paper:doi-10-1016-j-jtbi-2018-07-002Free-energy minimization in joint agent-environment systems: A niche construction perspective
Original abstract (expand)
The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the 'fit' between an embodied agent and its niche, where the quantity of free-energy is a measure for the 'misfit' or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the 'desire paths' emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy.
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- ≈ 76%
- A Message Passing Realization of Expected Free Energy MinimizationMykola Lukashchuk, Thijs van de Laar, Bert de Vries Wouter W. L. Nuijten2026≈ 76%
- Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent StudyRafael Kaufmann, Justice Sefas and Thomas Kopinski Michael Walters2025≈ 75%
- Active Inference and Epistemic Value in Graphical ModelsMagnus Koudahl, Bart van Erp, Bert de Vries Thijs van de Laar2022≈ 75%
- EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active InferenceKobus Esterhuysen, Jacqueline B. Hynes, Axel Constant, Ines Hipolito, Mahault Albarracin, Alex B. Kiefer, Karl Friston John C. Boik2025≈ 75%
- ≈ 75%
- Free Energy and the Generalized Optimality Equations for Sequential Decision MakingPedro A. Ortega and Daniel A. Braun2012≈ 75%
- Expected Free Energy-based Planning as Variational InferenceWouter Nuijten, Thijs van de Laar, Wouter Kouw, Sepideh Adamiat, Tim Nisslbeck, Mykola Lukashchuk, Hoang Minh Huu Nguyen, Marco Hidalgo Araya, Raphael Tresor, Thijs Jenneskens, Ivana Nikoloska, Raaja Ganapathy Subramanian, Bart van Erp, Dmitry Bagaev and Albert Podusenko Bert de Vries2025≈ 75%
- The free energy principle made simpler but not too simpleLancelot Da Costa, Noor Sajid, Conor Heins, Kai Ueltzh\"offer, Grigorios A. Pavliotis and Thomas Parr Karl Friston2023≈ 75%
- Approximate information maximization for bandit gamesChristian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier (CELESTE) Alex Barbier-Chebbah2024≈ 74%
- A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language ModelsDjamel Bouchaffra2026≈ 74%
- Active Inference, Curiosity and Insightin corpus2017≈ 74%
- A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and ThermodynamicsFaycal Ykhlef, Mustapha Lebbah, Hanane Azzag Djamel Bouchaffra2026≈ 74%
- ≈ 74%
- Distributionally Robust Free Energy Principle for Decision-MakingHozefa Jesawada, Karl Friston, Giovanni Russo Allahkaram Shafiei2025≈ 74%
- The Free Energy Principle for Perception and Action: A Deep Learning PerspectiveTim Verbelen, Ozan \c{C}atal, Bart Dhoedt Pietro Mazzaglia2022≈ 74%
- ≈ 74%
- ≈ 73%
- Active inference: demystified and comparedin corpus2021≈ 71%
- ≈ 70%
- Collective intelligence: A unifying concept for integrating biology across scales and substratesin corpus2024≈ 70%
- ≈ 69%
- ≈ 69%
- Cognitive glues are shared models of relative scarcities: the economics of collective intelligencein corpus2026≈ 68%
- Active Inference: A Process Theoryin corpus2017≈ 68%
- Contemplative Agentin corpus2025≈ 67%
- Darwin's agential materials: evolutionary implications of multiscale competency in developmental biologyin corpus2023≈ 67%
- ≈ 67%
- ≈ 67%
- Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individualityin corpus2022≈ 66%
Similar preprints — Semantic Scholar
Cited by (1)
- Active inference on discrete state-spaces: a synthesis
Active inference on discrete state-spaces, formalized as partially observable Markov decision processes (POMDPs) with likelihood matrix A, transition matrix B, and prior D, unifies perception, plannin