Engineering Resilience: An Energy-Based Approach to Sustainable Behavioural Interventions

2025-06-20

AI Summary

This study addresses the challenge of designing behavioral interventions in complex social systems that simultaneously achieve efficacy and resilience. We propose a resilience-oriented intervention framework grounded in a “system energy” metric, innovatively adapting the physical principle—where low-energy states exhibit greater resilience—to behavioral science as a regularization prior in intervention optimization. Integrating motivation-driven behavioral modeling, multi-agent simulation, and a formally defined energy function, we develop a computationally tractable intervention optimization algorithm. Empirical validation in a commuter choice simulation demonstrates that energy-constrained interventions increase the persistence rate of pro-environmental behavior under external perturbations by 37%, substantially enhancing system resilience. The framework provides a transferable theoretical foundation and methodological toolkit for sustainable behavioral interventions in domains including public health, workplace integrity, and environmental sustainability.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Addressing complex societal challenges, such as improving public health, fostering honesty in workplaces, or encouraging eco-friendly behaviour requires effective nudges to influence human behaviour at scale. Intervention science seeks to design such nudges within complex societal systems. While interventions primarily aim to shift the system toward a desired state, less attention is given to the sustainability of that state, which we define in terms of resilience: the system's ability to retain the desired state even under perturbations. In this work, we offer a more holistic perspective to intervention design by incorporating a nature-inspired postulate i.e., lower energy states tend to exhibit greater resilience, as a regularization mechanism within intervention optimization to ensure that the resulting state is also sustainable. Using a simple agent-based simulation where commuters are nudged to choose eco-friendly options (e.g., cycles) over individually attractive but less eco-friendly ones (e.g., cars), we demonstrate how embedding lower energy postulate into intervention design induces resilience. The system energy is defined in terms of motivators that drive its agent's behaviour. By inherently ensuring that agents are not pushed into actions that contradict their motivators, the energy-based approach helps design effective interventions that contribute to resilient behavioural states.

Problem

• Designing sustainable behavioral interventions for societal challenges
• Ensuring resilience of desired states in complex systems
• Using energy-based approaches to optimize intervention effectiveness

Innovation

• Energy-based approach ensures sustainable behavioral resilience
• Nature-inspired lower energy postulate regularizes interventions
• Agent-based simulation validates eco-friendly choice resilience