Duijf, Hein (2025) Diversity and expertise in binary classification problems. [Preprint]

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Abstract

Democratic theorists and social epistemologists often celebrate the epistemic benefits of diversity. One of the cornerstones is the ‘diversity trumps ability’ result by Hong and Page (2004). Ironically, the interplay between diversity and ability is rarely studied in radically different frameworks. Although diversity has been studied in prediction and search problems, the diversity-expertise tradeoff has not been studied systematically for small, deliberative groups facing binary classification problems. To fill this gap, I will introduce a new evidential sources framework and study whether, when, and (if so) why diversity trumps expertise in binary classification problems.

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Item Type:	Preprint
Creators:	Creators Email ORCID Duijf, Hein duijf.hein@gmail.com 0000-0001-6936-306X
Keywords:	Diversity Expertise Deliberation Agent-based models
Subjects:	General Issues > Computer Simulation General Issues > Social Epistemology of Science
Depositing User:	Dr. Hein Duijf
Date Deposited:	04 Sep 2025 10:42
Last Modified:	04 Sep 2025 10:42
Item ID:	26428
Subjects:	General Issues > Computer Simulation General Issues > Social Epistemology of Science
Date:	12 August 2025
URI:	https://philsci-archive.pitt.edu/id/eprint/26428

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