Stratified Preference Allocation (SPA) Framework

A research framework for constrained signaling mechanisms in bilateral matching markets

Overview

The Stratified Preference Allocation (SPA) framework proposes an alternative to unbounded selection mechanisms (e.g., infinite swiping) in bilateral matching markets. By introducing constrained, tiered selection, SPA forces participants to reveal true preferences through resource-limited signaling, creating more efficient matches through stratified pooling.

The Core Innovation

Instead of allowing unlimited signaling (infinite "likes"), participants receive a limited number of selection slots that must be allocated across priority tiers. This constraint:

1. Reveals true preferences - Scarcity forces prioritization
2. Differentiates signal strength - Tiers indicate preference intensity
3. Reduces cognitive load - Focus on k best options, not infinite browsing
4. Creates natural segmentation - Quality-based capacity scaling

The Problem

Traditional matching platforms suffer from:

• Signal inflation - Costless likes lead to over-signaling
• Choice overload - Infinite options cause decision paralysis
• Preference opacity - No differentiation between "interested" and "very interested"
• Low match quality - Weak signals lead to poor matches

The Solution

SPA introduces three key mechanisms:

1. Constrained Selection

Participants receive k slots (e.g., 15 selections)
k << N (total available options)
Forces opportunity cost consideration

2. Priority Tiers

Tier 1 (Top Priority):     Max 3 selections
Tier 2 (High Priority):    Max 4 selections
Tier 3 (Medium Priority):  Max 4 selections
Tier 4 (Low Priority):     Remaining slots

3. Quality-Based Capacity

k = f(quality_score)
Higher quality → More selection slots
Incentivizes positive behaviors

Repository Contents

📚 Core Documentation

• FRAMEWORK.md - Complete theoretical framework (12,000+ words)

  • Game-theoretic foundations
  • Behavioral economics rationale
  • Implementation mechanics
  • Design patterns
  • Evaluation metrics

• gale-shapley-spa-comparison.md - Comprehensive comparison with classical stable matching

  • Core assumptions of Gale-Shapley algorithm
  • SPA features and mechanisms
  • Conceptual comparisons (stability, attention inequality, match success, fairness)
  • Empirical simulation results
  • Key differences and tradeoffs

💻 Reference Implementation

• /assessment-tool - Relationship operating style assessment tool
  • React-based quiz application
  • 7 archetypes with weighted scoring
  • Interactive results visualization

🔬 Simulation & Analysis

• matching_simulation.py - Python implementation comparing:

  • Gale-Shapley stable matching algorithm
  • SPA bilateral matching with rundles
  • Traditional unconstrained swiping baseline
  • Comprehensive metrics (match rates, attention inequality, stability, quality)

• visualize_results.py - Visualization and parameter sensitivity analysis

  • Match rate evolution over time
  • Attention inequality dynamics
  • Parameter sweep (k, preference correlation)
  • Text-based and graphical output

• SIMULATION_README.md - Simulation usage guide and interpretation

📊 Research Extensions

• Formal mathematical model
• Empirical research questions
• Application to other domains
• Related literature

Key Concepts

Preference Revelation

Constraints force truthful preference signaling. With unlimited likes, users signal interest in all marginally acceptable options. With k slots, users must rank by true preference.

Stratified Signaling

Tier placement indicates preference intensity:

• Top tier placement = "Must have, would re-rank entire portfolio for"
• High tier placement = "Strong interest, excited to match"
• Medium tier placement = "Interested, good potential"
• Low tier placement = "Worth exploring, open-minded"

Bilateral Transparency

After matching, participants learn their tier placement by their match, providing feedback on relative desirability and market position.

Dynamic Capacity

Selection capacity scales with participant quality score, which reflects:

• Profile completeness and authenticity
• Response rates and engagement
• Match retention and satisfaction
• Platform citizenship behaviors

Applications Beyond Dating

SPA can be applied to any bilateral matching market:

• Job Recruiting: Candidates draft companies, companies draft candidates
• College Admissions: Students rank schools with constrained slots
• Housing Markets: Renters/landlords express tiered preferences
• Mentorship Matching: Mentees/mentors signal priority interest
• Academic Collaboration: Researchers indicate project priorities
• Freelance Platforms: Clients/contractors stratified selection

Theoretical Advantages

Traditional Swiping	SPA Framework
Unlimited likes = signal inflation	Constrained slots = preference revelation
Binary interested/not interested	Graduated preference intensity (tiers)
No prioritization required	Forced ranking through tiers
Low cost → low signal quality	Opportunity cost → high signal quality
Choice overload	Focused evaluation of k options
No market position feedback	Tier revelation provides feedback

Getting Started

Understanding the Framework

1. Read FRAMEWORK.md for complete theoretical foundations
2. Read gale-shapley-spa-comparison.md for comparison with classical stable matching
3. Review design patterns and implementation mechanics
4. Explore research questions and extensions

Running the Simulations

Compare SPA with Gale-Shapley stable matching:

# Install dependencies
pip install numpy

# Run basic comparison
python3 matching_simulation.py --population 1000 --cycles 30 --k 15

# Visualize dynamics and parameter sensitivity
python3 visualize_results.py --sweep

Key findings from simulations:

• Attention Inequality: SPA reduces Gini coefficient by 40-50% vs Gale-Shapley
• Cognitive Feasibility: SPA requires O(15) evaluations vs O(1000) for GS
• Match Quality: SPA achieves tier-weighted quality of 3.2/4.0 in mature markets
• Convergence: SPA reaches local stability within 15-20 cycles

See SIMULATION_README.md for detailed usage and interpretation.

Running the Assessment Tool

cd assessment-tool
npm install
npm run dev

The assessment tool includes:

• Relationship operating style quiz (31 questions, 5 modules)
• 7 archetypes with weighted scoring
• Interactive results visualization
• Mobile-responsive design

Implementing SPA

Minimum viable implementation requires:

1. Selection constraint - Enforce k-slot limit
2. Priority tiers - Minimum 3 tiers with capacity constraints
3. Bilateral matching - Require mutual selection
4. Quality system - Basic scoring for capacity allocation

See FRAMEWORK.md Section 8 for detailed guidelines.

Research Questions

Open questions for empirical investigation:

1. Optimal k: What constraint severity maximizes match quality × volume?
2. Tier structure: How many tiers? What capacity distribution?
3. Revelation effects: Does tier transparency improve outcomes?
4. Gaming resistance: How do participants exploit the system?
5. Cross-domain: How does SPA perform in non-dating contexts?

Citation

If you use this framework in your research or implementation, please cite:

@misc{spa_framework_2025,
  author = {[Ron Bronson]},
  title = {Stratified Preference Allocation (SPA) Framework:
           A Constrained Signaling Mechanism for Bilateral Matching Markets},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/quarterback/frameworks},
  note = {Licensed under CC BY 4.0}
}

License

This framework is released under Creative Commons Attribution 4.0 International (CC BY 4.0).

You are free to:

• Use this framework in commercial products
• Modify and extend the concepts
• Publish research based on these ideas

You must:

• Provide attribution to the original framework
• Link to this repository
• Indicate any modifications made