A computational model for designing energy behaviour change interventions

Area of Science:

• Computational Social Science
• Behavioral Economics
• Energy Systems

Background:

• Conflicting literature on energy feedback's impact on behavior change necessitates a holistic approach.
• Energy consumption is influenced by a complex interplay of individual and societal factors.
• Existing models often fail to integrate diverse determinants of energy behavior.

Purpose of the Study:

• To develop an integrated agent-based computational model for energy consumption behavior change interventions.
• To create a flexible framework accommodating various models influencing consumption decisions.
• To enable in-silico evaluation of persuasive technologies before real-world deployment.

Main Methods:

• An agent-based computational model integrating personal values, energy literacy, and social practices.
• Incorporation of persuasive technology design strategies within the model framework.
• Validation using survey data and energy consumption data from a 2-year field study in Exeter, UK.

Main Results:

• The model demonstrates superior prediction of energy-saving behavior compared to random models.
• Preliminary results indicate accurate estimation of persuasive technology effects on behavior.
• The validated framework effectively connects behavioral determinants with intervention strategies.

Conclusions:

• The developed agent-based model offers a novel framework for understanding and influencing energy behavior change.
• The model's ability to predict behavior and evaluate interventions provides a valuable tool for policymakers and researchers.
• Integration into adaptive decision-making systems can enhance the efficacy of energy behavior change initiatives.