GDP-Grounded Benchmarking

Summary: A methodology for selecting software applications and tools for research benchmarks based on their economic impact rather than researcher convenience or availability. This approach uses economic data like GDP contribution and occupational statistics to ensure benchmark relevance to real-world productivity and value creation.

Overview

GDP-Grounded Benchmarking represents a shift from traditional benchmark construction that often relies on easily accessible or popular software toward systematic selection based on economic significance. The methodology leverages economic data sources, particularly U.S. Bureau of Labor Statistics occupational data and GDP contribution metrics, to identify software applications that have measurable impact on economic productivity.

This approach addresses a critical bias in Computer-Use Agents research where benchmarks often focus on consumer applications or developer tools rather than the enterprise and professional software that drives significant economic activity. By grounding software selection in economic data, researchers can create more representative benchmarks that reflect actual software usage patterns across the economy.

The methodology ensures coverage across diverse economic sectors by using frameworks like the Standard Occupational Classification (SOC) system, which categorizes all occupations in the U.S. economy. This systematic approach helps avoid the natural researcher bias toward familiar or easily accessible software.

Key Details

• Data Sources: Utilizes U.S. occupational data and GDP contribution metrics to rank software by economic impact
• Coverage Scope: Can span all 22 SOC major occupation groups to ensure comprehensive economic representation
• Selection Criteria: Prioritizes software applications based on their contribution to economic productivity rather than popularity or accessibility
• Benchmark Size: Enables creation of large-scale benchmarks (e.g., 200+ software applications with 10,000+ tasks)
• Sector Representation: Ensures inclusion of enterprise software, professional tools, and industry-specific applications often overlooked in academic research
• Validation Method: Uses economic data as ground truth for software importance rather than subjective researcher judgment

Relationships

• Computer-Use Agents — provides more economically relevant evaluation environments for agent training and testing
• Agent Evaluation — creates benchmarks that better reflect real-world software usage and economic impact
• Multi-Agent Environment Creation — supplies principled software selection criteria for automated environment generation
• Economic Impact Assessment — applies economic analysis methods to technology research and benchmark design
• Cross-Software Generalization — ensures training and testing across economically diverse software categories
• Automated Verification — enables systematic validation of software selection based on quantitative economic metrics

Sources

• raw/articles/arxiv-260406126 — introduced the concept through CUA-World benchmark creation, demonstrating systematic selection of 200 software applications based on U.S. GDP data across all major occupation groups