fpf.tools
Library / First Principles Framework (FPF) - Core Conceptual Specification
Jump to passage
In this reading
Source · lines 46681–46694

Link to current text

Published source confirmed at last check

Source changed 2026-10-03 17:24:51 UTC · snapshot created 2026-10-03 17:30:20 UTC · last check 2026-10-03 18:00:10 UTC

B.5.2:4.4 - Plausibility filters

The filtering step is local and context-sensitive, but the criteria used SHALL be explicit. Typical filters include:

  • Parsimony. Does the candidate introduce only the additional structure that the prompt requires?
  • Explanatory reach. How much of the prompt does the candidate actually account for?
  • Consistency with established constraints. Does the candidate avoid collision with already trusted pillars, mechanisms, or scope declarations?
  • Falsifiability / probeability. What implication, deduction or possible observation could discriminate the candidate from its rivals? Keep that question separate from whether a check is obtainable and worth performing now.
  • Scope fit. Is the candidate framed for the declared prompt scope rather than for an inflated or shifted target?

No one filter is universally decisive. The pattern only requires that at least two filters be declared when a prime hypothesis is selected.

When a causal hypothesis cannot yet yield a discriminating implication because its mechanisms and rival accounts are unspecified, use C.28.CM to construct comparable causal models. Return their conditional consequences and unresolved premises to these plausibility filters. A useful hypothesis whose present question is already answered needs no additional model merely to remain a candidate.