pre-preference-elicitation

name	pre - Preference Elicitation
description	Elicit user preferences by presenting concrete trade-offs rather than asking open-ended questions. System does the work by generating options and letting user select.
output	{"format":"prose"}

Preference Elicitation

Input: $ARGUMENTS (domain or guess category to elicit preferences for)

---

Purpose

Discover what the user values by presenting concrete choices rather than asking them to articulate preferences directly.

Principle: People reveal preferences through choices better than through descriptions. Present trade-offs, observe selections.

---

Step 1: Generate Trade-Off Pairs

For the domain/category, generate pairs of options that trade off against each other:

TRADE-OFF 1: [dimension being traded]
├── Option A: [high on X, low on Y]
└── Option B: [low on X, high on Y]

TRADE-OFF 2: [dimension being traded]
├── Option A: [high on X, low on Y]
└── Option B: [low on X, high on Y]

Minimum: Generate 10 trade-off pairs covering different preference dimensions.

---

Step 2: Preference Dimensions to Cover

Dimension	Trade-Off	Option A	Option B
Time vs Money	Spend time to save money OR spend money to save time	Time-rich, money-poor	Money-rich, time-poor
Risk vs Reward	Lower risk, lower reward OR higher risk, higher reward	Safe, modest gains	Risky, potential big gains
Control vs Convenience	Full control, more effort OR less control, easier	DIY, full control	Done-for-you, hands-off
Speed vs Quality	Fast, good enough OR slow, excellent	Quick and dirty	Slow and polished
Autonomy vs Security	Independent, uncertain OR employed, stable	Freedom, risk	Stability, constraints
Public vs Private	Visible, social proof OR hidden, privacy	Build in public	Work in private
Generalist vs Specialist	Breadth, flexibility OR depth, expertise	Know many things	Know one thing deeply
Short-term vs Long-term	Immediate payoff OR delayed larger payoff	Quick wins	Long-term building
Solo vs Team	Work alone, keep all OR work with others, share	Full ownership	Shared effort/reward
Scale vs Craft	Many customers, less personal OR few customers, high-touch	Volume	Boutique

---

Step 3: Present as Scenarios, Not Abstractions

Don't ask: "Do you prefer risk or safety?" Instead present: "Would you rather have a 90% chance of making $50K or a 20% chance of making $500K?"

For each trade-off, generate 2-3 concrete scenarios:

SCENARIO SET: [trade-off dimension]

Scenario A: [concrete situation embodying option A]
- What happens: [specific outcome]
- You get: [specific benefit]
- You give up: [specific cost]

Scenario B: [concrete situation embodying option B]
- What happens: [specific outcome]
- You get: [specific benefit]
- You give up: [specific cost]

User selects: [A / B / NEITHER / BOTH]

---

Step 4: Infer Preferences from Selections

Selection	Inferred Preference	Confidence
Chose A in Scenario 1	Values X over Y	MEDIUM
Chose B in Scenario 2	Values Y over X	MEDIUM
Chose A in Scenario 3	Confirms: Values X	HIGH

Consistency check: If user chose A in Scenario 1 but B in Scenario 3, and both test the same dimension, probe for context dependence.

---

Step 5: Generate Preference Profile

PREFERENCE PROFILE

Strong Preferences (consistent across scenarios):
├── [preference 1]: [evidence from selections]
├── [preference 2]: [evidence from selections]
└── [preference 3]: [evidence from selections]

Weak Preferences (single selection):
├── [preference 4]: [single evidence]
└── [preference 5]: [single evidence]

Context-Dependent (inconsistent):
├── [preference 6]: Prefers X when [context], Y when [other context]
└── [preference 7]: Prefers X when [context], Y when [other context]

Unknown (not tested):
├── [dimension not covered]
└── [dimension not covered]

---

Step 6: Match Preferences to Options

For a list of options (e.g., methods from guess_generation):

Option	Matches Preferences	Conflicts With	Fit Score
[option 1]	[pref A, pref B]	[pref C]	HIGH/MED/LOW
[option 2]	[pref A]	[pref B, pref C]	LOW
[option 3]	[pref A, pref B, pref C]	none	HIGH

Surface better options: If an option matches all preferences but user hasn't mentioned it, highlight it.

---

Step 7: Detect Settling

Signs user is settling on suboptimal option:

1. Chosen option conflicts with stated preferences
2. Unchosen option matches preferences better
3. User hasn't considered high-match options

SETTLING DETECTION

User's current choice: [option X]
Preference match: [score]

Better matches not considered:
├── [option Y]: matches [preferences], score [higher]
├── [option Z]: matches [preferences], score [higher]

Why user might be settling:
├── [didn't know about option Y]
├── [assumed constraint that doesn't exist]
├── [first viable option, stopped looking]

Recommendation: Present [option Y, Z] before user commits to [option X]

---

Output Format

## PREFERENCE ELICITATION: [domain]

### Trade-Offs Presented
[list of scenario pairs]

### Selections Made
[user's choices]

### Preference Profile
STRONG:
- [preference]: [evidence]

WEAK:
- [preference]: [evidence]

CONTEXT-DEPENDENT:
- [preference]: [when X] vs [when Y]

### Option Matching
| Option | Preference Match | Conflicts | Score |
|--------|------------------|-----------|-------|
| ... | ... | ... | ... |

### Better Options to Consider
[options that match preferences but user hasn't considered]

### Settling Risk
[if user is choosing suboptimally, explain why and present alternatives]

---

Execution Checklist

• 10+ trade-off pairs generated
• Concrete scenarios created (not abstractions)
• User selections collected
• Preference profile generated
• Options matched to preferences
• Better options surfaced
• Settling detection run

---

Next Procedure

→ INVOKE: /boc [user's current choice] [preference profile]

---

Execute now: Elicit preferences for the domain provided.