A hybrid agent-based model integrated with a multi-stage learning-based fuzzy cognitive map

PurposeThis article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.Design/methodology/approachThe model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit f...

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Bibliographic Details
Published in:Kybernetes 2024-10, Vol.53 (10), p.3685-3706
Main Authors: Kocabey Ciftci, Pinar, Unutmaz Durmusoglu, Zeynep Didem
Format: Article
Language:English
Subjects:
Agent-based models
Behavior
Cognitive maps
Cognitive models
Farmers
Learning
Methods
Missing data
Simulation
Simulation models
Social networks
Tobacco
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Summary:PurposeThis article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.Design/methodology/approachThe model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.FindingsThe findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.Originality/valueThe proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.
ISSN:0368-492X
1758-7883
DOI:10.1108/K-01-2023-0104