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.
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Notes
This setup here is meant to associate the reader with possibilities to apply empirical methods to the task based time allocation model and underline the possibility for empirical estimation of the influence of variables, such as skills and challenge in our case, on utility from a concrete allocation of time among activities. Note that the empirical estimation of these effects is not the aim of this paper, which is simulation-based.
References
Anzai Y, Simon HA (1979) The theory of learning by doing. Psychol Rev 86(2):124–140
Arrow KJ (1971) The economic implications of learning by doing. In: Hahn FH (ed) Readings in the theory of growth. Palgrave Macmillan, London, pp 131–149
Banerjee AV (1992) A simple model of herd behavior. Q J Econ 107(3):797–817
Becker GS (1965) A theory of the allocation of time. Econ J 81(324):493–517
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 5–33
Csikszentmihalyi M (2004) Good business: leadership, flow, and the making of meaning. Penguin, New York
Csikszentmihalyi M (2008) Flow: the psychology of optimal experience. Harper perennial modern classics. Harper Collins, New York
Csikszentmihalyi M (2013) Creativity: the psychology of discovery and invention. Harper perennial modern classics. Harper Collins, New York
DeSerpa AC (1971) A theory of the economics of time. Econ J 81(324):828–846
DeTombe DJ (2002) Complex societal problems in operational research. Eur J Oper Res 140(2):232–240
Dolan RJ (2002) Emotion, cognition, and behavior. Science 298:1191–1194
Duckworth A (2016) Grit: the power of passion and perseverance. Scribner, New York
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
Jara-Díaz S, Rosales-Salas J (2017) Beyond transport time: a review of time use modeling. Transp Res Part A Policy Pract 97:209–230
Kitamura R (1984) A model of daily time allocation to discretionary out-of-home activities and trips. Transp Res Part B Methodol 18(3):255–266
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):459–469
Meiran N (2000) Modeling cognitive control in task-switching. Psychol Res 63(3):234–249
Monsell S (2003) Task switching. Trends Cogn Sci 7(3):134–140
Muth J F (1961) Rational expectations and the theory of price movements. Econom J Econom Soc 29(3):315–335
Simon HA (1959) Theories of decision-making in economics and behavioral science. Am Econ Rev 49(3):253–283
Simon HA (1972) Theories of bounded rationality. Decis Organ 1(1):161–176
Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458
Von Neumann J, Morgenstern O (1990) Theory of games and economic behavior, 3rd edn. Princeton University Press, Princeton
Walker J, Ben-Akiva M (2002) Generalized random utility model. Math Soc Sci 43(3):303–343
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):231–250
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.
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The research conducted in this paper was partially funded from the research agency of Slovenia, Grants L7-5459, N1-0057, and J1-8130, and research program P1-0297.
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Bokal, D., Steinbacher, M. Phases of psychologically optimal learning experience: task-based time allocation model. Cent Eur J Oper Res 27, 863–885 (2019). https://doi.org/10.1007/s10100-019-00609-0
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DOI: https://doi.org/10.1007/s10100-019-00609-0