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  1.  10
    Artificial Intelligence Methods for a Bayesian Epistemology‐Powered Evidence Evaluation.Francesco De Pretis, Jürgen Landes & William Peden - 2021 - Journal of Evaluation in Clinical Practice 27 (3):504-512.
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  2.  19
    Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review.Mantas Radzvilas, Francesco De Pretis, William Peden, Daniele Tortoli & Barbara Osimani - forthcoming - Computational Economics:1-44.
    Contemporary debates about scientific institutions and practice feature many proposed reforms. Most of these require increased efforts from scientists. But how do scientists’ incentives for effort interact? How can scientific institutions encourage scientists to invest effort in research? We explore these questions using a game-theoretic model of publication markets. We employ a base game between authors and reviewers, before assessing some of its tendencies by means of analysis and simulations. We compare how the effort expenditures of these groups interact in (...)
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    A Battle in the Statistics Wars: A Simulation-Based Comparison of Bayesian, Frequentist and Williamsonian Methodologies.Mantas Radzvilas, William Peden & Francesco De Pretis - 2021 - Synthese 199 (5-6):13689-13748.
    The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...)
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    Pharmacovigilance as Personalized Evidence.Francesco De Pretis, William Peden, Jürgen Landes & Barbara Osimani - 2022 - In Chiara Beneduce & Marta Bertolaso (eds.), Personalized Medicine in the Making. Springer. pp. 147-171.
    Personalized medicine relies on two points: 1) causal knowledge about the possible effects of X in a given statistical population; 2) assignment of the given individual to a suitable reference class. Regarding point 1, standard approaches to causal inference are generally considered to be characterized by a trade-off between how confidently one can establish causality in any given study (internal validity) and extrapolating such knowledge to specific target groups (external validity). Regarding point 2, it is uncertain which reference class leads (...)
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