multi-model-convergence

name	multi-model-convergence
description	Wimsatt-style multi-method cross-validation — enumerate assumptions, generate alternative models, compare results, flag divergences.
execution	tactic
dependencies	{"sops":["alternative-model-generation","convergence-assessment","deep-insight-assumption-enumeration","deep-insight-paper-research","fragility-flagging"]}

Multi-Model Convergence

Test robustness by checking if conclusions survive across different modeling choices.

Operations

• assumption-enumeration → alternative-model-generation → convergence-assessment → fragility-flagging

Available SOPs

Subagent: assumption-enumeration, alternative-model-generation, convergence-assessment, fragility-flagging Import: paper-research

Execution Guidance

For each key assumption, generate at least one alternative model. Run all models, compare outputs. Results that converge are robust; results that diverge are fragile.

Minimum Yield

<HARD-GATE>
- assumptions enumerated: >= 5
- alternative models generated: >= 3
- convergence assessments: >= 1
- fragility flags: assessed
</HARD-GATE>

Available SOPs

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

SOP	When to use
alternative-model-generation	Generate alternative model formulations by relaxing, replacing, or generalizing specific assumptions.
convergence-assessment	Compare results across multiple model variants — quantitative agreement metrics and qualitative conclusion stability.
deep-insight-assumption-enumeration	Systematically identify all assumptions in a method/model — structural, parametric, distributional, and scope assumptions.
deep-insight-paper-research	Full-text paper reading via three-pass Keshav method. Import of literature-engine/literature-research skill. Authoritative source for claims about paper content.
fragility-flagging	Identify which specific assumption changes cause conclusion divergence. Rates fragility severity and plausibility of alternatives.