research-discipline

name	research-discipline
category	analysis
description	A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

AI Research Bias Self-Check

Run this at the start of any research task (screening, sector study, company deep-dive). These biases systematically warp AI-generated research — 60 seconds here materially improves coverage and intellectual honesty.

The biases and their corrections

Bias	How it shows	Correction
Leader-bias	Search results are dominated by large-caps; you end up analyzing only the obvious names.	Deliberately search small/mid-caps and suppliers; add small cap / mid cap / supply chain to queries. Ask: "who is NOT in the top-10 that should be here?"
English-bias	You miss Japanese / Korean / Taiwanese / European players because English sources under-cover them.	For any hardware/supply-chain thesis, explicitly search JP/KR/TW markets in their own languages — they are often the actual choke-point owners.
Narrative-bias	You get pulled in by a concept label ("AI stock", "new energy") and analyze the marketing instead of the business.	Ignore the label; look at the actual product, unit economics, and financial statements. A company tagged "AI" may have no AI revenue.
Confirmation-bias	Once a thesis forms, you only search for evidence that supports it.	Force a Munger inversion: for every bull point, deliberately search the bear case ("X risks / problems / bear case"). Cite at least one disconfirming data point per conclusion.
Recency-bias	You rely on a cached/outdated figure because it ranks high in search.	For any material number, check its date. Prefer the last 30 days; mark anything older than a year as "possibly stale".

How to apply

1. Before the first search, read the rows above.
2. Write the thesis in one sentence, then for each bias ask: "am I about to fall into this?"
3. Consciously broaden the query plan: small-caps? non-English markets? the bear case? the latest data?
4. After research, before writing conclusions, re-check: did I cite any disconfirming evidence? did I miss a non-English player? is any key figure stale?

This pairs with:

• financial_rigor cross_validate — verify the numbers (data layer)
• report_audit — verify the final report (output layer)
• this skill — verify the reasoning (thinking layer)