Table of Contents

ChatGPT as an Intermediate Aggregator

Part of the "Big Three" Consensus

In our 3×3 protocol for the Top 10 AI Cities Ranking, ChatGPT serves as one of the three intermediate aggregators. Its role is to process results from 10 different AI models, identify patterns, resolve conflicts, and produce a preliminary consensus ranking. This intermediate layer ensures higher reliability before reaching the final consensus.

Data Collection: Two independent specialists collected 20 responses (10 models × 2 queries each) to ensure comprehensive coverage and reduce individual bias.

Role in the 3×3 Protocol

ChatGPT serves as one of the three core aggregators in the intermediate consensus layer. This position is critical for transforming raw data from 10 different AI models into a meaningful preliminary ranking.

1
Input Integration
ChatGPT receives normalized results from 10 diverse AI models, each providing their Top 10 AI Cities rankings.
2
Conflict Resolution
Identifies and resolves discrepancies between different model outputs using advanced reasoning capabilities.
3
Pattern Recognition
Analyzes common patterns across all inputs to identify cities with consistent high rankings.
4
Preliminary Consensus
Produces an intermediate consensus ranking that feeds into the final 3×3 consensus calculation.
Why ChatGPT was selected
Advanced Reasoning Capabilities
ChatGPT excels at identifying nuanced patterns and relationships between data points that simpler algorithms might miss.
Contextual Understanding
Ability to understand the context of "AI city" beyond raw statistics, considering innovation ecosystems and talent pipelines.
Bias Mitigation
Trained to recognize and compensate for potential biases in source data through counterfactual reasoning.

Real-Time AI Authority

Enhanced Decision-Making Capabilities

The Aggregator Model serves as the lead AI and Judge. It possesses the authority to remove material, perform real-time internet research for data validation, and append missing data.

Aggregation Process

ChatGPT employs a robust and transparent multi-stage process that balances simplicity with effectiveness, transforming inputs from 10 AI models into a reliable intermediate consensus:

Core Aggregation Philosophy

Robustness Over Complexity

ChatGPT prioritizes robust statistical methods over complex heuristics, using proven techniques like Borda count and median ranking alongside adaptive weighting to ensure reliable results even with conflicting inputs.

1
Data Normalization
All input rankings are converted to a standardized 0-100 scale, with cities missing from some lists assigned appropriate default values.
2
Confidence Scoring
Each source model is assigned a confidence weight based on its historical accuracy and specificity of reasoning.
3
Weighted Aggregation
City scores are calculated using a weighted average approach, giving more influence to higher-confidence sources.
4
Rank Adjustment
Final positions are adjusted based on consensus patterns and outlier analysis.

Key Technical Aspects

Graph-based Analysis
Models relationships between cities as a knowledge graph to identify innovation clusters.
Trend Analysis
Identifies upward and downward momentum in city rankings across multiple sources.
Robustness Checks
Performs leave-one-out analysis to ensure results aren't dependent on any single source.
Weighting System

ChatGPT employs adaptive weighting that learns from data patterns rather than using fixed coefficients, ensuring dynamic and evidence-based aggregation:

Hybrid Aggregation Methods

Borda Count Method
Primary method: Sum of position points across all models. City ranked 1st gets 10 points, 2nd gets 9 points, etc. This reduces sensitivity to outlier scores.
Median Rank Aggregation
Secondary validation: Calculate median position for each city across models. Provides robust consensus when models strongly disagree.
Weighted Average (When Appropriate)
Used only when models show high agreement (CV < 0.15). Combines normalized scores with adaptive weights based on model performance.

Adaptive Weight Calibration

# Dynamic weight learning algorithm def calibrate_adaptive_weights(models, historical_data): # Cross-validation approach weights = {} for model in models: # Leave-one-out validation accuracy = validate_without_model(model, historical_data) consistency = calculate_kendall_tau(model, other_models) recency = model.data_recency / MAX_RECENCY # Adaptive weight based on performance weights[model] = accuracy * 0.5 + consistency * 0.3 + recency * 0.2 # Normalize weights and apply 25% cap weights = normalize_and_cap(weights, max_contribution=0.25) return weights
Reliability Factors
Models with proven track records receive higher weights (30-40% impact)
Explanation Quality
Detailed, evidence-based explanations increase weight (25-35% impact)
Data Recency
Models using newer data receive bonus weighting (15-25% impact)

Transparency & Explainability

Natural Language Explanations

ChatGPT generates comprehensive explanations for every ranking decision:

# Example output for each city "San Francisco ranks #1 because: - 8 of 10 models placed it in top 3 (Borda score: 87) - Median rank across all models: 2nd position - Strong consensus on AI talent (similarity: 0.78) - Model contributions: GPT-4 (12%), Claude (11%), Gemini (10%)..."
  • Direct city comparisons: "London ranks above Berlin due to stronger venture funding ecosystem"
  • Confidence percentages: Precise scores (e.g., 87%) instead of vague High/Medium/Low
  • Full audit trail: Every calculation step documented and verifiable
  • Preventing dominance: No single source can account for more than 25% of the final weight

Robustness Mechanisms

Automatic Outlier Detection
Models deviating >2 standard deviations from consensus are flagged and down-weighted automatically to prevent skewing results.
Continuous Recalibration
Leave-one-out validation runs continuously. If removing any model changes rankings significantly (>3 positions), weights auto-adjust to increase stability.
Statistical Consensus Tests
Kendall's tau and Spearman correlation verify agreement levels. Low correlation triggers deeper analysis and potential expert review.
Tie Resolution Method

When cities have identical aggregated scores, ChatGPT employs a multi-step tie-breaking procedure:

1
Consistency Check
City that appears in more source rankings wins the tie
2
Momentum Analysis
City showing stronger upward trend in recent rankings
3
Innovation Density
Higher concentration of AI startups and research institutions
4
Talent Pipeline
Strength of local universities producing AI talent
Quantitative Factors
Patent filings, VC funding, research papers
Qualitative Factors
Expert opinions, policy environment, quality of life
Dynamic Adjustment
Tie-breaking criteria weights adapt based on context
ChatGPT's Unique Value in the Consensus Process
Contextual Intelligence
Understands that "AI potential" encompasses more than just current metrics - includes innovation trajectory, policy environment, and talent development pipelines.
Pattern Recognition
Identifies emerging tech hubs that might be undervalued by traditional ranking systems but show strong growth signals.
Bias Detection
Flags and corrects for geographic or recency biases present in source data through counterfactual analysis.
Explanation Generation
Produces human-readable justifications for ranking decisions, enhancing transparency.
ChatGPT Intermediate Consensus Results
Consensus Ranking TOP 10
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