hicks-law

Original：🇺🇸 English

Translated

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.

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Sourceflpbalada/my-opencode-config

Added on2026-02-24

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SKILL.md Content

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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

Chunking: Group related items (3-4 per group)
Progressive disclosure: Hide advanced options
Smart defaults: Pre-select the common choice
Filtering: Let users narrow options
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