Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis
Households are crucial in the energy transition, accounting for over 25% of the European Union's energy consumption. To design effective policy measures that motivate households to change their behavior in favor of the energy transition, agent-based models (ABMs) are vital. For ABMs to reach th...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1568730/full |
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| author | Mariëlle Rietkerk-van der Wijngaart Lynn de Jager Lynn de Jager Geeske Scholz Emile Chappin Gerdien de Vries |
| author_facet | Mariëlle Rietkerk-van der Wijngaart Lynn de Jager Lynn de Jager Geeske Scholz Emile Chappin Gerdien de Vries |
| author_sort | Mariëlle Rietkerk-van der Wijngaart |
| collection | DOAJ |
| description | Households are crucial in the energy transition, accounting for over 25% of the European Union's energy consumption. To design effective policy measures that motivate households to change their behavior in favor of the energy transition, agent-based models (ABMs) are vital. For ABMs to reach their full potential in policy design, they must appropriately represent behavioral dynamics. One way to accomplish this is by strengthening the fit in ABMs between behavioral determinants (e.g., trust in energy companies) and the behavior of interest (e.g., adopting tariff structures). This study investigates whether a structured behavioral analysis improves this “determinants-behavior-fit.” A systematic review of 71 ABMs addressing household energy decisions reveals that models incorporating a behavioral analysis formalize nearly twice as many behavioral determinants, indicating a more systematic uptake. Subsequently, we find a difference between models focusing on investment-related behaviors (e.g., households buying solar panels) and those examining daily energy practices (e.g., households adjusting charging habits). Models in the first category integrate more social factors when incorporating behavioral analyses, corresponding with the influence of networks and peer effects on investment behaviors. Models in the second category emphasize individual and external factors in response to behavioral analyses, corresponding with the energy practices' habitual and contextual nature. Despite the benefits of a behavioral analysis for improving the determinants-behavior fit in ABMs, only one-third of the studies apply it partially. On top of that, almost half of the studies do not report a rationale for their choice of behavioral determinants. This suggests that many models may not fully capture the behavioral mechanisms underlying household energy decisions, limiting ABMs' potential to inform policymakers. Our findings highlight the need for systematic behavioral assessments in model development. We conclude that collaboration between behavioral scientists and modelers is crucial to accomplish such integration, and we emphasize the importance of allowing sufficient time and resources for meaningful exchange. Future research could further investigate empirical validation of behavioral insights in ABMs and explore how ABM results improve with a better determinants-behavior fit. By bridging behavioral science with computational modeling, ABMs' decision-support power to policymakers can be improved, ultimately accelerating the energy transition. |
| format | Article |
| id | doaj-art-2ebd2047c49b4df487833b5e3a5ed8a1 |
| institution | DOAJ |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-2ebd2047c49b4df487833b5e3a5ed8a12025-08-20T02:40:18ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-07-011610.3389/fpsyg.2025.15687301568730Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysisMariëlle Rietkerk-van der Wijngaart0Lynn de Jager1Lynn de Jager2Geeske Scholz3Emile Chappin4Gerdien de Vries5Faculty of Technology, Policy and Management, Delft University of Technology (TU Delft), Delft, NetherlandsFaculty of Technology, Policy and Management, Delft University of Technology (TU Delft), Delft, NetherlandsCentre for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsFaculty of Technology, Policy and Management, Delft University of Technology (TU Delft), Delft, NetherlandsFaculty of Technology, Policy and Management, Delft University of Technology (TU Delft), Delft, NetherlandsFaculty of Technology, Policy and Management, Delft University of Technology (TU Delft), Delft, NetherlandsHouseholds are crucial in the energy transition, accounting for over 25% of the European Union's energy consumption. To design effective policy measures that motivate households to change their behavior in favor of the energy transition, agent-based models (ABMs) are vital. For ABMs to reach their full potential in policy design, they must appropriately represent behavioral dynamics. One way to accomplish this is by strengthening the fit in ABMs between behavioral determinants (e.g., trust in energy companies) and the behavior of interest (e.g., adopting tariff structures). This study investigates whether a structured behavioral analysis improves this “determinants-behavior-fit.” A systematic review of 71 ABMs addressing household energy decisions reveals that models incorporating a behavioral analysis formalize nearly twice as many behavioral determinants, indicating a more systematic uptake. Subsequently, we find a difference between models focusing on investment-related behaviors (e.g., households buying solar panels) and those examining daily energy practices (e.g., households adjusting charging habits). Models in the first category integrate more social factors when incorporating behavioral analyses, corresponding with the influence of networks and peer effects on investment behaviors. Models in the second category emphasize individual and external factors in response to behavioral analyses, corresponding with the energy practices' habitual and contextual nature. Despite the benefits of a behavioral analysis for improving the determinants-behavior fit in ABMs, only one-third of the studies apply it partially. On top of that, almost half of the studies do not report a rationale for their choice of behavioral determinants. This suggests that many models may not fully capture the behavioral mechanisms underlying household energy decisions, limiting ABMs' potential to inform policymakers. Our findings highlight the need for systematic behavioral assessments in model development. We conclude that collaboration between behavioral scientists and modelers is crucial to accomplish such integration, and we emphasize the importance of allowing sufficient time and resources for meaningful exchange. Future research could further investigate empirical validation of behavioral insights in ABMs and explore how ABM results improve with a better determinants-behavior fit. By bridging behavioral science with computational modeling, ABMs' decision-support power to policymakers can be improved, ultimately accelerating the energy transition.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1568730/fullagent-based modelshousehold decisionenergy transitionbehavioral insightspolicy design |
| spellingShingle | Mariëlle Rietkerk-van der Wijngaart Lynn de Jager Lynn de Jager Geeske Scholz Emile Chappin Gerdien de Vries Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis Frontiers in Psychology agent-based models household decision energy transition behavioral insights policy design |
| title | Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis |
| title_full | Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis |
| title_fullStr | Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis |
| title_full_unstemmed | Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis |
| title_short | Bridging behavioral theory and household energy decisions: enhancing agent-based models with behavioral analysis |
| title_sort | bridging behavioral theory and household energy decisions enhancing agent based models with behavioral analysis |
| topic | agent-based models household decision energy transition behavioral insights policy design |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1568730/full |
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