Metawisdom of the Crowd: Experimental Evidence of Crowd Accuracy Through Diverse Choices of Decision Aids
Jon Atwell, Marlon Twyman II

TL;DR
This paper demonstrates that diverse choices of decision aids by individuals can enhance crowd accuracy, highlighting the importance of aid diversity over the use of the most accurate aids in collective decision-making.
Contribution
It introduces the concept of metawisdom, showing how aid diversity leads to better crowd accuracy, supported by a theoretical model and three empirical studies.
Findings
Diverse decision aids improve crowd accuracy.
Aid bias and variance influence collective judgment.
Optimal aid distribution enhances crowd wisdom.
Abstract
The provision of information can improve individual judgments but also fail to make group decisions more accurate; if individuals choose to attend to the same information in the same manner, the predictive diversity that enables crowd wisdom may be lost. Decision support systems, from search engines to business intelligence platforms, present individuals with decision aids -- relevant information, interpretative frames, or heuristics -- to enhance the quality and speed of decision-making but potentially influence judgments through the selective presentation of information and interpretative frames. We describe decision-making as often containing two decisions: the choice of decision aids followed by the primary decision, and define \textit{metawisdom of the crowd} as any pattern by which individuals' choice of aids leads to higher crowd accuracy than equal assignment to the same aids, a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsForecasting Techniques and Applications · Decision-Making and Behavioral Economics
Methodsfail · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
