ATTENTION-WEIGHTED SYSTEMIC PROJECTION THEORY (AWSPT): A Medium-Independent Framework of Evidence Availability, Unequal Processing, and Whole-System Projection
Year: 2026
Type: preprint
Abstract: This blueprint proposes a medium-independent theoretical framework in which many or all relevant elements, data points, signals, records, events, outcomes, features, nodes, edges, time windows, or other evidence units within a specified observational scope may be available to an observer system while attention, processing depth, verification, relational integration, and computational resources remain unequally distributed. If evidence units receiving greater effective influence are subsequently used to represent the identity, principal structure, performance, or trajectory of the entire target system, an attention-weighted systemic projection may occur. The framework does not classify unequal attention as inherently incorrect. Instead, it distinguishes the availability and registration of evidence from the depth with which evidence is processed and from the influence that evidence ultimately exerts on a representation. Bias attribution is conditional on whether the resulting representation misaligns with a defensible, criterion-specific reference, whether underweighted but relevant evidence could materially change the conclusion, and whether a subset-dependent representation is inappropriately generalized to the whole system. The blueprint specifies a twenty-layer architecture, a central primary case, an evidence-unit-based default mechanism, a formal candidate model, operational variables, measurable quantities, testable hypotheses, alternative explanations, falsification criteria, and a research-development roadmap. The Attention-Weighted Systemic Projection Theory (AWSPT) is presented as a proposed, testable theoretical framework rather than an empirically established universal law.Keywords: attention weighting; systemic representation; unequal processing; whole-system attribution; observation; salience; representational projection; operationalization; falsifiability.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225788158
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