# Modeling Sustainable Resource Management using Active Inference

arXiv: https://arxiv.org/abs/2406.07593 · HTML: https://mlsanity.com/paper/2406.07593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [The free energy principle made simpler but not too simple](https://mlsanity.com/paper/2201.06387/) (2022)
- [Active Inference as a Model of Agency](https://mlsanity.com/paper/2401.12917/) (2024)
- [Is the free-energy principle a formal theory of semantics? From variational density dynamics to neural and phenotypic representations](https://mlsanity.com/paper/2007.09291/) (2020)

## Built on by (most cited first, 1 of 1)

- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)

## Abstract (arXiv)

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.