Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models
Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance thro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/23/3768 |
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| author | Shengkun Xie Chong Gan Anna T. Lawniczak |
| author_facet | Shengkun Xie Chong Gan Anna T. Lawniczak |
| author_sort | Shengkun Xie |
| collection | DOAJ |
| description | Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation. |
| format | Article |
| id | doaj-art-6899856d2b2a44b1a4b457f9ad13f59d |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-6899856d2b2a44b1a4b457f9ad13f59d2025-08-20T01:55:37ZengMDPI AGMathematics2227-73902024-11-011223376810.3390/math12233768Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects ModelsShengkun Xie0Chong Gan1Anna T. Lawniczak2Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaGlobal Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaMathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, CanadaEnhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation.https://www.mdpi.com/2227-7390/12/23/3768generalized linear mixed-effects modelscellular automatoncognitive agentsagent-based simulationscomplex systemsvariable importance measures |
| spellingShingle | Shengkun Xie Chong Gan Anna T. Lawniczak Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models Mathematics generalized linear mixed-effects models cellular automaton cognitive agents agent-based simulations complex systems variable importance measures |
| title | Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models |
| title_full | Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models |
| title_fullStr | Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models |
| title_full_unstemmed | Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models |
| title_short | Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models |
| title_sort | analyzing decision making in cognitive agent simulations using generalized linear mixed effects models |
| topic | generalized linear mixed-effects models cellular automaton cognitive agents agent-based simulations complex systems variable importance measures |
| url | https://www.mdpi.com/2227-7390/12/23/3768 |
| work_keys_str_mv | AT shengkunxie analyzingdecisionmakingincognitiveagentsimulationsusinggeneralizedlinearmixedeffectsmodels AT chonggan analyzingdecisionmakingincognitiveagentsimulationsusinggeneralizedlinearmixedeffectsmodels AT annatlawniczak analyzingdecisionmakingincognitiveagentsimulationsusinggeneralizedlinearmixedeffectsmodels |