Special Issue "Applying the Free-Energy Principle to Complex Adaptive Systems"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 December 2021).

Special Issue Editors

Dr. Paul Badcock
E-Mail Website
Guest Editor
1. Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
2. Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
3. Orygen, Melbourne, Australia
Interests: theoretical psychology; human evolution and development; mood and affective disorders; youth mental health; active inference; evolutionary systems theory; complex adaptive systems
Dr. Maxwell Ramstead
E-Mail Website
Guest Editor
1. Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
2. Culture, Mind, and Brain Program, McGill University, Montreal, Canada
3. Wellcome Centre for Human Neuroimaging, University College London, London WC1N3BG, UK
Interests: active inference; philosophy of psychiatry; cognitive science; ecological cognition; hermeneutics
Dr. Zahra Sheikhbahaee
E-Mail Website
Guest Editor
Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada
Interests: physics; machine learning; reinforcement learning; astrophysics and cosmology; free-energy principle; active inference; computational neuroscience; statistics
M.A. M.Sc. Axel Constant
E-Mail
Assistant Guest Editor
1. Charles Perkins Centre, University of Sydney, Sydney, NSW University of Sydney, Sydney, Australia
2. Culture, Mind, and Brain Program, McGill University, Montréal, QC H3A 0G4, Canada
3. Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1E 6BT, UK
Interests: computational, evolutionary and cultural psychiatry; evolutionary computation; human social cognition

Special Issue Information

The free-energy principle is a formulation of the behavior of systems at non-equilibrium steady state that originally gained prominence as a unified model of the brain. Since then, the theory has been applied to a wide range of biological phenomena extending from single-celled organisms and flora, and human culture and niche construction, to the emergence of life and evolutionary dynamics. In a nutshell, the free-energy principle tells us that to exist is to gather evidence for our own existence; it suggests that living systems and their environments become statistical models of each other over time. For organisms, this means actively reducing the uncertainty or entropy of their Bayesian beliefs about what caused their sensory observations, either by changing the statistical models they embody, or by sampling the sensory observations expected under those models. For environments, this means accumulating traces of meaningful, goal-directed activity driven by their denizens. Under this view, living systems (and, implicitly, their environments), are fundamentally in the game of reducing sensory uncertainty and maximizing evidence for their own existence.

It is now widely recognized that the implications of the free-energy principle for our understanding of the human mind and behavior are far-reaching and profound. To date, however, its capacity to extend beyond the human brain, to explain living systems more generally, has only begun to be explored. This begs the question: How far does its explanatory scope extend? Can the free-energy principle be applied to any organism? Does it allow us to explain the dynamics of all living systems, including large-scale social behavior and its effects on local ecologies? Does the free-energy principle provide a formal, empirically tractable theory of any complex adaptive system, living or not?

The aim of this Special Issue is to showcase the breadth of the free-energy principle as a unified theory of biological systems—and to test its limits. Instead of concentrating on the human brain and behavior, we welcome contributions that apply the free-energy principle to other complex adaptive systems, particularly papers that focus on distributed cognitive systems (such as ant colonies, slime molds, and plants) or on self-organization across hierarchically nested spatiotemporal scales.

Dr. Paul Badcock
Dr. Maxwell Ramstead
Dr. Zahra Sheikhbahaee
M.A. M.Sc. Axel Constant
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex adaptive systems
  • entropy
  • information
  • free-energy principle
  • active inference
  • living systems

Published Papers (6 papers)

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Article
Equality and Freedom as Uncertainty in Groups
Entropy 2021, 23(11), 1384; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111384 - 22 Oct 2021
Viewed by 419
Abstract
In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is [...] Read more.
In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets. Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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Article
An Active Inference Model of Collective Intelligence
Entropy 2021, 23(7), 830; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070830 - 29 Jun 2021
Cited by 2 | Viewed by 942
Abstract
Collective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have [...] Read more.
Collective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between autonomous sub-system components (individuals) and global-scale behavior of the composite system (the collective). In this paper we use the Active Inference Formulation (AIF), a framework for explaining the behavior of any non-equilibrium steady state system at any scale, to posit a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence. We explore the effects of providing baseline AIF agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model 2), Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4). These stepwise transitions in sophistication of cognitive ability are motivated by the types of advancements plausibly required for an AIF agent to persist and flourish in an environment populated by other highly autonomous AIF agents, and have also recently been shown to map naturally to canonical steps in human cognitive ability. Illustrative results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents’ local and global optima. Alignment emerges endogenously from the dynamics of interacting AIF agents themselves, rather than being imposed exogenously by incentives to agents’ behaviors (contra existing computational models of collective intelligence) or top-down priors for collective behavior (contra existing multiscale simulations of AIF). These results shed light on the types of generic information-theoretic patterns conducive to collective intelligence in human and other complex adaptive systems. Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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Article
The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again
Entropy 2021, 23(6), 783; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060783 - 20 Jun 2021
Cited by 2 | Viewed by 2374
Abstract
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic [...] Read more.
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals). Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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Article
Cancer Niches and Their Kikuchi Free Energy
Entropy 2021, 23(5), 609; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050609 - 14 May 2021
Viewed by 1063
Abstract
Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation [...] Read more.
Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation and specialization are released from their autopoietic homeostasis. This induces the construction of cancer niches and maintains their survival. In this paper, we characterise cancer niche construction as a direct consequence of interactions between clusters of cancer and healthy cells. Explicitly, we evaluate these higher-order interactions between niches of cancer and healthy cells using Kikuchi approximations to the free energy. Kikuchi’s free energy is measured in terms of changes to the sum of energies of baseline clusters of cells (or nodes) minus the energies of overcounted cluster intersections (and interactions of interactions, etc.). We posit that these changes in energy node clusters correspond to a long-term reduction in the complexity of the system conducive to cancer niche survival. We validate this formulation through numerical simulations of apoptosis, local cancer growth, and metastasis, and highlight its implications for a computational understanding of the etiopathology of cancer. Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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Article
Message Passing and Metabolism
Entropy 2021, 23(5), 606; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050606 - 14 May 2021
Cited by 1 | Viewed by 675
Abstract
Active inference is an increasingly prominent paradigm in theoretical biology. It frames the dynamics of living systems as if they were solving an inference problem. This rests upon their flow towards some (non-equilibrium) steady state—or equivalently, their maximisation of the Bayesian model evidence [...] Read more.
Active inference is an increasingly prominent paradigm in theoretical biology. It frames the dynamics of living systems as if they were solving an inference problem. This rests upon their flow towards some (non-equilibrium) steady state—or equivalently, their maximisation of the Bayesian model evidence for an implicit probabilistic model. For many models, these self-evidencing dynamics manifest as messages passed among elements of a system. Such messages resemble synaptic communication at a neuronal network level but could also apply to other network structures. This paper attempts to apply the same formulation to biochemical networks. The chemical computation that occurs in regulation of metabolism relies upon sparse interactions between coupled reactions, where enzymes induce conditional dependencies between reactants. We will see that these reactions may be viewed as the movement of probability mass between alternative categorical states. When framed in this way, the master equations describing such systems can be reformulated in terms of their steady-state distribution. This distribution plays the role of a generative model, affording an inferential interpretation of the underlying biochemistry. Finally, we see that—in analogy with computational neurology and psychiatry—metabolic disorders may be characterized as false inference under aberrant prior beliefs. Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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Concept Paper
Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle
Entropy 2021, 23(12), 1701; https://0-doi-org.brum.beds.ac.uk/10.3390/e23121701 - 20 Dec 2021
Viewed by 931
Abstract
What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that [...] Read more.
What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of ‘disorder’ (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, ‘losing control’ appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away. Full article
(This article belongs to the Special Issue Applying the Free-Energy Principle to Complex Adaptive Systems)
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