Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage

Emerson Bracy; Henrik Lassila; Jan Treur

doi:10.1007/978-3-031-53503-1_6

Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage

Emerson Bracy, Henrik Lassila, Jan Treur^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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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.

Original language	English
Title of host publication	Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications
Subtitle of host publication	COMPLEX NETWORKS 2023
Editors	Hocine Cherifi, Luis M. Rocha, Chantal Cherifi, Murat Donduran
Publisher	Springer
Chapter	Human Behavior
Pages	67–79
Number of pages	13
Volume	4
Edition	1
ISBN (Electronic)	978-3-031-53503-1
ISBN (Print)	978-3-031-53502-4
DOIs	https://doi.org/10.1007/978-3-031-53503-1_6
Publication status	Published - 29 Feb 2024
MoE publication type	A4 Conference publication
Event	International Conference on Complex Networks and their Applications - Menton, France Duration: 28 Nov 2023 → 30 Nov 2023 Conference number: 12 https://complexnetworks.org/

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	International Conference on Complex Networks and their Applications
Country/Territory	France
City	Menton
Period	28/11/2023 → 30/11/2023
Internet address	https://complexnetworks.org/

Keywords

adaptive network model
information overload
network model
network-oriented modeling

Access to Document

10.1007/978-3-031-53503-1_6

SCI_Bracy_etal_Complex_Networks_2023Accepted author manuscript, 1.37 MB

Cite this

Bracy, E., Lassila, H., & Treur, J. (2024). Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage. In H. Cherifi, L. M. Rocha, C. Cherifi, & M. Donduran (Eds.), Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 (1 ed., Vol. 4, pp. 67–79). (Studies in Computational Intelligence). Springer. https://doi.org/10.1007/978-3-031-53503-1_6

Bracy, Emerson ; Lassila, Henrik ; Treur, Jan. / Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage. Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023. editor / Hocine Cherifi ; Luis M. Rocha ; Chantal Cherifi ; Murat Donduran. Vol. 4 1. ed. Springer, 2024. pp. 67–79 (Studies in Computational Intelligence).

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title = "Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage",

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{\textquoteright}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 H Cherifi, LM Rocha, C Cherifi & M Donduran (eds), Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023. 1 edn, vol. 4, Studies in Computational Intelligence, Springer, pp. 67–79, International Conference on Complex Networks and their Applications, Menton, France, 28/11/2023. https://doi.org/10.1007/978-3-031-53503-1_6

Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage. / Bracy, Emerson; Lassila, Henrik; Treur, Jan.
Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023. ed. / Hocine Cherifi; Luis M. Rocha; Chantal Cherifi; Murat Donduran. Vol. 4 1. ed. Springer, 2024. p. 67–79 (Studies in Computational Intelligence).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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N2 - 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.

AB - 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. Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage. In Cherifi H, Rocha LM, Cherifi C, Donduran M, editors, Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023. 1 ed. Vol. 4. Springer. 2024. p. 67–79. (Studies in Computational Intelligence). doi: 10.1007/978-3-031-53503-1_6

Too Overloaded to Use : An Adaptive Network Model of Information Overload during Smartphone App Usage

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