Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-03T17:19:36.598Z Has data issue: false hasContentIssue false
  • English
  • Français

DELIBERATION, JUDGEMENT AND THE NATURE OF EVIDENCE

Published online by Cambridge University Press:  19 February 2015

Jon Williamson
Jon Williamson*
Affiliation:
Philosophy Department, University of Kent, Canterbury CT2 8AX, UK. Email: [email protected]. URL: http://www.kent.ac.uk/secl/philosophy/jw/
Get access Rights & Permissions [Opens in a new window]

Abstract:

A normative Bayesian theory of deliberation and judgement requires a procedure for merging the evidence of a collection of agents. In order to provide such a procedure, one needs to ask what the evidence is that grounds Bayesian probabilities. After finding fault with several views on the nature of evidence (the views that evidence is knowledge; that evidence is whatever is fully believed; that evidence is observationally set credence; that evidence is information), it is argued that evidence is whatever is rationally taken for granted. This view is shown to have consequences for an account of merging evidence, and it is argued that standard axioms for merging need to be altered somewhat.

Keywords

DeliberationJudgementEvidenceBayesianismMerging
Type
Symposium on Individual and Social Deliberation
Information
Economics & Philosophy , Volume 31 , Issue 1 , March 2015 , pp. 27 - 65
Copyright
Copyright © Cambridge University Press 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adamcik, M. and Wilmers, G.. 2014. Probabilistic merging operators. Logique et Analyse, in press.Google Scholar
Alchourrón, C. E., Gärdenfors, P. and Makinson, D.. 1985. On the logic of theory change: partial meet functions for contraction and revision. Journal of Symbolic Logic 50: 510–530.CrossRefGoogle Scholar
Aumann, R. J. 1976. Agreeing to disagree. Annals of Statistics 4: 1326–1329.Google Scholar
Bird, A. 2007. Underdetermination and evidence. In Images of Empiricism: Essays on Science and Stances, ed. Monton, B., 62–82. Oxford: Oxford University Press.CrossRefGoogle Scholar
Bratman, M. E. 1992. Practical reasoning and acceptance in a context. Mind 101: 1–15.CrossRefGoogle Scholar
Corfield, D. 2001. Bayesianism in mathematics. In Foundations of Bayesianism, ed. Corfield, D. and Williamson, J., 175–201. Dordrecht: Kluwer.Google Scholar
Dawid, A. P. 1982. The well-calibrated Bayesian. Journal of the American Statistical Association 77: 604–613.Google Scholar
Earman, J. 1992. Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge, MA: MIT Press.Google Scholar
Foley, R. 1993. Working without a Net. New York, NY: Oxford University Press.Google Scholar
Gillies, D. 1991. Intersubjective probability and confirmation theory. British Journal for the Philosophy of Science 42: 513–533.Google Scholar
Hild, M., Jeffrey, R. and Risse, M.. 2008. Preference aggregation after Harsanyi. In Justice, Political Liberalism, and Utilitarianism: Themes from Harsanyi and Rawls, ed. Fleurbaey, M., Salles, M. and Weymark, J. A., 198–218. Cambridge: Cambridge University Press.Google Scholar
Howson, C. 1997. Logic and probability. British Journal for the Philosophy of Science 48: 517–531.CrossRefGoogle Scholar
Hylland, A. and Zeckhauser, R.. 1979. The impossibility of Bayesian group decision making with separate aggregation of beliefs and values. Econometrica 47: 1321–1336.Google Scholar
Jaynes, E. T. 1957. Information theory and statistical mechanics. The Physical Review 106: 620–630.CrossRefGoogle Scholar
Jeffrey, R. 1968 [1982]. Probable knowledge. In Probability and the Art of Judgement, 30–43. Cambridge: Cambridge University Press.Google Scholar
Jeffrey, R. 2004. Subjective Probability: The Real Thing. Cambridge: Cambridge University Press.Google Scholar
Kern-Isberner, G. and Rödder, W.. 2004. Belief revision and information fusion on optimum entropy. International Journal of Intelligent Systems 19: 837–857.Google Scholar
Keynes, J. M. 1921 [1973]. A Treatise on Probability. London: Macmillan.Google Scholar
Konieczny, S. and Pino Pérez, R.. 1998. On the logic of merging. In Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning, pages 488–498, Trento. Morgan Kaufmann.Google Scholar
Konieczny, S. and Pino Pérez, R.. 2011. Logic based merging. Journal of Philosophical Logic 40: 239–270.Google Scholar
Kyburg, H. E. Jr 1991. Evidential probability. In Proceedings of the 12th International Joint Conference on Artificial Intelligence, ed. Mylopoulos, J. and Reiter, R., 1196–1203. Sydney: Morgan Kaufmann.Google Scholar
Lewis, C. I. 1946. An Analysis of Knowledge and Valuation. La Salle, IL: Open Court.Google Scholar
Lin, H. and Kelly, K. T.. 2012. Propositional reasoning that tracks probabilistic reasoning. Journal of Philosophical Logic, 41: 957–981.CrossRefGoogle Scholar
List, C. 2012. The theory of judgment aggregation: an introductory review. Synthese, 187: 179–207.Google Scholar
Littlejohn, C. 2011. Evidence and knowledge. Erkenntnis 74: 241–262.Google Scholar
Milne, P. 1991. A dilemma for subjective Bayesians and how to resolve it. Philosophical Studies 62: 307–314.Google Scholar
Mongin, P. 2005. Spurious Unanimity and the Pareto Principle. LSE Choice Group working paper series, vol. 1, no. 5. London: Centre for Philosophy of Natural and Social Science (CPNSS), London School of Economics.Google Scholar
Quine, W. V. O. and Ullian, J. S.. 1970. The Web of Belief. New York, NY: Random House.Google Scholar
Rowbottom, D. P. 2014. Information versus knowledge in confirmation theory. Logique et Analyse 57: 137–149.Google Scholar
Shoham, Y. 1987. A semantical approach to nonmonotonic logics. In Readings in Nonmonotonic Reasoning, ed. Ginsberg, M. L., 227–250. San Francisco, CA: Morgan Kaufmann.Google Scholar
Skorupski, J. 2010. The Domain of Reasons. Oxford: Oxford University Press.Google Scholar
Williamson, J. 2009. Aggregating judgements by merging evidence. Journal of Logic and Computation 19: 461–473.Google Scholar
Williamson, J. 2010. In defence of objective Bayesianism. Oxford: Oxford University Press.Google Scholar
Williamson, J. 2011. Objective Bayesianism, Bayesian conditionalisation and voluntarism. Synthese 178: 67–85.Google Scholar
Williamson, J. 2013. From Bayesian epistemology to inductive logic. Journal of Applied Logic 11: 468–486.Google Scholar
Williamson, T. 2000. Knowledge and its Limits. Oxford: Oxford University Press.Google Scholar
Williamson, T. 2007. The Philosophy of Philosophy. Oxford: Blackwell.CrossRefGoogle Scholar
Wilmers, G. 2010. The social entropy process: axiomatising the aggregation of probabilistic beliefs. In Probability, Uncertainty and Rationality, ed. Hosni, H. and Montagna, F., 87–104. Pisa: Edizioni della Normale.Google Scholar
Wittgenstein, L. 1969. On Certainty, ed. Anscombe, G. E. M. and von Wright, G. H., trans. D. Paul and G.E.M. Anscombe. New York, NY: Harper & Row.Google Scholar