A Trust Incentive-Driven Consensus Feedback Mechanism for Large-Scale Participatory Budgeting

Abstract

Large-scale participatory budgeting (PB) lacks a mechanism for reconciling divergent resident preferences before project selection. Conventional consensus feedback mechanisms are difficult to deploy in this setting because anonymous ballots provide no direct basis for tailoring feedback, while resident-by-resident adjustment is computationally infeasible at population scale. We therefore develop a trust incentive-driven consensus feedback mechanism that transforms ballots into structural and behavioral signals. We apply fuzzy c-means (FCM) to identify overlapping preference groups and use the resulting memberships to construct group-mediated proxy trust, while using supported project costs to infer residents’ risk attitudes. These signals are incorporated into an extended interactive trust function (E-ITF) and a bounded consensus model that generates differentiated trust incentives and resident-specific adjustment bounds. Then, we develop the trust incentive-driven bounded consensus (TIBC) algorithm, which identifies selected inconsistent residents as a batch, determines bounded adjustment coefficients, and iteratively updates preferences, proxy trust, and resident weights. Finally, we evaluate the proposed mechanism using a real municipal PB case. TIBC algorithm reaches the common consensus target in two iterations, while maintaining subgroup consensus balance. These findings provide decision makers with a transparent and scalable way to incorporate consensus feedback into anonymous PB without requiring observed interpersonal trust data.