A Joint Research Program in Structural Evolutionary Theory and Applied Predictive Processing
Boris Kriger & David Espesset
Institute of Integrative and Interdisciplinary Research, Toronto, Canada
The Problem
Predictive processing — the framework proposing that brains are prediction engines, generating expectations and updating on mismatch — has become one of the most influential paradigms in neuroscience and cognitive science. Formalized by Karl Friston through the free energy principle, developed by Andy Clark, Jakob Hohwy, and others, and independently rediscovered in motor control, perception, and artificial intelligence, the framework offers a unified computational principle for perception, action, learning, and attention.
Yet predictive processing faces persistent criticism. It has been called unfalsifiable — capable of redescribing any behavior after the fact. It struggles with the dark room problem: if organisms minimize surprise, why don’t they seek the most predictable environment? Its evolutionary grounding remains shallow: the standard argument — that predictive architectures were selected because they conferred survival advantage — attributes creative power to natural selection, treating the architecture as a product of external filtering rather than explaining why it arises in the first place. This is precisely the explanatory error identified across multiple domains in Espesset, D. (2021), “Le darwinisme tient-il debout ? — Revue critique,” and developed systematically in Espesset, D. (2023), “Changement de paradigme de l’évolution biologique: du darwinisme à une nouvelle synthèse conceptuelle élargie,” Eikasía Revista de Filosofía: invoking cumulative natural selection as a creative force explains the persistence of a trait but not its origin. And between the mathematics of the free energy principle and actual biological systems lies an implementation gap that no existing program has bridged with formal proofs grounded in evolutionary and physical reasoning.
The Thesis
The Predictive Navigator advances a fundamentally different claim: predictive processing is not an adaptation selected for by cumulative natural selection. It is a structural inevitability — a necessary consequence of physical constraints on any information-managing system operating under temporal latency and environmental complexity.
This claim rests on two converging lines of argument.
Kriger’s formal results provide the mathematical framework. The Dual-Pressure Convergence Theorem, proven in Kriger, B. (2026), “The evolutionary inevitability of predictive processing: A physical constraint argument” (https://doi.org/10.5281/zenodo.18444910) and extended in Kriger, B. (2026), “Latency and Compressibility: Two Independent Routes to Predictive Architecture in Adaptive Systems” (https://doi.org/10.5281/zenodo.18752102), demonstrates that temporal latency and environmental complexity each independently force adaptive systems toward predictive architecture. The Predictive Viability Law, established in the same work, proves that viable systems in complex environments must maintain generative models. Adaptive Observer Theory, developed in Kriger, B. (2026), “Adaptive Observer Theory: Navigation of Incompleteness and Incompatibility” (https://doi.org/10.5281/zenodo.18812040), proves that viable agents must maintain multiple models and switch between them. Together these results demonstrate that predictive architecture follows from physics, not from selection.
Espesset’s evolutionary biology provides the biological grounding. His systematic critique of the neo-Darwinian paradigm — developed across Espesset, D. (2018), “L’évolution biologique: vers une nouvelle synthèse conceptuelle étendue pour le 21e siècle”; Espesset, D. (2021), “Le darwinisme tient-il debout ? — Revue critique”; and Espesset, D. (2025), “Schizophrénie darwiniste — Partie 1: Comment certaines dissensions et incohérences de la pensée scientifique biologique mènent à une véritable schizophrénie intellectuelle” — establishes that natural selection operates as a conserving and eliminating force, not a creative one. His framework for understanding biological organization through internal principles — self-organization, information management, autopoietic dynamics, and niche construction, as articulated in Espesset, D. (2025), “L’organisme vivant et la construction de l’individu biologique: Origine et évolution. De la perte de la notion d’organisme et l’unité de sélection darwinienne à une approche systémique et co-évolutive intégrée centrée sur l’auto-organisation, l’information biologique et la cognition,” and in Espesset, D. (2021), “Les maîtres de la construction de niche” — provides the biological substance for a non-selectionist account of cognitive architecture.
The convergence is precise. Where the standard PP narrative says “selection built prediction,” the Kriger-Espesset argument says: any system managing information under physical constraints is structurally compelled to become predictive; selection merely conserves what physics necessitates. This is not a minor reframing. It replaces a circular evolutionary argument with a derivation from first principles — and it does so at a level of formality that the PP community has never had.
Why Complexity Matters
The question of whether an organism requires predictive architecture cannot be answered without a quantitative account of biological complexity. Espesset’s long-standing program on complexity in evolutionary biology — beginning with Espesset, D. (2020), “The concept of complexity in evolutive biology: A synthetic review,” and its expanded French version Espesset, D. (2021), “La notion de complexité en biologie évolutive: une revue synthétique” — provides the conceptual foundation. This work identifies the multiple dimensions along which biological complexity must be measured (informational, structural, functional, evolutionary) and argues that no single metric suffices.
This analysis led directly to the development of a formal composite measure: Kriger, B. & Espesset, D. (2026), “The GEKS Index: A Composite Measure of Biological Complexity Across Informational, Structural, Functional, and Evolutionary Dimensions” (https://doi.org/10.5281/zenodo.18775491). The GEKS Index quantifies complexity in a way that connects to Kriger’s phase boundary between reactive and predictive regimes, enabling specific predictions about which taxa must exhibit predictive architecture and which can remain reactive.
The Contingency Question
A recurring question in evolutionary theory is whether complex features — including cognitive architecture — are contingent historical accidents or inevitable structural outcomes. Espesset, D. (2024), “Contingency versus inevitability: a review and reinterpretation of Stephen Jay Gould’s book ‘Wonderful Life — The Burgess Shale and the Nature of History’,” Trends in Genetics and Evolution, argues that Gould’s emphasis on contingency is overstated: structural and organizational constraints channel evolutionary outcomes far more narrowly than the contingency thesis allows. This position aligns precisely with the Predictive Navigator’s central claim — that predictive architecture is not a contingent evolutionary invention but a structurally inevitable consequence of physical constraints. Kriger’s formal proof, in Kriger, B. (2025), “The law of imperative uncertainty: Why any complex world requires uncertainty” (https://doi.org/10.5281/zenodo.18101601), establishes the complementary mathematical result: complexity itself generates the conditions under which predictive processing becomes necessary.
The Program: Seven Papers to a Monograph
Paper 1. The Structural Inevitability of Predictive Processing. The foundational theoretical paper. Integrates Kriger’s Dual-Pressure Convergence Theorem with Espesset’s critique of selectionist explanation. Demonstrates formally that predictive architecture emerges from physical constraints — latency, channel capacity, environmental complexity, punishment severity — not from cumulative selection. Natural selection plays a conserving role (eliminating non-viable phenotypes, as Espesset characterizes in his account of “purifying” and “stabilizing” selection) and a stabilizing role, but does not create the architecture itself. The paper establishes the phase boundary between reactive and predictive regimes and shows that any information-managing system whose parameters cross this boundary must become predictive or lose viability. Espesset’s argument that explaining biological diversity through natural selection invokes an external creativity that does not belong to organisms themselves — developed in Espesset, D. (2025), “Extended book review: How Life Increases Biodiversity — An autocatalytic hypothesis by David Seaborg. Reflections on the autocatalytic, self-referential, autonomous, autopoietic, self-organised dimensions of life” — provides the biological counterpart to Kriger’s formal demonstration.
Builds on: Kriger, B. (2026). The evolutionary inevitability of predictive processing: A physical constraint argument. https://doi.org/10.5281/zenodo.18444910 Kriger, B. (2026). Latency and Compressibility: Two Independent Routes to Predictive Architecture in Adaptive Systems. https://doi.org/10.5281/zenodo.18752102 Kriger, B. (2026). Evolutionary and information-theoretic argument for the necessity of representational isolation: Why direct perception was never an option for complex systems. https://doi.org/10.5281/zenodo.18331202 Espesset, D. (2023). Changement de paradigme de l’évolution biologique: du darwinisme à une nouvelle synthèse conceptuelle élargie. Eikasía Revista de Filosofía. Espesset, D. (2025). L’organisme vivant et la construction de l’individu biologique: Origine et évolution. Espesset, D. (2025). Extended book review: How Life Increases Biodiversity — An autocatalytic hypothesis by David Seaborg.
Paper 2. The Phase Boundary: Predicting Which Systems Must Be Predictive. The falsifiability paper. Defines the formal threshold at which systems transition from reactive to predictive architecture. Calculates parameter values (latency, complexity, error cost) for organisms across taxa, using the GEKS Index as a quantitative bridge. The conceptual foundation for measuring biological complexity across taxa draws on Espesset, D. (2020), “The concept of complexity in evolutive biology: A synthetic review,” and its formal operationalization in Kriger, B. & Espesset, D. (2026), “The GEKS Index: A Composite Measure of Biological Complexity Across Informational, Structural, Functional, and Evolutionary Dimensions” (https://doi.org/10.5281/zenodo.18775491). The paper generates specific, testable predictions: which taxa must exhibit predictive architecture and which can remain reactive. A system with the requisite parameters that lacks predictive architecture would falsify the framework — making this the first formally falsifiable formulation of the PP hypothesis.
Builds on: Kriger, B. & Espesset, D. (2026). The GEKS Index. https://doi.org/10.5281/zenodo.18775491 Kriger, B. (2024). The Transformational Basis of Persistence: A Formal Theory of Structural Viability. https://doi.org/10.5281/zenodo.18435982 Kriger, B. (2026). The Viability Mismatch Law: A Universal Principle for Viable Systems with Stress as Special Case. https://doi.org/10.5281/zenodo.18433777 Espesset, D. (2020). The concept of complexity in evolutive biology: A synthetic review. Espesset, D. (2021). La notion de complexité en biologie évolutive: une revue synthétique.
Paper 3. Against the Selectionist Theory of Cognition. The polemical paper. Argues that the standard evolutionary explanation for PP — “predictive brains were selected because they outcompeted reactive brains” — is explanatorily redundant. If predictive architecture is a structural consequence of physical constraints, then selection adds no explanatory power beyond conservation of viable forms. The paper engages directly with Godfrey-Smith’s and Sterelny’s evolutionary epistemology, with Pezzulo’s evolutionary PP framework, and — centrally — with Espesset’s multi-layered critique of creative selection: from the systematic analysis in Espesset, D. (2021), “Le darwinisme tient-il debout ? — Revue critique,” through the epistemological dissection in Espesset, D. (2025), “Schizophrénie darwiniste — Partie 1,” to the specific demonstration that lateral DNA transfer, genome rewriting, and autopoietic self-organization undermine the standard phylogenetic narrative in Espesset, D. (2018), “L’évolution biologique: vers une nouvelle synthèse conceptuelle étendue pour le 21e siècle.” The paper also draws on Espesset, D. (2018), “Critical scientific reflexions about the Red Queen hypothesis,” which demonstrates that even arms-race dynamics traditionally attributed to selection pressures can be reinterpreted through internal organizational principles. Kriger’s formal complement — proving that generative modeling is a resource-theoretic consequence of complexity rather than a selected trait — is established in Kriger, B. (2022), “Evolutionary Theory of Credence: A Conceptual Framework with Formal Analogies for Understanding Generative Modeling as a Resource-Theoretic Consequence of Complexity” (https://doi.org/10.5281/zenodo.18379476).
Builds on: Kriger, B. (2022). Evolutionary Theory of Credence. https://doi.org/10.5281/zenodo.18379476 Kriger, B. (2019). Evolutionary Selection for Atemporal Memory Storage: Why Three Convergent Pressures Favor Architectures Where Time Belongs to Retrieval, Not to Storage. https://doi.org/10.5281/zenodo.18381880 Kriger, B. (2025). The law of imperative uncertainty: Why any complex world requires uncertainty. https://doi.org/10.5281/zenodo.18101601 Espesset, D. (2021). Le darwinisme tient-il debout ? — Revue critique. Espesset, D. (2025). Schizophrénie darwiniste — Partie 1. Espesset, D. (2018). L’évolution biologique: vers une nouvelle synthèse conceptuelle étendue pour le 21e siècle. Espesset, D. (2018). Critical scientific reflexions about the Red Queen hypothesis. Espesset, D. (2025). Extended book review: How Life Increases Biodiversity — An autocatalytic hypothesis by David Seaborg.
Paper 4. The Dark Room Dissolved. Formal resolution of the dark room problem. Uses Kriger’s Viability Mismatch Law, proven in Kriger, B. (2026), “The Viability Mismatch Law: A Universal Principle for Viable Systems with Stress as Special Case” (https://doi.org/10.5281/zenodo.18433777), to demonstrate that the “dark room” strategy — minimizing prediction error by seeking maximally predictable environments — leads to viability loss. An agent that ceases to generate prediction error ceases to update its models; environmental drift then creates an expanding mismatch between model and reality until the agent crosses a viability threshold, a process formalized in Kriger, B. (2026), “The Eruptive Manifestation of Model–Reality Mismatch: A Unified Structural Framework for High-Activation Episodes in Bounded Adaptive Systems” (https://doi.org/10.5281/zenodo.18474532). Espesset’s framework contributes the biological argument: organisms that ceased exploratory behavior did not survive — not because selection “punished” them, but because viability requires ongoing information management that a static environment cannot sustain. This connects to the niche-construction perspective developed in Espesset, D. (2021), “Les maîtres de la construction de niche,” which shows that organisms actively shape their informational environments rather than passively adapting to them — a biological parallel to the active inference framework in PP.
Builds on: Kriger, B. (2026). The Viability Mismatch Law. https://doi.org/10.5281/zenodo.18433777 Kriger, B. (2026). The Structural Distortion Principle: A Closed-Loop Model of Perception, Attention, and World-Maintenance in Bounded Cognitive Systems. https://doi.org/10.5281/zenodo.18452700 Kriger, B. (2026). The Eruptive Manifestation of Model–Reality Mismatch. https://doi.org/10.5281/zenodo.18474532 Espesset, D. (2021). Les maîtres de la construction de niche. Espesset, D. (2025). L’organisme vivant et la construction de l’individu biologique.
Paper 5. Adaptive Observer Theory and the Necessity of Multiple Models. Proves that a predictive system operating in a complex environment cannot maintain viability with a single generative model. Kriger’s Adaptive Observer Theory, developed in Kriger, B. (2026), “Adaptive Observer Theory: Navigation of Incompleteness and Incompatibility” (https://doi.org/10.5281/zenodo.18812040), together with the supporting results in Kriger, B. (2026), “The Theorem of Incompatible Truths” (https://doi.org/10.5281/zenodo.18811305) and Kriger, B. (2026), “Must Any Consistent Physics Contain Structure Inaccessible to All Internal Observers?” (https://doi.org/10.5281/zenodo.18810467), demonstrates that viable agents must sustain multiple incompatible models and switch between them. This result connects to Espesset’s observation — articulated in Espesset, D. (2025), “L’organisme vivant et la construction de l’individu biologique” — that biological systems maintain redundant and sometimes contradictory regulatory mechanisms, and that the organism as a cognitive agent manages information at multiple levels simultaneously. The epistemological dimension — the limits of parsimony as a guide when biological systems are inherently non-parsimonious — is addressed in Espesset, D. (2019), “De l’utilisation parcimonieuse du principe de… parcimonie. Où Hercule Poirot, le célèbre détective belge, démontre involontairement qu’un raisonnement rigoureux n’est pas forcément parcimonieux.” The paper bridges to PP by showing that the “Bayesian brain” cannot be a single Bayesian reasoner: it must be a navigator managing a portfolio of models under incompleteness and incompatibility constraints.
Builds on: Kriger, B. (2026). Adaptive Observer Theory: Navigation of Incompleteness and Incompatibility. https://doi.org/10.5281/zenodo.18812040 Kriger, B. (2026). Must Any Consistent Physics Contain Structure Inaccessible to All Internal Observers? https://doi.org/10.5281/zenodo.18810467 Kriger, B. (2026). The Theorem of Incompatible Truths. https://doi.org/10.5281/zenodo.18811305 Kriger, B. (2026). The Pragmatic Synthesis: How Complex Systems Navigate Incompatible Truths through Adaptive Heuristics. https://doi.org/10.5281/zenodo.18812001 Espesset, D. (2025). L’organisme vivant et la construction de l’individu biologique. Espesset, D. (2019). De l’utilisation parcimonieuse du principe de… parcimonie.
Paper 6. Coherence, Complexity, and the Predictive Threshold Across Taxa. The comparative paper. Applies the GEKS Index, developed in Kriger, B. & Espesset, D. (2026), “The GEKS Index: A Composite Measure of Biological Complexity Across Informational, Structural, Functional, and Evolutionary Dimensions” (https://doi.org/10.5281/zenodo.18775491), to map biological complexity across taxa onto the phase boundary defined in Paper 2. For each major clade, estimates whether physical constraints (neural latency, sensory channel capacity, environmental volatility) place the organism in the reactive or predictive regime. The question of whether complex outcomes are structurally inevitable or historically contingent — analyzed in Espesset, D. (2024), “Contingency versus inevitability: a review and reinterpretation of Stephen Jay Gould’s book ‘Wonderful Life — The Burgess Shale and the Nature of History’,” Trends in Genetics and Evolution — is here given a quantitative answer: for organisms above the phase boundary, predictive architecture is inevitable regardless of phylogenetic path. The paper draws on Espesset’s analysis of genome-level complexity, including the implications of lateral DNA transfer and genome rewriting for understanding the informational substrate on which predictive architecture operates, as discussed in Espesset, D. (2022), “Autour du livre L’entropie génétique et le mystère du génome.” Cross-references with comparative neurobiology literature on anticipatory behavior, forward models in motor control, and predictive sensory processing. The coherence framework from Kriger, B. (2026), “Coherence Epistemology for AI-Mediated Inter-Species Communication: A Black-Box Framework” (https://doi.org/10.5281/zenodo.18778705) provides the formal measure for assessing predictive capacity across species with incommensurable sensory systems.
Builds on: Kriger, B. & Espesset, D. (2026). The GEKS Index. https://doi.org/10.5281/zenodo.18775491 Kriger, B. (2026). Coherence Epistemology for AI-Mediated Inter-Species Communication. https://doi.org/10.5281/zenodo.18778705 Kriger, B. (2026). The principle of optimal coherence. https://doi.org/10.5281/zenodo.18341030 Kriger, B. (2024). No final theory: Law of scale-specific principles. https://doi.org/10.5281/zenodo.18099738 Espesset, D. (2024). Contingency versus inevitability: a review and reinterpretation of Stephen Jay Gould’s book “Wonderful Life.” Trends in Genetics and Evolution. Espesset, D. (2022). Autour du livre L’entropie génétique et le mystère du génome. Espesset, D. (2020). The concept of complexity in evolutive biology: A synthetic review.
Paper 7. The Predictive Navigator: Synthesis. The capstone monograph chapter. Integrates all results into a unified framework: predictive processing as a structurally inevitable consequence of physical constraints on information-managing systems, with natural selection serving a conserving rather than creative role. Addresses all five major PP criticisms — unfalsifiability, the dark room problem, the evolutionary gap, the implementation gap, and the cross-species gap — with formal proofs and evolutionary argument. Proposes the Navigator as a new theoretical entity: not a Bayesian brain, but a viability-maintaining system that manages multiple generative models under physical constraints, incompleteness, and incompatibility. The epistemological and methodological foundations are secured by Kriger, B. (2026), “Against Causation: A Formal Argument That Causality Is a Compression Artifact of Bounded Observers, Not a Feature of Reality” (https://doi.org/10.5281/zenodo.18851848); Kriger, B. (2026), “Why Mathematics Works: Structural Necessity of Isomorphism Between Formal Systems and Physical Reality” (https://doi.org/10.5281/zenodo.18793228); and Kriger, B. (2026), “The Predictive Mind and Its Myths: Metaphor, Narrative, and Ritual as Structural Necessities of Scientific Cognition” (https://doi.org/10.5281/zenodo.18490146). Espesset’s overarching synthesis — Espesset, D. (2023), “Changement de paradigme de l’évolution biologique: du darwinisme à une nouvelle synthèse conceptuelle élargie,” Eikasía Revista de Filosofía — provides the evolutionary counterpart: a post-Darwinian framework within which the Predictive Navigator is not merely compatible with evolutionary theory but is a central structural prediction of it.
Builds on: All preceding papers, plus: Kriger, B. (2026). The Predictive Mind and Its Myths. https://doi.org/10.5281/zenodo.18490146 Kriger, B. (2026). Against Causation. https://doi.org/10.5281/zenodo.18851848 Kriger, B. (2026). Why Mathematics Works. https://doi.org/10.5281/zenodo.18793228 Kriger, B. (2017). A Unified Theory of Self-Organizing Systems: Four Formal Laws on Cooperation, Viability, Interference, and Observability. https://doi.org/10.5281/zenodo.18363729 Espesset, D. (2023). Changement de paradigme de l’évolution biologique: du darwinisme à une nouvelle synthèse conceptuelle élargie. Eikasía Revista de Filosofía.
The seven papers combine into a co-authored monograph: The Predictive Navigator: From Physical Constraint to Evolutionary Proof.
Publication Plan with Target Journals
Phase I — Foundations
Paper 1. The Structural Inevitability of Predictive Processing. First choice: Neuroscience & Biobehavioral Reviews — high-impact review/theory journal, strong readership in computational and theoretical neuroscience, regularly publishes PP-related framework papers. Second choice: Physics of Life Reviews — interdisciplinary, welcomes formal/physical arguments about biological systems, includes open peer commentary format that would amplify the paper’s reach.
Paper 3. Against the Selectionist Theory of Cognition. First choice: Biology & Philosophy — the premier journal at the intersection of evolutionary theory and philosophy of biology, ideal venue for a rigorous challenge to selectionist explanation of cognitive architecture. Second choice: Behavioral and Brain Sciences — target article format with open peer commentary would generate maximum engagement from both the PP and evolutionary theory communities.
Phase II — Formal Consequences
Paper 2. The Phase Boundary: Predicting Which Systems Must Be Predictive. First choice: Journal of Theoretical Biology — publishes formal biological models with quantitative predictions, ideal for a paper that defines a measurable threshold with taxa-specific calculations. Second choice: Biosystems — interdisciplinary focus on information processing and organization in biological systems, strong fit for the GEKS-based cross-taxa predictions.
Paper 4. The Dark Room Dissolved. First choice: Philosophical Transactions of the Royal Society B — regularly publishes theme issues on PP and active inference, high visibility in both neuroscience and philosophy of mind. Second choice: Adaptive Behavior — focused on the interface between formal models and biological behavior, ideal for a viability-theoretic resolution of a behavioral paradox.
Paper 5. Adaptive Observer Theory and the Necessity of Multiple Models. First choice: Entropy (MDPI) — open-access, strong track record in information-theoretic approaches to cognition and biological systems, appropriate for formal proofs about model multiplicity. Second choice: Synthese — philosophy of science journal with strong computational and formal theory coverage, appropriate for the epistemological implications of model incompatibility.
Phase III — Comparative Evidence and Synthesis
Paper 6. Coherence, Complexity, and the Predictive Threshold Across Taxa. First choice: Proceedings of the Royal Society B — premier venue for comparative biology with quantitative framework, ideal for a falsifiable cross-taxa map of predictive architecture. Second choice: Journal of Evolutionary Biology — strong fit for a paper connecting complexity measures to evolutionary outcomes across clades.
Paper 7. The Predictive Navigator: Synthesis. Published as a co-authored monograph: The Predictive Navigator: From Physical Constraint to Evolutionary Proof. First choice: MIT Press — publishes the leading monographs in PP (Clark, Hohwy, Friston), natural home for a synthesis that reframes the field’s evolutionary foundations. Second choice: Oxford University Press — strong list in philosophy of biology and cognitive science, appropriate for a cross-disciplinary synthesis.
Existing Publications Supporting the Program
Kriger — Predictive Processing & Dual-Pressure Framework: Kriger, B. (2026). The evolutionary inevitability of predictive processing: A physical constraint argument. https://doi.org/10.5281/zenodo.18444910 Kriger, B. (2026). Latency and Compressibility: Two Independent Routes to Predictive Architecture in Adaptive Systems. https://doi.org/10.5281/zenodo.18752102 Kriger, B. (2026). Evolutionary and information-theoretic argument for the necessity of representational isolation. https://doi.org/10.5281/zenodo.18331202 Kriger, B. (2026). The Structural Distortion Principle. https://doi.org/10.5281/zenodo.18452700 Kriger, B. (2026). The Predictive Mind and Its Myths. https://doi.org/10.5281/zenodo.18490146
Kriger — Adaptive Observer Theory & Navigator: Kriger, B. (2026). Adaptive Observer Theory: Navigation of Incompleteness and Incompatibility. https://doi.org/10.5281/zenodo.18812040 Kriger, B. (2026). Must Any Consistent Physics Contain Structure Inaccessible to All Internal Observers? https://doi.org/10.5281/zenodo.18810467 Kriger, B. (2026). The Theorem of Incompatible Truths. https://doi.org/10.5281/zenodo.18811305 Kriger, B. (2026). The Pragmatic Synthesis. https://doi.org/10.5281/zenodo.18812001
Kriger — Coherence Epistemology & Complexity: Kriger, B. (2026). Coherence Epistemology for AI-Mediated Inter-Species Communication. https://doi.org/10.5281/zenodo.18778705 Kriger, B. (2026). The principle of optimal coherence. https://doi.org/10.5281/zenodo.18341030
Kriger — Viability, Dynamical Systems & Behavioral Dysregulation: Kriger, B. (2026). The Viability Mismatch Law. https://doi.org/10.5281/zenodo.18433777 Kriger, B. (2026). The Eruptive Manifestation of Model–Reality Mismatch. https://doi.org/10.5281/zenodo.18474532 Kriger, B. (2024). The Transformational Basis of Persistence. https://doi.org/10.5281/zenodo.18435982
Kriger — Evolutionary Dynamics: Kriger, B. (2022). Evolutionary Theory of Credence. https://doi.org/10.5281/zenodo.18379476 Kriger, B. (2019). Evolutionary Selection for Atemporal Memory Storage. https://doi.org/10.5281/zenodo.18381880 Kriger, B. (2025). The law of imperative uncertainty. https://doi.org/10.5281/zenodo.18101601 Kriger, B. (2024). No final theory: Law of scale-specific principles. https://doi.org/10.5281/zenodo.18099738
Kriger — Methodology & Epistemology: Kriger, B. (2026). Against Causation. https://doi.org/10.5281/zenodo.18851848 Kriger, B. (2026). Why Mathematics Works. https://doi.org/10.5281/zenodo.18793228 Kriger, B. (2017). A Unified Theory of Self-Organizing Systems. https://doi.org/10.5281/zenodo.18363729
Kriger & Espesset: Kriger, B. & Espesset, D. (2026). The GEKS Index: A Composite Measure of Biological Complexity Across Informational, Structural, Functional, and Evolutionary Dimensions. https://doi.org/10.5281/zenodo.18775491
Espesset — Evolutionary Theory & Paradigm Critique: Espesset, D. (2025). L’organisme vivant et la construction de l’individu biologique: Origine et évolution. De la perte de la notion d’organisme et l’unité de sélection darwinienne à une approche systémique et co-évolutive intégrée centrée sur l’auto-organisation, l’information biologique et la cognition. Espesset, D. (2025). Schizophrénie darwiniste — Partie 1: Comment certaines dissensions et incohérences de la pensée scientifique biologique mènent à une véritable schizophrénie intellectuelle. Espesset, D. (2025). Extended book review: How Life Increases Biodiversity — An autocatalytic hypothesis by David Seaborg. Reflections on the autocatalytic, self-referential, autonomous, autopoietic, self-organised dimensions of life. Espesset, D. (2024). Contingency versus inevitability: a review and reinterpretation of Stephen Jay Gould’s book “Wonderful Life — The Burgess Shale and the Nature of History.” Trends in Genetics and Evolution. Espesset, D. (2023). Changement de paradigme de l’évolution biologique: du darwinisme à une nouvelle synthèse conceptuelle élargie. Eikasía Revista de Filosofía. Espesset, D. (2022). Autour du livre L’entropie génétique et le mystère du génome. Espesset, D. (2021). Le darwinisme tient-il debout ? — Revue critique. Espesset, D. (2021). La notion de complexité en biologie évolutive: une revue synthétique. Espesset, D. (2021). Les maîtres de la construction de niche. Espesset, D. (2020). The concept of complexity in evolutive biology: A synthetic review. Espesset, D. (2019). De l’utilisation parcimonieuse du principe de… parcimonie. Où Hercule Poirot, le célèbre détective belge, démontre involontairement qu’un raisonnement rigoureux n’est pas forcément parcimonieux. Espesset, D. (2018). L’évolution biologique: vers une nouvelle synthèse conceptuelle étendue pour le 21e siècle. Espesset, D. (2018). Critical scientific reflexions about the Red Queen hypothesis.
Espesset — Molecular Biology: Espesset, D., Duché, D., Baty, D. & Géli, V. (1996). The channel domain of colicin A is inhibited by its immunity protein through direct interaction in the Escherichia coli inner membrane. The EMBO Journal. Espesset, D., Corda, Y., Cunningham, K. & Géli, V. (1994). The colicin A pore-forming domain fused to mitochondrial intermembrane space sorting signals can be functionally inserted into the Escherichia coli plasma membrane by a mechanism that bypasses the Tol proteins. Molecular Microbiology. Espesset, D., Piet, P., Lazdunski, C. & Géli, V. (1994). Immunity proteins to pore-forming colicins: Structure-function relationships. Molecular Microbiology.
Engagement with the PP Community
The program builds on existing scholarly relationships with researchers at the center of the predictive processing field, including Laurent Perrinet (Aix-Marseille / CNRS), whose work on heterogeneous delays in spiking neural networks and oculomotor active inference connects directly to Paper 1; Giovanni Pezzulo, whose evolutionary PP framework provides the context for Paper 3; and Pedro Mediano (Imperial College London), whose work on phase transitions in integration measures connects to Papers 2 and 6. We anticipate targeted contributions from these and other specialists on individual papers, and welcome inquiries from researchers interested in the program.
What Makes This Program Unique
The combination of a formally rigorous PP-adjacent theoretical framework with proven theorems and simulation code; a principled evolutionary critique grounded in two decades of published work on biological complexity, self-organization, and the limits of selectionist explanation — from molecular biology through evolutionary theory; a quantitative complexity index (GEKS) already developed and published as a joint tool for cross-taxa prediction; and established relationships with the PP community’s key researchers — this combination exists nowhere else.
The predictive processing community has the theory. We have the proof that it could not have been otherwise.
Contact
Boris Kriger | ORCID: 0009-0001-0034-2903 | boriskriger@interdisciplinary-institute.org
David Espesset | ORCID: 0000-0002-7643-4687
Institute of Integrative and Interdisciplinary Research 