Whence the Expected Free Energy?

Millidge, Beren; Tschantz, Alexander; Buckley, Christopher L.

doi:10.1162/neco_a_01354

Cited by 58 publications

(61 citation statements)

References 67 publications

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“…For example, the congruence between survival through minimizing long-term average surprise and organizations minimizing expectancy discrepancy, expectancy violation, and expectancy disconfirmation [ 55 , 95 , 96 ]. Here, it is important to note that there is ongoing debate about the exact meaning of terms, such as EFE and VFE, and interrelationships between them [ 97 ] Accordingly, there is not fixed exact correspondence between technical terminology and practice examples [ 98 ]. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to practice in human organizations.…”

Section: Discussionmentioning

confidence: 99%

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Fox

2021

Entropy

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In this paper, the Adaptive Calibration Model (ACM) and Active Inference Theory (AIT) are related to future-proofing startups. ACM encompasses the allocation of energy by the stress response system to alternative options for action, depending upon individuals’ life histories and changing external contexts. More broadly, within AIT, it is posited that humans survive by taking action to align their internal generative models with sensory inputs from external states. The first contribution of the paper is to address the need for future-proofing methods for startups by providing eight stress management principles based on ACM and AIT. Future-proofing methods are needed because, typically, nine out of ten startups do not survive. A second contribution is to relate ACM and AIT to startup life cycle stages. The third contribution is to provide practical examples that show the broader relevance ACM and AIT to organizational practice. These contributions go beyond previous literature concerned with entrepreneurial stress and organizational stress. In particular, rather than focusing on particular stressors, this paper is focused on the recalibrating/updating of startups’ stress responsivity patterns in relation to changes in the internal state of the startup and/or changes in the external state. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to human organizations.

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Section: Discussionmentioning

confidence: 99%

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Fox

2021

Entropy

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“…Therefore, G can be regarded as a model parameter and is not represented as a random variable in the Forney factor graph. The definition and justification of the expected free energy are provided in appendix C and a recent paper by Millidge, Tschantz, and Buckley (2020). Also, the expected free energy arises naturally in mathematical treatments of the free energy principle when considering self-organization at nonequilibrium steady state (Friston, 2019;.…”

Section: 14mentioning

confidence: 99%

Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker

2021

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Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of brain function. It is also proposed as a framework for planning in AI. Unfortunately, the complex mathematics required to create new models can impede application of active inference in neuroscience and AI research. This letter addresses this problem by providing a complete mathematical treatment of the active inference framework in discrete time and state spaces and the derivation of the update equations for any new model. We leverage the theoretical connection between active inference and variational message passing as described by John Winn and Christopher M. Bishop in 2005. Since variational message passing is a well-defined methodology for deriving Bayesian belief update equations, this letter opens the door to advanced generative models for active inference. We show that using a fully factorized variational distribution simplifies the expected free energy, which furnishes priors over policies so that agents seek unambiguous states. Finally, we consider future extensions that support deep tree searches for sequential policy optimization based on structure learning and belief propagation.

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“…Now we apply the bound from Appendix A.3 to swap q for p in the denominator. Making use of this inequality is a standard move across the active inference literature [8,11,13,25,29].…”

Section: Computing G-expected Free Energymentioning

confidence: 99%

On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

Koudahl

Kouw

Vries

2021

Entropy

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Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free Energy (EFE) minimisation, a core feature of the framework, does not lead to purposeful explorative behaviour in linear Gaussian dynamical systems. We provide a simple proof that, due to the specific construction used for the EFE, the terms responsible for the exploratory (epistemic) drive become constant in the case of linear Gaussian systems. This renders AIF equivalent to KL control. From a theoretical point of view this is an interesting result since it is generally assumed that EFE minimisation will always introduce an exploratory drive in AIF agents. While the full EFE objective does not lead to exploration in linear Gaussian dynamical systems, the principles of its construction can still be used to design objectives that include an epistemic drive. We provide an in-depth analysis of the mechanics behind the epistemic drive of AIF agents and show how to design objectives for linear Gaussian dynamical systems that do include an epistemic drive. Concretely, we show that focusing solely on epistemics and dispensing with goal-directed terms leads to a form of maximum entropy exploration that is heavily dependent on the type of control signals driving the system. Additive controls do not permit such exploration. From a practical point of view this is an important result since linear Gaussian dynamical systems with additive controls are an extensively used model class, encompassing for instance Linear Quadratic Gaussian controllers. On the other hand, linear Gaussian dynamical systems driven by multiplicative controls such as switching transition matrices do permit an exploratory drive.

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Whence the Expected Free Energy?

Cited by 58 publications

References 67 publications

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker

On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

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