TY - GEN
T1 - An Adaptive Network Model for Procrastination Behaviour Including Self-Regulation and Emotion Regulation
AU - Moulie, H.
AU - van den Berg, R.
AU - Treur, J.
PY - 2021
Y1 - 2021
N2 - Procrastination is an ever-growing problem in our current society. It was shown that 80-95% of college students are subject to it. The importance of this natural human behaviour is what led to this study. In this paper, the goal was to model both the self-control and the emotion regulation dynamics involved in the process of procrastination. This is done by means of a temporal-causal network, incorporating learning and control of the learning. We set out to unveil the dynamics of the system. Additionally, the effect of stress regulation-therapy on the process of procrastination was investigated. The model’s base level implementation was verified by making sure the aggregated impact matches the node values for certain stationary points and the model’s Hebbian learning behaviour was also mathematically shown to be correctly implemented. The results proved this model’s ability to model different types of individuals, all with different stress sensitivities. Therapy was also shown to be greatly beneficial. This temporal-causal network, however, can be improved, such as including self-compassion into the model as a link between procrastination and stress.
AB - Procrastination is an ever-growing problem in our current society. It was shown that 80-95% of college students are subject to it. The importance of this natural human behaviour is what led to this study. In this paper, the goal was to model both the self-control and the emotion regulation dynamics involved in the process of procrastination. This is done by means of a temporal-causal network, incorporating learning and control of the learning. We set out to unveil the dynamics of the system. Additionally, the effect of stress regulation-therapy on the process of procrastination was investigated. The model’s base level implementation was verified by making sure the aggregated impact matches the node values for certain stationary points and the model’s Hebbian learning behaviour was also mathematically shown to be correctly implemented. The results proved this model’s ability to model different types of individuals, all with different stress sensitivities. Therapy was also shown to be greatly beneficial. This temporal-causal network, however, can be improved, such as including self-compassion into the model as a link between procrastination and stress.
UR - https://m.youtube.com/watch?v=BpC-rwc9ft4
U2 - 10.1007/978-3-030-77961-0_44
DO - 10.1007/978-3-030-77961-0_44
M3 - Conference contribution
SN - 9783030779603
VL - 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 540
EP - 554
BT - Computational Science – ICCS 2021
A2 - Paszynski, Maciej
A2 - Kranzlmüller, Dieter
A2 - Krzhizhanovskaya, Valeria V.
A2 - Dongarra, Jack J.
A2 - Sloot, Peter M.A.
PB - Springer Nature Switzerland AG
T2 - 21st International Conference on Computational Science, ICCS 2021
Y2 - 16 June 2021 through 18 June 2021
ER -