Group decision-making is ubiquitous in human society, aiming to pool individual wisdom to address complex problems. Traditional group decision-making prioritizes achieving group consensus, that is seeking a compromise solution acceptable to all decision-makers. However, in predictive and judgmental tasks with objective ground truths, such as expert forecasting and risk assessment, the goal of group decision-making should shift from orchestrating agreement to identifying the single optimal decision that best corresponds to reality. This paper proposes a non-consensus aggregation algorithm that minimizes the correlation between decision-maker weights and decision deviations, thereby guiding aggregated results toward the optimal decision. Theoretically, the method is proved to provide the maximum likelihood estimate of the optimal decision. Extensive experiments on multi-problem decision-maker judgment datasets demonstrate that the proposed method significantly reduces decision errors compared to conventional aggregation methods, particularly for difficult or large-scale problems. This work offers a new and effective path for group decision-making in accuracy-oriented scenarios.
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