Adaptive Weighted Democracy
Year: 2026
Type: article
Abstract: The DPA treats democratic decision-making as a collective neural network: people are the units, certified competences by issue are the weights, technical constraints produce an error signal that returns to the proposals, and the re-vote is the correction step. Public decisions are broken down into issues: objectives, technical constraints, strategies, universal rights. Every person has recognised competences, derived from education and work experience and certified by several independent AI systems, without human intervention. On each issue, those who exceed the competence threshold relevant to that issue vote, with a weight that grows with competence up to a cap; universal rights are voted by equal vote. The rules of admission, weight and minimum base are those of TEPS 1.5 [12]. Public registers record who is summoned and who votes, never the content of the vote.
Author: LORENZETTO, Cristian
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225742249
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