--- name: hicks-law description: Optimize decision-making speed by managing choice quantity. Use when designing navigation, menus, feature sets, onboarding flows, or any interface where users must choose between options. --- # Hick's Law - Less Choice, Faster Decisions Hick's Law (also Hick-Hyman Law) states that the time it takes to make a decision increases logarithmically with the number and complexity of choices. Named after British psychologist William Edmund Hick and American psychologist Ray Hyman (1952). ## When to Use This Skill - Designing navigation menus and information architecture - Simplifying onboarding and setup flows - Reducing form field options - Prioritizing feature exposure - Optimizing conversion funnels - Planning dashboard layouts ## Core Concepts ### The Formula ``` RT = a + b * log2(n+1) Where: RT = Reaction time a = Time not involved in decision (physical movement, etc.) b = Empirical constant (~0.155s for choice tasks) n = Number of equally probable choices ``` ### Practical Impact | Choices | Relative Decision Time | User Experience | | ------- | ---------------------- | --------------------- | | 2 | Baseline | Quick, confident | | 4 | +1 unit | Still manageable | | 8 | +2 units | Starting to slow | | 16 | +3 units | Noticeable hesitation | | 32 | +4 units | Overwhelm begins | | 64+ | +5+ units | Paralysis likely | ### The Paradox of Choice ``` User Satisfaction ^ | * | * * | * * | * * |* *____ +-----------------------> Number of Choices Sweet spot (4-7 items) ``` ## Analysis Framework ### Step 1: Audit Decision Points Map all places users must choose: | Screen/Flow | Decision Type | Options Count | Complexity | | ----------- | ------------- | ------------- | ---------- | | [Screen 1] | Navigation | [n] | [H/M/L] | | [Screen 2] | Selection | [n] | [H/M/L] | | [Screen 3] | Configuration | [n] | [H/M/L] | ### Step 2: Categorize Choices ``` Essential (keep) Nice-to-have (maybe) Remove | | | v v v [_______] [_______] [_______] [_______] [_______] [_______] [_______] [_______] [_______] ``` ### Step 3: Apply Reduction Strategies 1. **Chunking**: Group related items (3-4 per group) 2. **Progressive disclosure**: Hide advanced options 3. **Smart defaults**: Pre-select the common choice 4. **Filtering**: Let users narrow options 5. **Recommendations**: Highlight "Most Popular" ## Output Template ```markdown ## Hick's Law Analysis **Interface/Flow:** [Name] **Analysis Date:** [Date] ### Decision Point Inventory | Location | Current Options | Target | Strategy | | --------- | --------------- | ------ | -------------------- | | [Point 1] | [n] | [n] | [Chunk/Hide/Default] | | [Point 2] | [n] | [n] | [Chunk/Hide/Default] | ### Reduction Plan **Quick wins (no functionality loss):** 1. [Change 1] 2. [Change 2] **Strategic reductions (requires tradeoffs):** 1. [Change with impact analysis] ### Expected Impact - Decision time reduction: ~[X]% - Conversion improvement: ~[X]% (estimated) - Support ticket reduction: ~[X]% (estimated) ``` ## Real-World Examples ### Example 1: Netflix vs. Cable **Cable TV**: 500+ channels = Decision paralysis - Users spend more time browsing than watching - Satisfaction decreases despite more options **Netflix approach**: - Curated rows (chunking) - "Top 10" highlights (social proof + reduction) - "Because you watched..." (personalized filtering) - Auto-play (eliminates decision entirely) ### Example 2: In-N-Out Burger Menu has only 4 items vs. competitors' 50+: - Order time: 30 seconds vs. 2+ minutes - Customer satisfaction: Higher - Operation efficiency: Better The constraint creates confidence in choice quality. ### Example 3: Slack's Onboarding Original: 15 configuration options upfront - Completion rate: 62% - Time to complete: 8 minutes Redesigned: 3 essential questions, rest defaulted - Completion rate: 89% - Time to complete: 2 minutes ## Best Practices ### Do - Aim for 5-7 options maximum in any grouping - Use categorization to chunk larger sets - Provide clear visual hierarchy - Make the "default" choice obvious - Offer search/filter for large option sets ### Avoid - Showing all features at once - Flat menus with 10+ items - Requiring decisions without clear benefit - Equal visual weight for all options - Removing options users actively need ### When Hick's Law Doesn't Apply - Expert users with learned shortcuts - Emergency situations (trained responses) - When options are not equally weighted - Sequential vs. parallel choices ## Reduction Techniques ### 1. Smart Defaults ``` Instead of: [ ] Option A [ ] Option B [ ] Option C Do: [x] Option B (Recommended) [ ] Option A [ ] Option C ``` ### 2. Progressive Disclosure ``` Basic Options [Configure] v Advanced (click to expand) [_] Setting 1 [_] Setting 2 ``` ### 3. Chunking ``` Instead of 12 flat options: Category A Category B Category C - Item 1 - Item 5 - Item 9 - Item 2 - Item 6 - Item 10 - Item 3 - Item 7 - Item 11 - Item 4 - Item 8 - Item 12 ``` ## Integration with Other Methods | Method | Combined Use | | -------------------------- | -------------------------------------- | | **Progressive Disclosure** | Hide complexity, reveal on demand | | **Cognitive Load** | Fewer choices = lower cognitive burden | | **Fogg Behavior Model** | Simpler choices increase ability | | **Jobs-to-be-Done** | Focus options on user's actual job | ## Resources - [On the Rate of Gain of Information - Hick (1952)](https://psycnet.apa.org/record/1953-03853-001) - [The Paradox of Choice - Barry Schwartz](https://www.amazon.com/Paradox-Choice-Why-More-Less/dp/0060005696) - [Don't Make Me Think - Steve Krug](https://sensible.com/dont-make-me-think/)