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  1. Mathematical Models and Robustness Analysis in Epistemic Democracy: A Systematic Review of Diversity Trumps Ability Theorem Models.Ryota Sakai - 2020 - Philosophy of the Social Sciences 50 (3):195-214.
    This article contributes to the revision of the procedure of robustness analysis of mathematical models in epistemic democracy using the systematic review method. It identifies the drawbacks of robustness analysis in epistemic democracy in terms of sample universality and inference from samples with the same results. To exemplify the effectiveness of systematic review, this article conducted a pilot review of diversity trumps ability theorem models, which are mathematical models of deliberation often cited by epistemic democrats. A review of nine models (...)
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  • Diversity, Ability, and Expertise in Epistemic Communities.Patrick Grim, Daniel J. Singer, Aaron Bramson, Bennett Holman, Sean McGeehan & William J. Berger - 2019 - Philosophy of Science 86 (1):98-123.
    The Hong and Page ‘diversity trumps ability’ result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page’s model to landscapes of different degrees of randomness and demonstrate the sensitivity of the ‘diversity trumps ability’ result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive (...)
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  • Citizens and collective deliberation in social science.Leandro de Brasi - 2020 - Manuscrito 43 (3):72-113.
    ABSTRACT It is argued that, in certain particular conditions related to the intellectual character of the deliberators and their cognitive diversity, small research teams that engage in deliberation in the analysis of data and involve citizens can better promote good epistemic results than those teams which do not involve citizens. In particular, it is argued that certain communities within the social sciences that lack the relevant cognitive diversity among their professionals can take advantage of the diversity found in the citizenry (...)
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  • Modeling Epistemology: Examples and Analysis in Computational Philosophy of Science.Patrick Grim - 2019 - In A. Del Barrio, C. J. Lynch, F. J. Barros & X. Hu (eds.), IEEE SpringSim Proceedings 2019. IEEE. pp. 1-12.
    What structure of scientific communication and cooperation, between what kinds of investigators, is best positioned to lead us to the truth? Against an outline of standard philosophical characteristics and a recent turn to social epistemology, this paper surveys highlights within two strands of computational philosophy of science that attempt to work toward an answer to this question. Both strands emerge from abstract rational choice theory and the analytic tradition in philosophy of science rather than postmodern sociology of science. The first (...)
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  • Diversity and Democracy: Agent-Based Modeling in Political Philosophy.Bennett Holman, William Berger, Daniel J. Singer, Patrick Grim & Aaron Bramson - 2018 - Historical Social Research 43:259-284.
    Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated “diversity trumps ability” result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits (...)
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  • Wisdom of Crowds, Wisdom of the Few: Expertise versus Diversity across Epistemic Landscapes.Patrick Grim, Daniel J. Singer, Aaron Bramson, Bennett Holman, Sean McGeehan & William J. Berger - manuscript
    In a series of formal studies and less formal applications, Hong and Page offer a ‘diversity trumps ability’ result on the basis of a computational experiment accompanied by a mathematical theorem as explanatory background (Hong & Page 2004, 2009; Page 2007, 2011). “[W]e find that a random collection of agents drawn from a large set of limited-ability agents typically outperforms a collection of the very best agents from that same set” (2004, p. 16386). The result has been extremely influential as (...)
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