Modeling Sustainable Resource Management using Active Inference

TL;DR

This paper presents a computational model demonstrating how active inference enables agents to learn sustainable resource management strategies, balancing immediate needs with long-term environmental stability in static and dynamic settings.

Contribution

It introduces a novel active inference-based model for sustainable resource management, highlighting adaptive behaviors in changing environments.

Findings

Agent learns to consume resources sustainably in static environments.

Agent adapts to resource depletion and replenishment in dynamic environments.

Model demonstrates potential for understanding sustainable behaviors.

Abstract

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.