Abstract
In this paper, a first-order adaptive self-modeling network model is introduced to model information overload in the context of cyclical usage of smartphone apps. The model consists of interacting attention resources and emotional responses to both attention taxation and the app engagements. The model makes use of first-order reification to simulate the agent’s learning of the connections between app engagement and emotional responses, and strategic use of attention resources. Furthermore, external factors, such as context and influence of the environment to use the apps, are included to model the usage decision of the agent. Simulations in two scenarios illustrate that the model captures expected dynamics of the phenomenon.
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Bracy, E., Lassila, H., Treur, J. (2024). Too Overloaded to Use: An Adaptive Network Model of Information Overload During Smartphone App Usage. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_6
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DOI: https://doi.org/10.1007/978-3-031-53503-1_6
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