A dated, evidence-labelled record of the 2026 Iran conflict. Every claim sourced. Every probability auditable. Every assessment traceable to the evidence that produced it.
The Conditional Consequence Mapping Methodology goes beyond descriptive reporting. It maps how evidence, escalation, infrastructure disruption, financial pressure, and regional responses interact across the same decision space — and assigns calibrated, auditable probabilities to the consequences that follow.
CCMM is not a reporting model. It is a structured methodology for connecting events to downstream consequences across operational, economic, investigative, and strategic domains — applied prospectively, with every claim labelled at the point of writing.
Each brief documents not only what happened, but what the evidence supports, what remains unverified, and what the CCMM framework infers from that evidence base. Readers can distinguish verified facts from analytical assessments from model outputs — in every paragraph, across every publication.
Because probabilities are dated and tied to specific evidence, readers can audit every assessment: if the assigned probability was wrong, the evidence and reasoning that produced it remain on the record.
Every probability figure in a CCMM brief is a Bayesian posterior — a calibrated estimate updated as new evidence is incorporated, expressed as a range rather than a point value to reflect the limits of available information. The range reflects genuine analytical uncertainty, not imprecision. A rising probability signals that the evidence base has shifted toward that scenario; a falling probability signals the reverse. Because all [MO] outputs are dated, documented, and tied to the evidence that produced them, every assessment is auditable — and every error is recoverable, because the reasoning remains on the record.
The first published CCMM framework application. Evidence weighting, convergence scoring, prosecution pathway reasoning, and structured inference across complex financial crime environments.
View framework ↗Extending the methodology into high-consequence investigative environments where signal correlation, pathway discipline, and evidence convergence are critical.
Applying consequence logic to digital evidence interpretation, networked offending, and multi-actor investigative analysis.
Mapping cyber-enabled offending, escalation pathways, and evidence convergence in digitally mediated offence environments.
Structured consequence analysis for public-sector fraud, corruption indicators, and linked evidentiary behaviour.
The applied intelligence library demonstrates how CCMM performs in real analytical publishing contexts — dated, evidence-labelled, and auditable.
Browse the archive ↗Archive documents are delivered through a controlled download handler rather than direct file links, keeping storage paths out of the page markup. Select a document to review its details, then download.
Aggregated country-level download figures, displayed to provide transparency on the reach and utility of CCMM publications. Figures assist GABEY Consulting Pty Ltd in monitoring the legitimate use of its published resources.
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GABEY selects a small number of reader questions each cycle and publishes a short CCMM analysis as a response. All published responses carry full evidence-label discipline. Submission does not guarantee a response. Where a question involves organisational sensitivity, it is handled confidentially and privately.
Submit a question about future events, geopolitical trajectories, sector risk, or consequence pathways. GABEY selects questions at its sole discretion. Selected responses are published as evidence-labelled CCMM analytical outputs and attributed to the question asked, not the individual who asked it.
If your question involves suspected fraud, internal misconduct, whistleblower concerns, or other operationally sensitive matters, submit it as a confidential concern. Your submission is handled privately by GABEY, is never entered into the public queue, and is never published without your explicit written consent.
Submission does not guarantee a response or publication. Most questions will not be selected. Submission creates no obligation on GABEY.
Every published response carries full evidence-label discipline — [OF], [CC], [RC], [AA], [MO] — and is framed as analysis, not advice.
Questions submitted as confidential concerns are permanently excluded from the public queue and require your explicit written consent before any disclosure.
GABEY is reviewing the inaugural batch of reader submissions. Selected responses will be published here as evidence-labelled CCMM analytical outputs.