and Life Sciences, 19, 41–63.Google Scholar
  12. Lathrop, D. P., & Kostelich, E. J. (1989). Characterization of an experimental strange attractor by periodic orbits. Physics Review, 40, 4028–4031.CrossRefGoogle Scholar
  13. Nathan, D. E., Guastello, S. J., Prost, R. W., & Jeutter, D. C. (2012). Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics. Nonlinear Dynamics, Psychology, and Life Sciences, 16, 37–59.Google Scholar
  14. Newhouse, R., Ruelle, D., & Takens, F. (1978). Occurrence of strange attractors: An axiom near quasi-periodic flows on Tm, m ≥ 3. Communications in Mathematical Physics, 64, 35–41.CrossRefGoogle Scholar
  15. Nicolis, G., & Prigogine, I. (1989). Exploring complexity. New York: Freeman.Google Scholar
  16. Peressini, A. F., & Guastello, S. J. (2014). Orbital decomposition: A short user’s guide to ORBDE v2.4. [Software]. Retrieved May 1, 2014, from Menu 4.
  17. Pincus, D. (2001). A framework and methodology for the study of nonlinear, self-organizing family dynamics. Nonlinear Dynamics, Psychology, and Life Sciences, 5, 139–174.CrossRefGoogle Scholar
  18. Pincus, D. (2014). One bad apple: Experimental effects of psychological conflict on social resilience. Interface Focus, 4, 20014003.CrossRefGoogle Scholar
  19. Pincus, D., & Guastello, S. J. (2005). Nonlinear dynamics and interpersonal correlates of verbal turn-taking patterns in group therapy. Small Group Research, 36, 635–677.CrossRefGoogle Scholar
  20. Pincus, D., Fox, K. M., Perez, K. A., Turner, J. S., & McGee, A. R. (2008). Nonlinear dynamics of individual and interpersonal conflict in an experimental group. Small Group Research, 39, 150–178.CrossRefGoogle Scholar
  21. Pincus, D., Ortega, D. L., & Metten, A. (2011). Orbital decomposition for multiple time series comparisons. In S. J. Guastello & R. A. M. Gregson (Eds.), Nonlinear dynamical systems analysis for the behavioral sciences using real data (pp. 517–538). Boca Raton: CRC Press.Google Scholar
  22. Pincus, D., Eberle, K., Walder, C. S., Kemp, A. S., Lanjavi, M., & Sandman, C. A. (2014). The role of self-injury in behavioral flexibility and resilience. Nonlinear Dynamics, Psychology, and Life Sciences, 18, 277–296.Google Scholar
  23. Prigogine, I., & Stengers, I. (1984). Order out of chaos: Man’s new dialog with nature. New York: Bantam.Google Scholar
  24. Puu, T. (1993). Nonlinear economic dynamics (3rd ed.). New York: Springer.CrossRefGoogle Scholar
  25. Puu, T. (2000). Attractors, bifurcation and chaos: Nonlinear phenomena in economics. New York: Springer.CrossRefGoogle Scholar
  26. Robinson, C. (1999). Dynamical systems: Stability, symbolic dynamics, and chaos (2nd ed.). Boca Raton: CRC Press.Google Scholar
  27. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.CrossRefGoogle Scholar
  28. Spohn, M. (2008). Violent societies: An application of orbital decomposition to the problem of human violence. Nonlinear Dynamics, Psychology, and Life Sciences, 12, 87–115.Google Scholar
  29. Sprott, J. C. (2003). Chaos and time series analysis. Oxford: New York.Google Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.Marquette UniversityMilwaukeeUSA

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