closest-worlds

name	closest-worlds
description	Strategy: Lewis Possible Worlds — find the minimal change to reality that would flip the conclusion, measuring how close the nearest world where the conclusion fails.
type	strategy
tactics	["minimal-change-search","systematic-factor-ablation"]
dependencies	{"tactics":["minimal-change-search","systematic-factor-ablation"],"sops":["causal-claim-extraction","counterfactual-scenario-construction","factor-enumeration","flip-point-detection","fragility-measurement","load-bearing-identification"]}

Closest Worlds Strategy

Lewis semantics: evaluate counterfactuals by finding the nearest possible world where the antecedent holds and checking whether the consequent follows.

Method

1. causal-claim-extraction identifies the conclusion and its supporting factors
2. factor-enumeration maps the space of possible changes
3. flip-point-detection searches for minimal changes that flip the conclusion
4. counterfactual-scenario-construction builds the nearest world where conclusion fails
5. fragility-measurement computes distance from actuality to flip-point
6. load-bearing-identification ranks factors by proximity to flip

Budget Table

Parameter	S	M	L
Change candidates explored	5	12	25
Flip-point searches	3	8	15
World-distance comparisons	3	6	12

Orchestration

causal-claim-extraction → factor-enumeration
→ [generate change candidates]:
    flip-point-detection (binary search for minimal flip)
    → counterfactual-scenario-construction (build nearest world)
    → fragility-measurement (compute distance)
→ load-bearing-identification (rank by proximity)

Subagents

• causal-claim-extraction (conclusion identification)
• factor-enumeration (change space mapping)
• flip-point-detection (minimal flip search)
• counterfactual-scenario-construction (world building)
• fragility-measurement (distance computation)
• load-bearing-identification (proximity ranking)

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
minimal-change-search	Tactic: Generate candidate changes, detect flip-points where conclusion reverses, measure fragility as distance to nearest flip.
systematic-factor-ablation	Tactic: List all factors, remove one at a time, assess conclusion stability, rank factors by load-bearing importance.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
causal-claim-extraction	Extract all causal claims (X causes Y, X leads to Y, X enables Y) from an artifact, producing a structured list of cause-effect pairs.
counterfactual-scenario-construction	Construct precise, internally consistent counterfactual scenarios where specified factors are altered, then reason about the resulting conclusion.
factor-enumeration	List all key factors, conditions, and assumptions that support or enable the artifact's conclusion.
flip-point-detection	Find the minimal change magnitude along a dimension that causes the conclusion to flip from true to false.
fragility-measurement	Compute a fragility index from flip-point distances and degradation scores, summarizing how robust the conclusion is.
load-bearing-identification	Identify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion.