2022
DOI: 10.48550/arxiv.2201.08247
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Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation

Abstract: The outcome of a collective decision-making process, such as crowdsourcing, often relies on the procedure through which the perspectives of its individual members are aggregated. Popular aggregation methods, such as the majority rule, often fail to produce the optimal result, especially in high-complexity tasks. Methods that rely on meta-cognitive information, such as confidence-based methods and the Surprisingly Popular Option, had shown an improvement in various tasks. However, there is still a significant n… Show more

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