Synthese

, Volume 193, Issue 7, pp 2013–2034 | Cite as

Belief merging with the aim of truthlikeness

Article
  • 154 Downloads

Abstract

The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness.

Keywords

Belief merging Information fusion Truthlikeness  Judgment aggregation 

References

  1. Cevolani, G. (2014). Truth approximation, belief merging, and peer disagreement. Synthese, 191(11), 2383–2401.CrossRefGoogle Scholar
  2. Cevolani, G., & Calandra, F. (2010). Approaching the truth via belief change in propositional languages. EPSA Epistemology and Methodology of Science, 1, 47–62.Google Scholar
  3. Cevolani, G., Crupi, V., & Festa, R. (2011). Verisimilitude and belief change for conjunctive theories. Erkenntnis, 75(2), 183–202.CrossRefGoogle Scholar
  4. Clemen, R. T., & Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19(2), 187–203.Google Scholar
  5. Eckert, D., & Pigozzi, G. (2005). Belief merging, judgment aggregation, and some links with social choice theory. In: In Belief change in rational agents: Perspectives from artificial intelligence, philosophy, and economics, Dagstuhl Seminar Proceedings 05321.Google Scholar
  6. Everaere, P., Konieczny, S., & Marquis, P. (2010). The epistemic view of belief merging: Can we track the truth? In H. Coelho, R. Studer, & M. Wooldridge (Eds.), Proceedings of ECAI 2010—19th European conference on artificial intelligence, Lisbon, Portugal, August 16–20, 2010 (pp. 621–626). Amsterdam: IOS Press.Google Scholar
  7. Grégoire, E., & Konieczny, S. (2006). Logic-based approaches to information fusion. Information Fusion, 7, 4–18.CrossRefGoogle Scholar
  8. Konieczny, S., & Pérez, R. P. (1998). On the logic of merging. In Proceedings of the sixth international conference on principles of knowledge representation and reasoning (KR’98) (pp. 488–498). Morgan Kaufmann Publisher.Google Scholar
  9. Konieczny, S., & Pérez, R. P. (2002). Merging information under constraints: A logical framework. Journal of Logic and Computation, 12(5), 773–808.CrossRefGoogle Scholar
  10. Konieczny, S., & Pérez, R. P. (2011). Logic based merging. Journal of Philosophical Logic, 40(2), 239–270.CrossRefGoogle Scholar
  11. Machina, M. J. (2008). Non-expected utility theory. In S. N. Durlauf & L. E. Blume (Eds.), The New Palgrave dictionary of economics. Basingstoke: Palgrave Macmillan.Google Scholar
  12. Niiniluoto, I. (1987). Truthlikeness. Dordrecht: D. Reidel.CrossRefGoogle Scholar
  13. Niiniluoto, I. (1998). Verisimilitude: The third period. British Journal for the Philosophy of Science, 49(1), 1–29.CrossRefGoogle Scholar
  14. Niiniluoto, I. (2011). Revising beliefs towards the truth. Erkenntnis, 75(2), 165–181.CrossRefGoogle Scholar
  15. Oddie, G. (1986). Likeness to truth. Dordrecht: D. Reidel.CrossRefGoogle Scholar
  16. Oddie, G. (2013). The content, consequence and likeness approaches to verisimilitude: Compatibility, trivialization, and underdetermination. Synthese, 190(9), 1647–1687.CrossRefGoogle Scholar
  17. Oddie, G. (2014). Truthlikeness. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy, summer 2014 edn. Stanford University. http://plato.stanford.edu/archives/sum2014/entries/truthlikeness/
  18. Pigozzi, G., & Hartmann, S. (2007). Judgment aggregation and the problem of truth-tracking. In Proceedings of the 11th conference on theoretical aspects of rationality and knowledge, TARK ’07 (pp. 248–252). New York: ACM.Google Scholar
  19. Schmidt, U. (2004). Alternatives to expected utility: Formal theories. In S. Barber, P. Hammond, & C. Seidl (Eds.), Handbook of utility theory (pp. 757–837). Berlin: Springer.CrossRefGoogle Scholar
  20. Schurz, G. (2011). Verisimilitude and belief revision. With a focus on the relevant element account. Erkenntnis, 75(2), 203–221.CrossRefGoogle Scholar
  21. Williamson, J. (2009). Aggregating judgements by merging evidence. Journal of Logic and Computation, 19(3), 461–473.CrossRefGoogle Scholar
  22. Zwart, S. (2001). Refined verisimilitude. Dordrecht: Kluwer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.MelbourneAustralia

Personalised recommendations