Assessment-Driven Judgment Aggregation Within a Group of Peers

Group decision-making sometimes involves evaluating the decision-makers themselves, e.g., selecting the best expert or assigning rewards within a team. In such cases, all participants should be involved, but their influence should reflect their competence or contribution. This article proposes two new opinion aggregation models in which a person’s assessment weight depends on their ranking, preventing low-performing members from exerting the same influence and promoting respected experts. The proposed aggregation methods uphold the principle of distributive justice by ensuring that individual contributions are proportional to the rewards they receive. In addition to formulating new methods for aggregating results, we presented several of their formal properties and indicated practical ways to calculate the results. For one of the methods, which is more challenging to compute, we conducted a Monte Carlo experiment demonstrating the practical feasibility of computing the aggregated weight vector.