Risk in the Risk in the Risk: Recursive Risk Discovery as a Polymath's Converted Function (The Advocatus Diaboli)
Abstract
This paper documents a specific cognitive profile and the recursive risk-decomposition method built on top of it, in my own account. The profile includes a measured extreme of visual imagery vividness (hyperphantasia, VVIQ 78/80, ~95th percentile) and a persistent, self-constructed spatial environment used for active reasoning rather than memorization. The method, "risk in the risk in the risk," extends the established premortem technique (Klein, 2007) past its normal single-pass stopping point, recursing failure analysis down to root causes before proposing a mitigation. I present two worked case studies (a generic-object teardown and a spacecraft-design exercise) demonstrating the method's transfer to an unfamiliar domain, and a professional track record (600+ built frameworks, concentrated in document red-teaming) as evidence the underlying operation is domain-general rather than subject-specific. I explicitly do not claim the vividness score explains or validates the method (it describes a striking input, not a mechanism), and I do not claim domain expertise in any field the method could be applied to; Section 6 argues instead that the documentand systems-red-teaming service already demonstrated elsewhere in the paper extends to any highstakes technical field on the strength of the reviewer's outside position, not new subject knowledge, and is testable through client-side verification, a different, and intuitively stronger, check than the self-caught corrections described elsewhere in the paper, not the same mechanism as them. The paper's central limitation, named directly rather than obscured, is that every claim currently rests on my own self-report, and Section 7 specifies what independent validation would need to look like before that changes.
Fields of Study & Subjects
AI-based Problem Solving and Planning
Human-Automation Interaction and Safety
Space exploration and regulation
Publication details
- Journal / source
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Preprint