- name
- structured-falsification
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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:
- Surface logic: Why does the market / conventional wisdom support this?
- Challenge: Where is the logic fragile? Causal chain breaks? Concept substitution? Hidden assumptions?
- 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:
- Infrastructure / tooling (selling shovels during a gold rush) → highest certainty
- Core technology owners (commercializable IP) → highest upside
- 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.