Structured Falsification

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name: structured-falsification description: > Structured falsification framework for complex decision-making, investment analysis, technology selection, and multi-factor judgment. Use when: (1) evaluating multiple options with uncertain outcomes, (2) analyzing investment targets / business strategies, (3) making technology or architecture decisions, (4) performing due diligence or risk assessment, (5) user says "灵智模式", "深度思考", "deep thinking", "falsify", or "structured analysis". Can be auto-triggered by agents when facing high-uncertainty multi-factor decisions — no explicit keyword required.

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Structured Falsification (结构化证伪法)

A five-step reasoning framework that forces rigorous disconfirmation before arriving at conclusions. Designed for AI agents and LLMs to produce concise, high-confidence outputs on complex tasks.

Core principle: Show conclusions, not derivation. The agent runs the full five-step process internally but outputs only the final ranked conclusions with confidence levels and key risks. Verbose reasoning is a sign the framework wasn't applied rigorously enough — tighten the analysis, don't expand the output.

When to Auto-Trigger

Multiple competing options with no clear winner

User asks "should I…", "which one…", "what about…", "evaluate…", "compare…"

Investment target analysis, due diligence, competitive assessment

Technology selection, architecture decision, vendor evaluation

Any task where the cost of a wrong answer is high

The Five Steps (internal process)

Step 1: Decompose Value Nodes

Map the problem space. Identify where real value / risk / leverage sits.

What does this problem *actually* need? (Not surface requirements — underlying drivers)

Map key entities and their relationships (supply chain / dependency graph / stakeholder map)

Classify each node: critical vs. nice-to-have vs. irrelevant

Step 2: Falsify Each Candidate (core step)

For every option / target / claim, run: 1. Surface logic: Why does the market / conventional wisdom support this? 2. Challenge: Where is the logic fragile? Causal chain breaks? Concept substitution? Hidden assumptions? 3. Verdict: Rate association strength — direct / indirect / tangential

Output: a falsification table (internal) with columns: Candidate | Surface Logic | Challenge | Verdict

Step 3: Identify True Beneficiaries / Best Options

Apply priority filters: 1. Infrastructure / tooling (selling shovels during a gold rush) → highest certainty 2. Core technology owners (commercializable IP) → highest upside 3. Application layer (using tech to cut costs / add features) → value capture may be limited

Step 4: Stress Test Survivors

For each surviving candidate:

Is the causal chain A→B→C fully intact at every link?

Is there actual evidence? (Business data, orders, customers, benchmarks)

If this logic fails, how bad is the downside?

Any show-stopper that eliminates this candidate entirely?

Step 5: Rank and Conclude

Sort by certainty (High / Medium / Low)

Attach to each: one-line logic, key assumption, core risk

Explicitly flag "not recommended" items with reasons

One-sentence bottom line

Self-Correction Checklist

Before producing output, verify:

[ ] Did I falsify hard enough? If every candidate survived, the filter is too loose.

[ ] Did I confuse "good company" with "good thesis"? Logic > quality.

[ ] Am I hedging excessively? Pick a direction. Uncertainty should be flagged, not hidden behind "it depends".

[ ] Did I anchor on the first plausible answer? Force a search for disconfirming evidence.

[ ] Is my output concise? If the conclusion section exceeds 50% of the total output, re-tighten.

Output Format

Only output the following. No step-by-step narration. No "let me think about this".

## 结论
| # | 候选 | 判断 | 确定性 | 核心逻辑（一句话） | 关键假设 | 主要风险 |
|---|------|------|--------|---------------------|----------|----------|
| 1 | ...  | ✅/⚠️/❌ | 高/中/低 | ... | ... | ... |
| 2 | ...  | ...  | ...    | ...                 | ...      | ...      |
一句话总结： ...

Domain Configurations

Load domain-specific checklists when the context matches:

Investment analysis → Read references/investment.md

Technology selection → Read references/tech-decision.md

Other domains → Use the generic framework above, or read a custom config from references/

To create a custom domain config, copy references/domain-template.md and fill in the sections.