Abstract
According to probabilism, uncertain conditionals are to be reconstructed as assertions of high conditional probability. In everyday life one often encounters situations of ‘exception’. In these situations two uncertain conditionals have contradicting consequents and both of their antecedents are instantiated or true, respectively. The often cited example of this sort is ‘Tweety’, who happens to be both a bird and a penguin. We believe that if Tweety is a bird then it probably can fly, and if it is a penguin then it probably cannot fly. If one reconstructs these examples by only one probability function, as is required by strong Bayesianism, they come out as probabilistically incoherent (with or without the existence of a specificity relation between the two antecedents). This result is counterintuitive. I argue that if one intends a coherent reconstruction, one has to distinguish between two probability functions, evidential probabilities which are subjective, and objective probabilities which are backed up by statistical probabilities. Drawing on Hawthorne (2005) I give further reasons why probabilism and even Bayesianism needs this distinction. In the end of the paper I present results of an experimental study on examples of ‘exception’ which confirm that humans operate with these two distinct probability concepts.
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References
Jonathan Bennett (2003), A Philosophical Guide to Conditionals, Oxford: Clarendon Press.
Gerhard Brewka (1991a), Nonmonotonic Reasoning. Logical Foundations of Common Sense. Cambridge: Cambridge University Press.
Gerhard Brewka (1991b), “Cumulative Default Logic”, in: Artificial Intelligence 50, pp. 183–205.
Rudolf Carnap (1950), Logical Foundations of Probability, Chicago: The University of Chicago Press.
Rudolf Carnap (1971), “Inductive Logic and Rational Decisions”, in: Rudolf Carnap and Richard Jeffrey (Eds.), Studies in Inductive Logic and Probability I. Los Angeles: Univ. of Calif. Press.
John Earman (1992), Bayes or Bust? Cambridge/Mass.: MIT Press.
Ward Edwards, Harold Lindman and Leonard J. Savage (1963), “Bayesian Statistical Inference for Psychological Research”, in: Psychological Review 70, pp. 193–242.
Jonathan St. Evans, Simon, J. Handley and David E. Over (2003), “Conditionals and Conditional Probability. Journal of Experimental Psychology: Learning. Memory, and Cognition 29/2, pp. 321–335.
Dov M. Gabbay et al. (Eds., 1994), Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3: Nonmonotonic Reasoning and Uncertain Reasoning. Oxford: Clarendon Press.
Moises Goldszmidt and Judea Pearl (1996), “Qualitative Probabilities for Default Reasoning, Belief Revision and Causal Modeling”, in: Artificial Intelligence, 84, pp. 57–112.
Alan Hájek (2008), “Arguments for (or against) Probabilism?”, in: British Journal for the Philosophy of Science 59, pp. 793–819.
Joseph Halpern (2003), Reasoning about Uncertainty. Cambridge/Mass.: MIT Press.
James Hawthorne (2005), “Degree-of-Belief and Degree-of-Support: Why Bayesians Need Both Notions”, in: Mind 114, pp. 277–320.
Collin Howson and Peter Urbach (1993), Scientific Reasoning: The Bayesian Approach. Chicago: Open Court (2nd ed.).
James M. Joyce (1998), “A Nonpragmatic Vindication of Probabilism”, Philosophy of Science 65/4, pp. 575–603.
Franz von Kutschera (1972), Wissenschaftstheorie, Bd. I und II. München: W. Fink.
David Lewis (1976), “Probabilities of Conditionals and Conditional Probabilities”, in: The Philosophical Review 85, pp. 297–315.
Vann MacGee (1989): “Conditional Probabilities and Compounds of Conditionals”, in: The Philosophical Review 98, pp. 485–541.
Mike Oaksford and Nick Chater (2007), Bayesian Rationality. The Probabilistic Approach to Human Reasoning, Oxford: Oxford Univ. Press.
Klaus Oberauer and Oliver Wilhelm (2003), “The Meaning(s) of Conditionals: Conditional Probabilities, Mental Models, and Personal Utilities”, in: Journal of Experimental Psychology: Learning. Memory, and Cognition 29/4, pp. 680–693.
Judea Pearl (1988), Probabilistic Reasoning in Intelligent Systems. Santa Mateo: Morgan Kaufmann.
Niki Pfeifer and Gernot Kleiter (2008), “The Conditional in Mental Probability Logic”, in: Mike Oaksford (Ed.), The Psychology of Conditionals. Oxford: Oxford University Press.
John Pollock (1994), “Justification and Defeat”, in: Artificial Intelligence 67, pp. 377–407.
Hans Reichenbach (1949), The Theory of Probability. Berkeley: University of California Press.
Raymond Reiter (1980), “A Logic for Default Reasoning”, in: Artificial Intelligence 13, pp. 81–132.
Gerhard Schurz (2005), “Non-monotonic Reasoning from an Evolutionary Viewpoint”, in: Synthese 146/1-2, pp. 37–51.
Gerhard Schurz (2007), “Human Conditional Reasoning Explained by Non- Monotonicity and Probability: An Evolutionary Account”, in: Stella Vosniadou et al. (Eds.), Proceedings of EuroCogSci07. The European Cognitive Science Conference 2007, Lawrence Erlbaum Assoc., New York, 2007, pp. 628–633.
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Schurz, G. (2012). Tweety, or Why Probabilism and even Bayesianism Need Objective and Evidential Probabilities. In: Dieks, D., Gonzalez, W., Hartmann, S., Stöltzner, M., Weber, M. (eds) Probabilities, Laws, and Structures. The Philosophy of Science in a European Perspective, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-3030-4_5
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DOI: https://doi.org/10.1007/978-94-007-3030-4_5
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