Decision Journals: Track Your Thinking to Improve It

Most of us judge our decisions by how they turn out. The job offer we accepted that worked out was a 'good decision'; the investment that lost money was a 'bad decision'. This is the most common mistake in everyday thinking — it conflates decision quality with outcome quality, and the two are not the same thing. A decision journal — a written record of decisions and the reasoning behind them, captured before you know the outcome — is the single most effective tool for separating one from the other. By logging what you knew, what you assumed, and what you expected at the moment of choice, you build the only honest data you'll ever have about how well you actually think. This guide covers what to record, how to review entries, and why the people who try it rarely go back to deciding without one.

A decision journal is a written log of decisions you make, recorded at the moment of decision, capturing your reasoning, assumptions, expected outcomes, and confidence levels. The entries are short (five to ten minutes each) and structured. The goal isn't to write beautifully — it's to lock in your thinking before hindsight rewrites it.

The format you use matters less than the discipline of using one. A paper notebook, a spreadsheet, a Notion database, or a dedicated app all work. What matters is consistency: every meaningful decision (career moves, big purchases, investments, business choices, important relationships) gets an entry, and every entry is reviewed at a defined point in the future.

The technique is most associated with Annie Duke, whose book Thinking in Bets is the modern reference. The deeper roots run through the calibration-training research of Phil Tetlock and the Good Judgment Project, and through the financial-decision writing of Peter Bevelin and Charlie Munger.

Imagine watching a poker player call a big bet on a marginal hand. They get lucky and win the pot. Did they make a good decision? You can't tell from the outcome of one hand. Across ten thousand hands, the win rate of a strategy is data. Across one hand, it's noise.

Most life decisions are more like one poker hand than ten thousand. You don't accept the same job ten thousand times to find out whether the move was sound. You accept it once, the world's randomness picks an outcome from the distribution of things that could have happened, and that's the only data point you ever get. Judging the decision by that single outcome is exactly as misleading as judging a poker strategy by one hand.

Three forces conspire to make this worse:

• Small samples plus high variance. Outcomes are dominated by luck (risk and uncertainty alike) far more than people intuitively believe. The same decision in the same context can produce very different outcomes.
• Hindsight bias. Once you know the outcome, you remember being more certain of it than you were. Hindsight bias rewrites the memory of what you thought; the journal entry is the only thing it can't rewrite.
• The streetlight effect. Outcomes are visible; counterfactuals are not. You see what happened in the world that did unfold. You don't see what would have happened in the worlds that didn't.

Without a record taken before the outcome was known, you have no way to separate the contribution of your reasoning from the contribution of the world's randomness. The journal is the record.

Each entry has six fields. Most take a sentence; the whole entry should fit on one screen.

1. The decision. What you decided to do, and what alternatives you actively considered. The alternatives matter — a decision is meaningless without options to choose between.
2. Why you decided this way. The reasoning, evidence, and arguments. One paragraph. If you can't articulate why, that's information.
3. Your assumptions. The things you're treating as true that might not be. Which beliefs are load-bearing? If one of these turned out wrong, would you still make the same choice?
4. Your expected outcome and confidence. What you predict will happen, on what timeframe, and how confident you are — a number 0–100%. Be specific: 'I expect this role to last at least 18 months — 70% confident' beats 'I think this will probably work out'.
5. What would change your mind. What evidence or counterfactual would have led you to the other choice. If nothing would have, you weren't really deciding — you were rationalising.
6. When to review. Set a date now, before you know how it turned out. Without a review date, the journal is just a diary.

The confidence number is the most important field. Without it, every 'I sort of expected that' becomes 'I knew that all along'. With it, you can compare your stated confidence to base rates over time and see exactly where you're overconfident — the same calibration loop that superforecasters use to push their accuracy ahead of subject-matter experts.

Reviews are where the value lands. Without them, the journal is data collection without analysis.

• Short-horizon decisions (a week to a month): review at the natural outcome point.
• Medium-horizon decisions (3–12 months): set a quarterly review date.
• Long-horizon decisions (multi-year): set an annual review, then a final outcome review.

At each review, ask three questions:

1. What actually happened? Write the outcome alongside the original entry — never on top of it.
2. Was the outcome inside the range I predicted? If you said 70% confident in X and X happened, your prediction was correct. Across many such predictions, were you right roughly 70% of the time? That's calibration.
3. With the benefit of my actual reasoning record (not my memory), would I make the same decision today with the same information? This is the only legitimate way to judge the decision separately from the outcome.

You're judging the decision, not the outcome. A decision that produced a bad outcome can still have been a good decision; the question is whether the reasoning was sound given what you knew at the time. Positive-expected-value choices still lose half the time at 50% confidence — that's the maths, not the failure.

Take a concrete decision: should you accept a new role at £15k more salary, with a longer commute and less-interesting work?

Decision: Accept the new role. Alternatives considered: stay (counter-offer not requested), use the offer to renegotiate, decline both.

Reasoning: Net £15k pre-tax improves my savings rate from 18% to 26%. Less-interesting work is recoverable; the pay gap closes career-pivot regret faster than it accumulates. The new firm is in a more stable industry segment.

Assumptions: New role's stability matches old. Commute won't materially affect health or relationships. No recession in the next 12 months. Manager is a normal-distribution manager (didn't check references).

Expected outcome and confidence: 70% confident I'm still in the role at 18 months. 60% confident I'll consider the move worth it at 24 months.

What would change my mind: Counter-offer of £10k+ from old job. Specific bad signal on new manager from a trusted reference.

Review date: 18 months from start.

At the 18-month review you write the outcome alongside the original: still in the role, savings progress matched the prediction, but the commute was a bigger drag than predicted (only 50% retrospectively confident at the 18-month mark). Both predictions were broadly correct — calibration roughly right. With the same information today you'd make the same call, but you'd flag 'manager reputation' as a load-bearing assumption to actually verify next time. That last sentence — the one calibration entry — is the practice's whole compounding return.

The practice is simple to describe and easy to do badly. The patterns that ruin it:

• Recording only big decisions. The big ones are infrequent. You learn faster from a high volume of small-stakes decisions than from a handful of high-stakes ones — the calibration data is denser.
• Editing past entries. The entire value is the unedited record. If your future self disagrees with your past self, write the disagreement next to the original — never over it.
• Skipping the confidence number. Confidence numbers feel uncomfortable because they commit you. That discomfort is exactly the thing you're training away.
• Reviewing too early. If you said review-in-a-year, review in a year. Sneaking peeks at three months collapses the whole thing into rolling rationalisation.
• Treating the journal as a journal of feelings. It's a record of reasoning. Feelings can go in a separate diary; conflating the two corrupts both.
• Beating yourself up over bad outcomes. The whole point is decoupling outcome from decision quality. A bad outcome on a good decision is bad luck — that's a finding, not a failure.
• Logging without reviewing. A journal you never read is a write-only data store. The review cadence is the practice.

Don't over-engineer the tool. Three options that all work:

• Paper notebook. Lowest friction. Hard to sort or search later, but you'll never lose the discipline to a software change.
• Spreadsheet (Google Sheets, Excel). One row per decision, columns for the six fields plus 'review date' and 'outcome'. Sortable, searchable, lasts forever.
• Notion / dedicated apps (Decisive Journal, Decision Journal app, etc.). Templates and review reminders built in. Useful if reminders are what's stopping you from reviewing on schedule.

The medium is irrelevant if the discipline isn't there. Pick the one you'll actually use this week and stop optimising.

The journal is a calibration tool. It pairs naturally with:

• Pre-mortems — for decisions important enough to write a journal entry, also imagine the failure mode and write down what would cause it. The pre-mortem and the journal entry are the same artefact, viewed from two angles.
• Bayesian updating — when new evidence arrives between the decision and the review, the journal lets you ask 'how should this update my expected outcome?' rather than 'has my view drifted?'
• Second-order thinking — the 'what would change my mind' field forces second-order reasoning at the moment of decision, when it actually changes behaviour.

The journal alone doesn't make you a better decision-maker overnight. What it does is build a feedback loop between your reasoning and your reality, the absence of which is the reason most people don't improve at decisions over a lifetime.