Constitutional Governance in Metric Spaces

• URL: http://arxiv.org/abs/2605.13362v2
• Date: Thu, 14 May 2026 15:12:56 GMT
• Title: Constitutional Governance in Metric Spaces
• Abstract summary: Computational social choice and decision theory offer rich aggregation theory but no comprehensive process for egalitarian self-governance.<n>Here, we propose constitutional governance in metric spaces, integrating deliberation, amendment, and consensus.<n>We focus on the utility of the generalised median, prove that at majority threshold no misreport and study the compromise gap between best peak and unconstrained optimum.
• Score: 8.575010739465993
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational social choice and algorithmic decision theory offer rich aggregation theory but no comprehensive process for egalitarian self-governance: aggregation, deliberation, amendment, and consensus are each considered in isolation, with key metric-space aggregators being NP-hard. Here, we propose constitutional governance in metric spaces, integrating these stages into a coherent polynomial-time protocol for constitutional governance. The constitution assigns, per amendable component including itself, a metric space, aggregation rule, and supermajority threshold. Amendments proceed by members voting with their ideal elements, followed by members submitting public proposals carrying supermajority public support under the revealed votes. Public proposals can be sourced from deliberation among members, vote aggregation, or AI mediation. The constitutional rule adopts a supported proposal with positive maximal score, if there is one, else retains the status quo. With Constitutional Consensus, a community can run the constitutional governance protocol on members' personal computing devices (e.g., smartphones), achieving digital sovereignty. We focus on the utility of the generalised median, prove that at majority threshold no misreport weakly dominates sincere voting, and study the compromise gap between best peak and unconstrained optimum. We instantiate the framework to seven canonical settings -- electing officers, setting rates, allocating budgets, ranking priorities, selecting boards, drafting bylaws, and amending the constitution. By unifying metric-space aggregation, reality-aware social choice, supermajority amendment, constitutional consensus, deliberative coalition formation, and AI mediation, this work delivers a comprehensive solution to the constitutional governance of digital communities and organisations.

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