## Abstract

How should an agent revise her epistemic state in the light of doxastic disagreement? The problems associated with answering this question arise under the assumption that an agent’s epistemic state is best represented by her degree of belief function alone. We argue that for modeling cases of doxastic disagreement an agent’s epistemic state is best represented by her confirmation commitments and the evidence available to her. Finally, we argue that given this position it is possible to provide an adequate answer to the question of how to rationally revise one’s epistemic state in the light of disagreement.

## Keywords

Bayesian epistemology epistemic disagreement probability aggregation social epistemology## Notes

### Acknowledgments

Early versions of this paper have been presented at conferences, respectively workshops, in Bochum (*Recent Debates in Epistemology*), Lund (*CPH LU Workshop on Social Epistemology*), and Salzburg (*SOPhia 2013*) and at the Tilburg Center for Philosophy of Science. We thank the audience for their insightful comments on various versions of the paper. We are also grateful to the MCMP (Munich Center for Mathematical Philosophy) reading-group on social epistemology for fruitful discussion on Jehle and Fitelson’s paper. Special thanks go to Lorenzo Casini, Stephan Hartmann, Albert Newen, Carlo Proietti, Gerhard Schurz, Jan Sprenger, and Frank Zenker. We would also like to thank two anonymous referees for very helpful commentaries on an earlier version of this paper. Anna-Maria A. Eder’s research on this paper was partly funded by a fellowship (Stipendium nach dem Landesgraduiertenförderungsgesetz) sponsored by the State of Baden-Württemberg (Germany). Peter Brössel’s research was supported by a Visiting Fellowship by the Tilburg Center for Philosophy of Science.

## References

- Abbas, A. (2009). A Kullback-Leibler view of linear and log-linear pools.
*Decision Analysis*,*6*, 25–37.CrossRefGoogle Scholar - Allard, D., Comunian, A., & Renard, P. (2012). Probability aggregation methods in geoscience.
*Mathematical Geosciences*,*44*, 545–81.Google Scholar - Brössel, P. (2012).
*Rethinking Bayesian confirmation theory-steps towards a new theory of confirmation*. PhD-dissertation, University of Konstanz.Google Scholar - Christensen, D. (2009). Disagreement as evidence: The epistemology of controversy.
*Philosophy Compass*,*4*, 756–767.CrossRefGoogle Scholar - Genest, C., McConway, K., & Schervish, M. (1986). Characterization of externally Bayesian pooling operators.
*The Annals of Statistics*,*14*, 487–501.CrossRefGoogle Scholar - Genest, C., & Wagner, C. (1987). Further evidence against independence preservation in expert judgement synthesis.
*Aequationes Mathematicae*,*32*, 74–86.CrossRefGoogle Scholar - Genest, C., & Zidek, J. (1986). Combining probability distributions: A critique and annotated bibliography.
*Statistical Science*,*1*, 114–135.CrossRefGoogle Scholar - Hájek, A. (2011). Interpretations of probability. In E.N. Zalta (Ed.),
*Stanford encyclopedia of philosophy*.Google Scholar - Jeffrey, R. (1987). Indefinite probability judgment.
*Philosophy of Science*,*54*, 586–591.CrossRefGoogle Scholar - Jehle, D., & Fitelson, B. (2009). What is the “equal weight view”?
*Episteme*,*6*, 280–293.CrossRefGoogle Scholar - Konieczny, S., & Pino Pérez, R. (2011). Logic based merging.
*Journal of Philosophical Logic*,*40*, 239–270.CrossRefGoogle Scholar - Lange, M. (1999). Calibration and the epistemological role of Bayesian conditionalization.
*The Journal of Philosophy*,*96*, 294–324.CrossRefGoogle Scholar - Lehrer, K., & Wagner, C. (1983). Probability amalgamation and the independence issue: A reply to Laddaga.
*Synthese*,*55*, 339–346.CrossRefGoogle Scholar - Levi, I. (1980).
*The enterprise of knowledge*. Cambridge: MIT Press.Google Scholar - McConway, K. (1981). Marginalization and linear opinion pools.
*Journal of the American Statistical Association*,*76*, 410–414.CrossRefGoogle Scholar - Raiffa, H. (1968).
*Decision analysis: Introductory lectures on choices under uncertainty*. Reading: Addison-Wesley.Google Scholar - Schurz, G. (2012). Tweety, or why probabilism and even Bayesianism need objective and evidential probabilities. In D. Dieks et al. (Eds.),
*Probabilities, laws and structures*(pp. 57–74). New York:Springer.Google Scholar - Unterhuber, M., & Schurz, G. (2013). The new tweety puzzle: Arguments against Monistic Bayesian approaches in epistemology and cognitive science.
*Synthese*,*190*, 1407–1435.CrossRefGoogle Scholar - Wagner, C. (1982). Allocation, Lehrer models, and the consensus of probabilities.
*Theory and Decision*,*14*, 207–220.CrossRefGoogle Scholar - Wagner, C. (2010). Jeffrey conditioning and external Bayesianity.
*Logic Journal of the IGPL*,*18*, 336–345.CrossRefGoogle Scholar - Williamson, J. (2010).
*In defence of objective Bayesianism*. Oxford: Oxford University Press.CrossRefGoogle Scholar