A Human Behavioral Baseline for Collective Governance in Software Projects

2025-10-09

AI Summary

This study investigates how open-source communities encode participation mechanisms and power structures in version-controlled governance documents, aiming to establish a reproducible benchmark for governance evolution to assess AI-driven effects on authority centralization or redistribution. Methodologically, we propose a text-parsing framework to extract governance-relevant quadruples—(actor, rule, action, object)—and quantify textual balance, diversity, and temporal drift using entropy, richness, and Jensen–Shannon divergence. Innovatively, this work establishes the first behavior-level, reproducible baseline for governance analysis. Results reveal that over time, actor roles and permitted actions significantly expand and become more evenly distributed, whereas rule composition remains stable—indicating that governance evolution manifests primarily as participatory scaling and balancing, rather than normative power restructuring. These findings provide a critical empirical benchmark and analytical framework for evaluating collaborative governance in the AI era.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Multiagent Systems: Mechanism Design
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority.

Problem

• Analyzing governance evolution in open source projects over time
• Measuring changes in roles, rules and actions using entropy metrics
• Establishing baseline to evaluate AI's impact on authority distribution

Innovation

• Parsing governance documents into structured components
• Measuring change with entropy richness and divergence
• Providing baseline for AI workflow authority evaluation