Set default rules for willpower-intense situations
A pre-decided rule requires no willpower at the decision point — the decision has already been made.
Why it works
Heuristics reduce cognitive load by converting a repeated decision into a rule. When a rule is established in advance, the moment of temptation does not require deliberation — the rule fires automatically. This is how experienced practitioners in any domain reduce the cost of discipline: they’ve made the decision so many times that it’s no longer a decision, it’s a procedure.
How to do it
- Identify the three situations where you most often make decisions you regret.
- Write a specific rule for each: "If [situation], then I [specific action], no exceptions this week."
- Review the rules in advance of high-risk situations, not in the middle of them.
Evidence
Implementation intentions research shows that if-then plans reduce reliance on in-the-moment deliberation and improve goal attainment. Pre-commitment research finds structural rules outperform in-the-moment decisions. (observational)
Rules work when the situation is sufficiently predictable to match; highly novel or complex situations may require deliberation that fixed rules can’t substitute for.
Sources
- Gollwitzer & Sheeran (2006), implementation intentions meta-analysis, Advances in Experimental Social Psychology
Common mistake
Making the rule so general ("eat healthy") that it provides no guidance in the specific temptation moment — the rule must specify the exact behavior in the exact situation.
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More practices for Simple Heuristics: Gerd Gigerenzer’s Case for Fast and Frugal Thinking
- Trust the recognition heuristic in uncertain environments
If you recognize one option and not the other, the recognized one is usually better — in the right domain.
- Use "take the best": choose on your single most informative cue
When choosing between options, identify the most diagnostic cue and use it — stop searching for more.
- Satisfice: set a good-enough threshold and stop searching when you hit it
Optimize for "good enough" rather than "best possible" — the search cost often exceeds the gain.
- Match your heuristic to the structure of the environment
A good rule works because it matches the statistical regularities of the environment — wrong environment, wrong rule.
- Use the 1/N rule for diversification under deep uncertainty
When you cannot estimate the value of each option reliably, spread resources equally.
- Build your personal adaptive toolbox of domain-specific rules
The goal isn’t one universal heuristic — it’s a curated collection that matches the domains you actually navigate.