Derivation of attention dynamics in a gaming environment reveals implications for systemic AI risk assessments.
Derives the inter-platform coupling tensor T_ij = C_i × C_j × S_ij from the void framework's three-dimensional scoring system. Proves that individual Pe scores (Layer 1) are insufficient to characterize systemic attention dynamics — the off-diagonal coupling coefficients S_ij, measuring how platform i's engagement dimension restructures demand toward platform j, require a controlled measurement substrate where desire-transition dynamics are fully observable. Demonstrates that the Fantasia game environment constitutes the only valid instrument for empirical S_ij derivation, as opacity (O=3) prevents external measurement of transition dynamics in real platforms. Shows that the eigenstructure of T predicts the natural attractor basin classification of the attention economy, generates falsifiable predictions against the framework's existing Qliphoth shell taxonomy, and establishes the mathematical foundation for systemic AI risk assessment under EU AI Act Annex III §5 and Annex IV.
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Anthony W. Eckert (2026) studied this question.
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