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Abstract
In this article, we develop an agent-based model of a classic binomial decision problem, including players based on variations of Evidential Probability and Objective Bayesianism. We compare the performances of these players, including against a benchmark player who uses standard Bayesian inductive logic. We find that the calibrated players can match the performance of the Bayesian player, but only with particular acceptance thresholds and decision rules. Among other points, our discussion raises some challenges for characterising “cautious” reasoning using imprecise probabilities. Thus, we demonstrate a new way of systematically comparing imprecise probability systems, and we conclude that calibration inductive logics are surprisingly promising for making decisions.
Original language | English |
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Article number | 109030 |
Number of pages | 37 |
Journal | International Journal of Approximate Reasoning |
Volume | 162 |
Early online date | 19 Sept 2023 |
DOIs | |
Publication status | Published - Nov 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Author(s)
Funding
We thank Daniele Tortoli (University of Modena and Reggio Emilia, Italy) for his very valuable support in accelerating computations in our research. The work described in this article was partly supported by a Senior Research Fellowship award from the Research Grants Council of the Hong Kong SAR, China (“Philosophy of Contemporary and Future Science”, Project no. SRFS2122-3H01), German Research Foundation project SP 279/21-1 (project no. 420094936), and the University of Modena and Reggio Emilia, Italy. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this article. The Authors are grateful to the University of Modena and Reggio Emilia for providing open access for this article.
Keywords
- Agent-based modelling
- Imprecise probability
- Machine learning
- Decision under uncertainty
- Frequentist statistics
- Objective Bayesianism
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Dive into the research topics of 'Making decisions with evidential probability and objective Bayesian calibration inductive logics'. Together they form a unique fingerprint.Projects
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Philosophy of Contemporary and Future Science
ROWBOTTOM, D. P. (PI)
Research Grants Council (HKSAR)
1/01/22 → 30/06/27
Project: Grant Research