AI Researchers Unveil Polynomial-Time Constitutional Governance for Digital Communities

In a new paper on arXiv, computer scientists Ehud Shapiro and Nimrod Talmon introduce a comprehensive framework for constitutional governance in metric spaces. The key innovation is an end-to-end, polynomial-time process that integrates multiple stages of collective decision-making—aggregation, deliberation, amendment, and consensus—which prior work treated in isolation. The framework assigns a metric space, aggregation rule, and supermajority threshold to each amendable component of the constitution. Members submit an ideal element (vote and proposal), and any member can submit a public proposal that carries supermajority support. The generalized median serves as the worked rule, establishing framework-level guarantees including that no misreporting weakly dominates sincere voting. The authors also study the compromise gap between the best peak and unconstrained optimum, showing it is zero in one dimension and bounded generally, narrowed by a simple heuristic in simulations.

This work is significant because it provides a practical, computationally tractable basis for democratic governance in digital communities and organizations. It unifies metric-space aggregation, reality-aware social choice, supermajority amendment, constitutional consensus, deliberative coalition formation, and AI mediation—all within a single framework. The paper instantiates the framework on seven canonical settings and includes a utilitarian alternative via the mean in the appendix. By delivering a polynomial-time solution to problems that traditionally involve NP-hard aggregators, this research could enable scalable, transparent, and egalitarian decision-making for DAOs, online platforms, and other digital collectives, potentially reshaping how such entities govern themselves.