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Meta-Abduction: Inference to the Probabilistically Best Prediction

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Part of the Philosophical Studies Series book series (PSSP,volume 143)

Abstract

In this paper, a taxonomy of abduction in a wide sense and an exact characterisation of probabilistic selective abduction is given. Two important features of such inferences, namely accuracy and simplicity of explanations and predictions, are described in detail. Afterwards, the epistemic merits of simplicity are discussed. They are used to justify probabilistic selective abduction in terms of an inference to the probabilistically best explanation. By help of the theory of meta-induction and its account of induction, this justification is expanded to abduction in terms of an inference to the probabilistically best prediction. This expansion of the theory of meta-induction to a theory of meta-abduction indicates that the framework fruitfully accounts for important inference methods of science.

Keywords

  • Abduction
  • Simplicity
  • Meta-induction
  • Justification of induction
  • Cognitive costs

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Acknowledgements

This research was supported by the German Research Foundation (DFG), research unit Inductive Metaphysics FOR 2495. I would like to thank two anonymous referees for very helpful comments on an earlier version of this paper. For valuable discussion of this topic, I would like to thank Atocha Aliseda and the audience of IACAP 2019 (annual meeting of the International Association for Computing and Philosophy, June 5–7, 2019, Mexico City). Thanks a lot also to Nancy Abigail Nuñez Hernandez and Björn Lundgren for organising the proceedings to this conference.

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Correspondence to Christian J. Feldbacher-Escamilla .

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Feldbacher-Escamilla, C.J. (2022). Meta-Abduction: Inference to the Probabilistically Best Prediction. In: Lundgren, B., Nuñez Hernández, N.A. (eds) Philosophy of Computing. Philosophical Studies Series, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-030-75267-5_2

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