Modeling Sustainable Resource Management using Active Inference
This work addresses sustainable resource management for researchers in AI and sustainability, but it is incremental as it builds on prior work on active inference and well-being.
The authors tackled the problem of modeling sustainable resource management by developing a computational model using active inference, where an agent learns to balance immediate needs with long-term resource availability in static and dynamic environments, demonstrating adaptive and resilient behaviors.
Active inference helps us simulate adaptive behavior and decision-making in biological and artificial agents. Building on our previous work exploring the relationship between active inference, well-being, resilience, and sustainability, we present a computational model of an agent learning sustainable resource management strategies in both static and dynamic environments. The agent's behavior emerges from optimizing its own well-being, represented by prior preferences, subject to beliefs about environmental dynamics. In a static environment, the agent learns to consistently consume resources to satisfy its needs. In a dynamic environment where resources deplete and replenish based on the agent's actions, the agent adapts its behavior to balance immediate needs with long-term resource availability. This demonstrates how active inference can give rise to sustainable and resilient behaviors in the face of changing environmental conditions. We discuss the implications of our model, its limitations, and suggest future directions for integrating more complex agent-environment interactions. Our work highlights active inference's potential for understanding and shaping sustainable behaviors.