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Abductive Inference and Iterated Conditionals

  • Claudio Pizzi
Part of the Studies in Computational Intelligence book series (SCI, volume 64)

Summary. The first part of the paper aims to stressing the analogy between conditional inference and abductive inference, making evident that in both cases what is here called “reasonable” inference involves a choice between a finite set of incompatible conclusions, selecting the most information preserving-consequent in the case of standard conditionals and the most information-preserving antecedent in the case of abductive conditionals. The consequentialist view of conditionals which is endorsed in this perspective is then extended to cover the case of higher degree conditionals, introducing in the semantical analysis the notion of inferential agents reasoning about the activity of other inferential agents. It is then shown (i) that iterated conditionals are essential in the treatment of redundant causation (ii) that abductive conditionals are essential parts of iterated conditionals in the analysis of causal preemption (iii) that there is a widespread use of second-degree conditionals involving first degree abductive conditionals. The final section is devoted to remind that Peirce’s original notion of abductive inference was actually defined in terms of second degree conditionals.

Keywords

Inductive Logic Abductive Reasoning Conditional Logic Abductive Inference Reasonable Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Claudio Pizzi
    • 1
  1. 1.Dipartimento di Filosofia e Scienze SocialiUniversitàa di SienaSienaItaly

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