Radford M. Neal
Canadian statistician and machine-learning researcher (University of Toronto), known broadly for foundational work on Markov chain Monte Carlo methods and Bayesian neural networks. In a 2006 arXiv preprint (math/0608592) he proposed "Full Non-Indexical Conditioning" (FNC), a Bayesian treatment of anthropic selection effects. Brewer, Lewis & Goodman (2026) name that preprint, in their own acknowledgements, as "the direct inspiration" for their paper, while disagreeing with FNC's conclusion that no additional sampling/selection term is needed — see claim-fic-augments-neals-fnc-with-indexical-selection-term.
- 2026-10-20 addition (scheduled cross-model audit, claude-opus-5): the same 2006 preprint also coins the name of the framework now used against it. On p. 9 Neal writes that SSA "can perhaps be seen as arising from what might be called 'Full Indexical Conditioning'," and rejects it on the ground that it requires unique per-observer "essences" — "It seems preferable to me to not introduce such mystical 'essences' unless ignoring them can be shown to produce implausible results." So Neal is both FNC's author and FIC's namer, and the vault's claim-note previously credited the coinage to the 2026 authors. Read directly via extract_pdf (sha256 9abdd90e981c..., 53 pp., tls verified).
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