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11.3Visualisation of Belief Evolution
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThis section articulates the outward-facing components of BEWA—how its epistemic machinery is rendered accessible, interpretable, and actionable to end-users. The system is not designed to remain a closed inferential engine, but to serve as a transparent, intelligible platform for researchers, auditors, and knowledge institutions. Its interface architecture reflects a dual imperative: first, to expose the reasoning process with precision and granularity, enabling full auditability of belief formation and claim evolution; second, to present this complexity without compromising usability or inte…
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference10.3Probationary Periods for New Claims
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inferencehe truth utility function—
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference8.2Graph Structures and Belief Propagation To enable rigorous updating, evaluation, and querying of interconnected epistemic content, BEWA employs a formal belief graph over structured claims. This graph encodes both propositional assertions and their inferential, semantic, and evidentiary relationships, facilitating structured belief propagation via a generalised Bayesian network architecture augmented with non-monotonic belief revision rules.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference7Citation and Replication Framework
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference6.1Author Score Calculation
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThe architecture of BEWA’s credibility engine recognises that impact must be distinguished from popularity. Authors who generate high-citation work may still score poorly if that work fails to replicate or accumulates contradictions.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceAuthorial Credibility and Impact Modelling
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceBelief in a proposition, in the absence of continual evidential reinforcement, should gradually diminish to reflect epistemic uncertainty introduced by the passage of time.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference5.3Contradiction Handling and Counter-Evidence Processing
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceProposition 10 (Monotonicity of Posterior Updates): Let 𝑒 be a piece of evidence supporting 𝜙 with positive likelihood ratio: 𝑃 ( 𝑒 ∣ 𝜙 ) 𝑃 ( 𝑒 ∣ ¬ 𝜙 ) > 1 , then 𝑃 ( 𝜙 ∣ 𝑒 ) > 𝑃 ( 𝜙 ) . Proof. Follows directly from Bayes’ Theorem: 𝑃 ( 𝜙 ∣ 𝑒 ) = 𝑃 ( 𝑒 ∣ 𝜙 ) ⋅ 𝑃 ( 𝜙 ) 𝑃 ( 𝑒 ∣ 𝜙 ) ⋅ 𝑃 ( 𝜙 ) + 𝑃 ( 𝑒 ∣ ¬ 𝜙 ) ⋅ ( 1 − 𝑃 ( 𝜙 ) ) , and the assumption implies that the numerator grows faster than the denominator. ∎ Definition 17 (Cumulative Posterior Update): Given a sequence of 𝑛 evidence items 𝐸 = { 𝑒 1 , … , 𝑒 𝑛 } , define: 𝑃 ( 𝜙 ∣ 𝐸 ) = ∏ 𝑖 = 1 𝑛 ℒ ( 𝑒 𝑖 , 𝜙 ) ⋅ 𝜋 ( 𝑐 ) ∏…
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference5.2Evidence-Based Posterior Updating In accordance with the Bayesian framework adopted by BEWA, posterior beliefs over scientific claims are updated through the application of Bayes’ Theorem as new evidence is ingested. Evidence may take the form of citations, replications, contradictions, or derivations, each of which carries a quantifiable influence on the belief assigned to a structured propositional claim. Posterior updating must not only conform to the laws of probability, but also preserve inferential consistency, causal ordering, and network-level epistemic coherence.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceDefinition 14 (Authorial Trust Score 𝐴 ( 𝑐 ) ):
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference5.1Initial Prior Formulation
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference5Bayesian Weighting and Belief Updating
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference4.2Contextual Tagging and Domain Indexing
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference4Claim Representation and Propositional Structure
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference3.2Canonical Author and Claim Identification
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference3Data Ingestion and Canonical Normalisation
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceAccordingly, BEWA operationalises a utility function over claims that reflects not popularity or downstream use, but the claim’s contribution to the discovery, confirmation, or rectification of scientific truth.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference𝑉 ( 𝑐 ) is the verified downstream influence (e.g., in confirmed applications),
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference2.3Epistemic Integrity and Truth-Promoting Utility
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference2.2System-Level Design Principles
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceProposition 1 (Gradual Confirmation): Let { 𝐸 1 , 𝐸 2 , … , 𝐸 𝑛 } be an increasing sequence of independent pieces of evidence favouring 𝐻 . Then 𝑃 ( 𝐻 ∣ 𝐸 1 , … , 𝐸 𝑛 ) converges to 1 as 𝑛 → ∞ if 𝑃 ( 𝐸 𝑖 ∣ 𝐻 ) > 𝑃 ( 𝐸 𝑖 ∣ ¬ 𝐻 ) for all 𝑖 .
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference2.1Philosophical Basis: Bayesian Epistemology
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThe structure of the system rests on three critical pillars. First, the philosophical grounding in Bayesian epistemology ensures that all claims are embedded within a probabilistic inferential model, allowing beliefs to evolve incrementally and cautiously. Second, the architectural design is modular and hierarchical, integrating ingest pipelines, structured claim representation, belief propagation, and dynamic decay mechanisms, while ensuring coherence across each component. Third, the system embodies a core commitment to epistemic integrity: it prioritises replicability, resists premature bel…
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThe Bayesian Epistemology Weighting Architecture (BEWA)
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThe Problem of Scientific Epistemology in AI
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific InferenceThe proliferation of scientific literature and the accelerating complexity of epistemic discourse have outpaced the evalu
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference