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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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.
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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
- Monte Carlo simulations
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