The decision-window worksheet for biography

# The decision-window worksheet for biography

Biographies can make a past decision look inevitable because the outcome is already known. This window reconstructs what was visible before the choice.

## Reopen the decision window

Save one page that makes the method useful, then complete the fields in your own words:

| Field | Prompt |
|---|---|
| **Options** | What choices appeared available at that moment? |
| **Constraints** | What money, law, duty, health, or timing limited them? |
| **Belief** | What did the person seem to believe then? |
| **Unknown** | Which later consequence could they not yet know? |

Keep the page number as the route back to the source. Avoid copying a protected passage when a short paraphrase will do.

## Resist hindsight

- Remove facts learned only after the decision.
- Distinguish the subject's stated reason from the biographer's explanation.
- Ask which option looked safest before the outcome was known.

Write one working sentence:

> My current reading is ____________________________________________.

## Review rule

After reading the consequence, return to the window and note what the result reveals without pretending it was predictable.

## Boundaries

This is a private reading-reflection aid, not a quotation archive, plot substitute, academic citation, factual verification, or proof of an author's intention. For consequential claims, return to the full passage and verify outside facts independently.

## Optional iPhone workflow

I am the developer of **Reading Journal: TabMap**, an iPhone and iPad companion for physical-book readers. It lets a reader save a page, a reason such as **Discuss** or **Question**, and an intensity, with an optional private note. The free version includes one complete book map; no account is required, and books, markers, labels, and optional notes stay on the device.

[View Reading Journal: TabMap on the App Store](https://apps.apple.com/us/app/reading-journal-tabmap/id6796663483)

Campaign marker: `day12-strong-distribution`