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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Conditional Consequence Mapping and the 0.001% Problem: A Probabilistic Framework for Tail-Risk Prophecy Verification with Live Event Validation in the 2026 Iran War

Authors: Abeysekera, Prasanna;

Conditional Consequence Mapping and the 0.001% Problem: A Probabilistic Framework for Tail-Risk Prophecy Verification with Live Event Validation in the 2026 Iran War

Abstract

This paper introduces the Conditional Consequence Mapping Model (CCMM), a probabilistic framework for evaluating historically marginalised predictive texts against geopolitical outcomes. Applied to the quatrains of Michel de Nostredame (Nostradamus, 1503-1566), the framework departs fundamentally from conventional retrospective interpretation by establishing falsifiable conditional branches with pre-assigned probability weights, observable confirmation markers, and a novel Quatrain Convergence Score (QCS) derived from forensic analysis of the original 1555 Lyon edition French text. The model was constructed and documented prior to the commencement of hostilities between the United States, Israel, and Iran beginning 28 February 2026. Subsequent live event verification against three primary quatrains (Century II:62, Century III:61, and Century I:70) yielded an originally reported Day 6 QCS of 82.1% for the primary quatrain against confirmed events as of 6 March 2026; under the corrected six-term, 120-point denominator used in this revised edition, the recalculated Day 6 QCS is 83.3%. This revised edition updates verification through Day 28 (28 March 2026), with a revised QCS of 87.5% for Century II:62, reflecting additional confirming events across the defaite and vengeance dimensions. Century III:61 has advanced from 26% to 32% (pending activation), and Century I:70 has advanced from 55% to 65% (building, with partial activation of the Gaule and secret augure branches). A null model demonstrates that the QCS of 87.5% lies approximately 8.5 standard deviations above the mean score achieved by randomly selected quatrains scored against the same events. The paper argues that the intellectual contribution is not a claim of prophetic validity, but a methodological demonstration that rigorous probabilistic modelling applied to any dismissed or marginalised predictive corpus generates analytically useful intelligence about catastrophic event trajectories, even when the prior probability of realisation approaches 0.001%. Since the original SSRN submission, the CCMM framework has been applied to three additional operational domains: financial crime investigation (Volume 1 Technical Investigation Manual), homicide and serious crime investigation (Volume 2), and critical infrastructure cyber security (CCMM Cyber Framework, Stage 1 Architecture and Stage 2A Technical Specification). Cross-domain application supports the view that the analytical discipline of the framework, rather than any property of the Nostradamus corpus specifically, is the source of its utility.

Keywords

geopolitical risk, quatrain analysis, Bayesian inference, catastrophic event modelling, cross-domain application, tail-risk modelling, conditional probability, Iran, foresight methodology, evidence convergence scoring

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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