GDP-Grounded Software Selection

Summary: A methodology for prioritizing software applications based on their economic impact by leveraging occupational wage data from GDP statistics and O*NET occupational data. This approach ensures that software selection for research, benchmarking, or agent training reflects real-world economic significance rather than convenience or popularity, providing systematic coverage across all major economic sectors.

Overview

GDP-Grounded Software Selection represents a systematic approach to choosing software applications based on their contribution to economic productivity. Rather than selecting software arbitrarily or based on researcher convenience, this methodology uses U.S. occupational data to identify applications that have measurable impact on GDP across different economic sectors.

The core principle involves mapping software applications to occupational categories from the Standard Occupational Classification (SOC) system and weighting selection based on the economic output of those occupations. This ensures comprehensive coverage across all 22 SOC major occupation groups, creating a representative sample of economically significant software tools.

The methodology addresses a critical gap in Computer-Use Agents research, where software selection often reflects academic or technical bias rather than real-world economic importance. By grounding selection in GDP data, researchers can create more realistic and economically relevant benchmarks for evaluating agent performance. The approach leverages O*NET occupational databases to systematically identify software tools that are genuinely important to economic productivity across diverse sectors including healthcare, engineering, finance, and scientific domains.

Key Details

  • Coverage Scope: Encompasses all 22 SOC major occupation groups to ensure comprehensive economic representation
  • Data Sources: Utilizes U.S. occupational wage and employment statistics as proxy measures for economic impact, combined with O*NET occupational data for software mapping
  • Selection Criteria: Software applications are weighted and prioritized based on the GDP contribution of their associated occupations rather than popularity or convenience
  • Application Scale: Successfully applied to select 200 software applications for the CUA-World benchmark, creating 10,000+ interactive tasks
  • Economic Grounding: Moves beyond convenience-based selection to reflect actual economic productivity patterns across major economic sectors
  • Sector Balance: Ensures representation across diverse economic sectors from healthcare to manufacturing to professional services
  • Validation Approach: Provides objective, data-driven justification for software inclusion decisions based on quantifiable economic metrics
  • Scalability: Can be adapted to different geographic regions or economic contexts using local occupational data and GDP statistics
  • Implementation: Integrated with Multi-Agent Environment Creation systems to automatically generate economically relevant software environments
  • Quality Assurance: Works in conjunction with Creation-Audit Loop processes to verify that selected software environments are properly configured and functional

Relationships

  • Computer-Use Agents — provides economically grounded software environments for agent training and evaluation, ensuring agents learn skills relevant to real economic productivity
  • CUA-World — benchmark created using GDP-grounded selection methodology to ensure economic relevance, resulting in 200+ software applications across all major occupational groups
  • Agent Evaluation — improves benchmark validity by selecting software that reflects real-world economic importance rather than academic convenience
  • Multi-Agent Environment Creation — supplies principled software selection criteria for automated environment generation, ensuring created environments have economic relevance
  • O*NET Occupational Data — leverages occupational databases to systematically map software applications to economic sectors and job functions
  • Economic Impact Assessment — applies economic analysis principles to technology research methodology, quantifying the GDP contribution of different software categories
  • Occupational Classification Systems — leverages SOC framework to map software to economic sectors and ensure comprehensive coverage across all major occupation groups
  • Benchmark Design — establishes new standards for creating representative software evaluation suites that reflect real-world economic priorities
  • Research Methodology — demonstrates data-driven approach to research artifact selection that can be replicated and validated across different contexts
  • Test-Time Auditing — benefits from economically relevant software selection by focusing evaluation efforts on tasks that matter for real-world productivity

Sources

  • sources/arxiv-260406126 — introduced GDP-grounded methodology for selecting 200 software applications across all SOC occupation groups for CUA-World benchmark creation, demonstrating systematic approach using O*NET occupational data and U.S. GDP statistics