---
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| **Recency-bias** | You rely on a cached/outdated figure because it ranks high in search.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)