Dynamics of subjective uncertainty in complex problem solving

1627

Abstract

The study of processes of complex problems solving is an important task of many areas of modern psychological science, which can be successfully solved at the intersection of different disciplines, i.e. through an interdisciplinary approach. The present work is devoted to the experimental study of complex problems solving in a situation of uncertainty. The experimental situation required the construction of an effective system of management of the virtual factory on the basis of the developed computer model of its activity and in accordance with an adequate to reality dynamic scenario. Comparative analysis of the indicators of the effectiveness of complex problems solving, as well as indicators of the resistance to the situation of uncertainty of the two groups of subjects – beginners and experts – indicates a significant positive relationship between tolerance to uncertainty and successful performance of the task in the group of experts, as well as the absence of the significant differences between the indicators of successful performance of the task and the indicators of tolerance for uncertainty in the group of beginners. Results of the study suggest that tolerance to uncertainty, along with the experience and cognitive abilities of a person, is an important factor that affects the effective implementation of the management of complex technological systems.

General Information

Keywords: complex problems solving, uncertainty, dynamics, computer scenario

Journal rubric: Cognitive Psychology

Article type: scientific article

For citation: Eliseenko A.S. Dynamics of subjective uncertainty in complex problem solving . Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2013. Vol. 6, no. 3, pp. 16–30. (In Russ., аbstr. in Engl.)

References

  1. Barabanshikov V. A. Princip sistemnosti v sovremennoj psihologii // Psihologija. Zhurnal GU-VShE. 2004. № 4. S. 3–17.
  2. Derner D. Logika neudachi. M.: Smysl, 1997.
  3. Korotkova A. V. Specifika orientirovochnoj osnovy v myslitel'noj dejatel'nosti pri reshenii kompleksnyh problem. M., 2005.
  4. Podd'jakov A. N. Neopredelennost' v reshenii kompleksnyh problem // Chelovek v situacii neopredelennosti. M., 2007. S. 177–193.
  5. Berry D. C., Broadbent D. E. On the relationship between task performance and associated verbalizable knowledge // Quarterly Journal of Experimental Psychology. 1984. V. 36. P. 209–231.
  6. Brehmer B. Dynamic decision-making—human control of complex-systems //Acta Psychologica. 1992. V. 81. P. 211–241.
  7. Broadbent D., Fitzgerald P., Broadbent M. H. Implicit and explicit knowledge in the control of complex systems // British Journal of Psychology. 1986. V. 77. P. 33–50.
  8. Busemeyer J. R. Dynamic decision making // International Encyclopedia of the Social and Behavioral Sciences: Methodology, Mathematics and Computer Science / Eds. N. J. Smelser, P. B. Bates. Oxford: Elsevier, 2002. P. 3903–3908.
  9. Complex Problem Solving / Eds. J. R. Sternberg, P. Frensch. Hillsdale, N Y: Lawrence Erlbaum associates, 1991.
  10. Complex Problem Solving: The European Perspective / Eds. P. A. Frensch, J. Funke. Hillsdale, N J: Lawrence Erlbaum, 1995.
  11. Frensch P., Funke J. Definitions, traditions and a general framework for understanding complex problem solving // Complex Problem Solving: The European Perspective / Eds. P. A. Frensch, J. Funke. Hillsdale, N J: Lawrence Erlbaum, 1995. P. 3–25.
  12. Greco V., Roger D. Coping with uncertainty: the construction and validation of a new measure // Personality and Individual Differences. 2001. V. 31. P. 519–534.
  13. Klein G. The recognition-primed decision model: looking back, looking forward // Naturalistic Decision Making / Eds. C. Zsambok, G. Klein. Mahwah, N J: Lawrence Erlbaum Associates, 1997. P. 285–292.
  14. Quesada J., Kintsch W., Gomez E. Complex problem-solving: a field in search of a definition? // Theoretical Issues in Ergonomics Science. 2005. V. 6. № 1. P. 5–33.
  15. Simon H. The Structure of Ill-Structured Problems // Artificial Intelligence. 1973. V. 4. P. 181–201.

Information About the Authors

Alexander S. Eliseenko, Master Sci. in Psychology, Trainee-researcher of the Laboratory of Cognitive studies of the Faculty of Psychology, National Research University Higher School of Economics, Moscow, Russia, e-mail: aseliseenko@edu.hse.ru

Metrics

Views

Total: 4251
Previous month: 38
Current month: 1

Downloads

Total: 1627
Previous month: 5
Current month: 0