home / skills / flpbalada / my-opencode-config / hicks-law

hicks-law skill

/skills/hicks-law

This skill helps you optimize decision speed by reducing choices in navigation, onboarding, and forms, applying Hick's Law to improve user flow.

npx playbooks add skill flpbalada/my-opencode-config --skill hicks-law

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
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/)

Overview

This skill applies Hick's Law to reduce choice overload and speed user decisions. It helps designers and product teams audit decision points, prioritize options, and implement reduction strategies to improve conversion and satisfaction. Use it to shape menus, onboarding, forms, dashboards, and feature exposure for faster, clearer user choices.

How this skill works

The skill inspects interfaces to count and classify decision points, then recommends targeted reductions using techniques like chunking, progressive disclosure, smart defaults, filtering, and recommendations. It estimates decision-time impact using the RT = a + b * log2(n+1) concept and produces a practical reduction plan with quick wins and tradeoff analysis. Outputs include an inventory table, reduction plan, and expected impact metrics.

When to use it

  • Building or restructuring navigation menus and information architecture
  • Designing onboarding flows or setup wizards to maximize completion
  • Optimizing conversion funnels, forms, and checkout choices
  • Prioritizing feature discovery or exposure in product UIs
  • Designing dashboards or toolbars with many controls

Best practices

  • Limit grouped options to about 5–7 items; use categories to chunk larger sets
  • Prefer smart defaults and highlight recommended choices to reduce friction
  • Apply progressive disclosure for advanced or infrequent options
  • Provide search and filters for large catalogs instead of flat lists
  • Measure outcomes (completion time, conversion, support tickets) after changes

Example use cases

  • Audit a signup/onboarding flow: reduce initial questions from 12 to 3 essentials with follow-up defaults
  • Redesign a product menu: convert a 10-item flat menu into 3 top categories with 3–4 items each
  • Optimize checkout: pre-select shipping/payment methods based on user history
  • Improve dashboard layout: hide advanced controls behind an 'Advanced' panel and surface top actions
  • Increase conversion: highlight 'Most Popular' options and reduce equal visual weight

FAQ

Will removing options frustrate power users?

Not if you use progressive disclosure and allow advanced users to access hidden options; keep essential expert features available but not front-and-center.

How many choices are optimal?

Aim for a 4–7 item sweet spot per visible group; larger sets should be chunked, filtered, or searchable.