2024/11/13 by Dino Carpentras, Regula Hänggli, Dirk Helbing · 1 voice
Decision Sciences · Social Sciences · Business, Management and Accounting · #Auction Theory and Applications #Experimental Behavioral Economics Studies #FinTech, Crowdfunding, Digital Finance
paper · pdf · doi:10.1098/rsta.2024.0090
openalex publication_date 2024/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11
Currently, there are increasing attempts to better involve citizens in political decision processes. A successful approach in that regard has been participatory budgeting (PB), which allows citizens to propose projects and then decide how to distribute a given budget over them. Meanwhile, the literature on collective intelligence (CI) has also shown the ability of groups to solve complex problems. Thus, by combining CI and PB, it should be possible for citizens to identify problems and create their own solutions. In this article, we study this possibility by using agent-based models. Specifically, we first show that a system combining CI and PB produces solutions that strongly penalize minorities if the solution quality depends on group size. Then, we introduce an approach that can overcome this issue. Indeed, by using a common knowledge base for the storage of partial solutions, the quality of solutions of minorities can benefit from the work of the majority, thereby promoting fairness. Interestingly, this approach also benefits majorities, as the quality of their solutions is further improved by the work of the minorities, thus reaching better solutions for everyone. This stresses the potential and importance of an open innovation approach, which is committed to information sharing.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.