Trivalent Conditionals: Stalnaker's Thesis and Bayesian Inference


This paper develops a trivalent semantics for indicative conditionals and extends it to a probabilistic theory of valid inference and inductive learning with conditionals. On this account, (i) all complex conditionals can be rephrased as simple conditionals, connecting our account to Adams's theory of p-valid inference; (ii) we obtain Stalnaker's Thesis as a theorem while avoiding the well-known triviality results; (iii) we generalize Bayesian conditionalization to an updating principle for conditional sentences. The final result is a unified semantic and probabilistic theory of conditionals with attractive results and predictions.

Author Profiles

Paul Egré
École Normale Supérieure
Jan Sprenger
University of Turin
Lorenzo Rossi
Università di Torino


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