Reframing the Expected Free Energy: Four Formulations and a Unification

Active inference is a process theory of perception, learning, and decision making that is applied to a range of research fields, including neuroscience, robotics, psychology, and machine learning. Active inference rests on an objective function called the expected free energy, which can be justified by the intuitive plausibility of its formulations-for example, the risk plus ambiguity and information gain/pragmatic value formulations. This letter seeks to formalize the problem of deriving these formulations from a single root expected free energy definition-the unification problem. Then we analyze two approaches to defining expected free energy. More precisely, the expected free energy is either defined as (1) the risk over observations plus ambiguity or (2) the risk over states plus ambiguity. In the first setting, no rigorous mathematical justification for the expected free energy has been proposed to date, but all the formulations can be recovered from it by assuming that the likelihood of target distribution T(o|s) is the likelihood of the generative model P(o|s). Importantly, under this likelihood constraint, if the likelihood is lossless,1 then prior preferences over observations can be defined arbitrarily. However, in the more general case of partially observable Markov decision processes (POMDPs), we demonstrate that the likelihood constraint effectively restricts the set of valid prior preferences over observations. Indeed, only a limited class of prior preferences over observations is compatible with the likelihood mapping of the generative model. In the second setting, a justification of the root expected free energy definition exists, but this setting only accounts for two formulations: the risk over states plus ambiguity and entropy plus expected energy formulations. We conclude with a discussion of the conditions under which a unification of expected free energy formulations has been proposed in the literature by appeal to the free energy principle in the specific context of systems without random fluctuations.

Authors

Institutions

Publication Details

Journal
Neural Computation
Published
2026-02-04
DOI
https://doi.org/10.1162/neco.a.1491
Primary Topic
Embodied and Extended Cognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reframing the Expected Free Energy: Four Formulations and a Unification

Howard Bowman, Théophile Champion, Dimitrije Marković, Marek Grzes
Neural Computation
Embodied and Extended Cognition
article

Reframing the Expected Free Energy: Four Formulations and a Unification

Howard Bowman, Théophile Champion, Dimitrije Marković, Marek Grzes
article en

Abstract

Active inference is a process theory of perception, learning, and decision making that is applied to a range of research fields, including neuroscience, robotics, psychology, and machine learning. Active inference rests on an objective function called the expected free energy, which can be justified by the intuitive plausibility of its formulations-for example, the risk plus ambiguity and information gain/pragmatic value formulations. This letter seeks to formalize the problem of deriving these formulations from a single root expected free energy definition-the unification problem. Then we analyze two approaches to defining expected free energy. More precisely, the expected free energy is either defined as (1) the risk over observations plus ambiguity or (2) the risk over states plus ambiguity. In the first setting, no rigorous mathematical justification for the expected free energy has been proposed to date, but all the formulations can be recovered from it by assuming that the likelihood of target distribution T(o|s) is the likelihood of the generative model P(o|s). Importantly, under this likelihood constraint, if the likelihood is lossless,1 then prior preferences over observations can be defined arbitrarily. However, in the more general case of partially observable Markov decision processes (POMDPs), we demonstrate that the likelihood constraint effectively restricts the set of valid prior preferences over observations. Indeed, only a limited class of prior preferences over observations is compatible with the likelihood mapping of the generative model. In the second setting, a justification of the root expected free energy definition exists, but this setting only accounts for two formulations: the risk over states plus ambiguity and entropy plus expected energy formulations. We conclude with a discussion of the conditions under which a unification of expected free energy formulations has been proposed in the literature by appeal to the free energy principle in the specific context of systems without random fluctuations.

Neural ComputationVol. 38(3)
Wellcome Centre for Human Neuroimaging (GB), University of Kent (GB), University College London (GB), Technische Universität Dresden (DE), University of Birmingham (GB)
Affordable and clean energy
Openalex Percentile: Top 67%
Embodied and Extended Cognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.