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Phases of psychologically optimal learning experience: task-based time allocation model

  • Drago BokalEmail author
  • Mitja Steinbacher
Original Paper
  • 16 Downloads

Abstract

Individual’s preferences, learning ability, passion, and perseverance influence which available learning challenges he will choose, for how long he will persist, what emotions will be experienced while working on those challenges and what utility will be gained from these activities. In our approach to this interdisciplinary problem, we build a bridge between time-allocation models developed within utility theory and empirical emotional experience and learning models from psychology by developing a novel task-based time allocation model. As parameters of the model are highly dynamic, we use Monte Carlo simulations to investigate the phase space of observed emotional states with respect to aforementioned individual’s traits.

Keywords

Flow Grit Utility Task-choice Learning Phase-diagram Monte Carlo simulations 

Mathematics Subject Classification

90B70 97C70 91G60 91B16 

Notes

Acknowledgements

Authors wish to express their thankfulness to Ines Štampar for preparing contour plots and for the assistance with a video abstract, to Aljaž Protić for the assistance with a video abstract (available as a supplementary material to the paper). We are indebted to anonymous referees and participants of the 14th International Symposium on Operational Research in Slovenia for their useful comments and suggestions. Finally, we express our thankfulness to anonymous referees of the Central European Journal of Operations Research for stressing excellent arguments which sharpened the focal points of our paper about rational usage of time and emerging emotions of the practicing agent.

Supplementary material

Supplementary material 1 (mp4 59086 KB)

References

  1. Anzai Y, Simon HA (1979) The theory of learning by doing. Psychol Rev 86(2):124–140CrossRefGoogle Scholar
  2. Arrow KJ (1971) The economic implications of learning by doing. In: Hahn FH (ed) Readings in the theory of growth. Palgrave Macmillan, London, pp 131–149CrossRefGoogle Scholar
  3. Banerjee AV (1992) A simple model of herd behavior. Q J Econ 107(3):797–817CrossRefGoogle Scholar
  4. Becker GS (1965) A theory of the allocation of time. Econ J 81(324):493–517CrossRefGoogle Scholar
  5. Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short term travel decisions. In: Hall RW (ed) Handbook of transportation science. Springer, Boston, MA, pp 5–33CrossRefGoogle Scholar
  6. Csikszentmihalyi M (2004) Good business: leadership, flow, and the making of meaning. Penguin, New YorkGoogle Scholar
  7. Csikszentmihalyi M (2008) Flow: the psychology of optimal experience. Harper perennial modern classics. Harper Collins, New YorkGoogle Scholar
  8. Csikszentmihalyi M (2013) Creativity: the psychology of discovery and invention. Harper perennial modern classics. Harper Collins, New YorkGoogle Scholar
  9. DeSerpa AC (1971) A theory of the economics of time. Econ J 81(324):828–846CrossRefGoogle Scholar
  10. DeTombe DJ (2002) Complex societal problems in operational research. Eur J Oper Res 140(2):232–240CrossRefGoogle Scholar
  11. Dolan RJ (2002) Emotion, cognition, and behavior. Science 298:1191–1194CrossRefGoogle Scholar
  12. Duckworth A (2016) Grit: the power of passion and perseverance. Scribner, New YorkGoogle Scholar
  13. EURO: the Association of European Operational Research Societies, what is operational research? https://www.euro-online.org/web/pages/301/or-and-euro. Accessed 16 July 2018
  14. Jara-Díaz S, Rosales-Salas J (2017) Beyond transport time: a review of time use modeling. Transp Res Part A Policy Pract 97:209–230CrossRefGoogle Scholar
  15. Kitamura R (1984) A model of daily time allocation to discretionary out-of-home activities and trips. Transp Res Part B Methodol 18(3):255–266CrossRefGoogle Scholar
  16. Leitner J, Leopold-Wildburger U (2011) Experiments on forecasting behavior with several sources of information: a review of the literature. Eur J Oper Res 213(3):459–469CrossRefGoogle Scholar
  17. Meiran N (2000) Modeling cognitive control in task-switching. Psychol Res 63(3):234–249CrossRefGoogle Scholar
  18. Monsell S (2003) Task switching. Trends Cogn Sci 7(3):134–140CrossRefGoogle Scholar
  19. Muth J F (1961) Rational expectations and the theory of price movements. Econom J Econom Soc 29(3):315–335Google Scholar
  20. Simon HA (1959) Theories of decision-making in economics and behavioral science. Am Econ Rev 49(3):253–283Google Scholar
  21. Simon HA (1972) Theories of bounded rationality. Decis Organ 1(1):161–176Google Scholar
  22. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458CrossRefGoogle Scholar
  23. Von Neumann J, Morgenstern O (1990) Theory of games and economic behavior, 3rd edn. Princeton University Press, PrincetonGoogle Scholar
  24. Walker J, Ben-Akiva M (2002) Generalized random utility model. Math Soc Sci 43(3):303–343CrossRefGoogle Scholar
  25. Yamamoto T, Kitamura R (1999) An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non-working days. Transportation 26(2):231–250CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Natural Sciences and MathematicsUniversity of MariborMariborSlovenia
  2. 2.Faculty of Business StudiesCatholic InstituteLjubljanaSlovenia

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