  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.
