Proportional Belief Merging


  • Adrian Haret TU Wien
  • Martin Lackner TU Wien
  • Andreas Pfandler TU Wien
  • Johannes P. Wallner TU Wien



In this paper we introduce proportionality to belief merging. Belief merging is a framework for aggregating information presented in the form of propositional formulas, and it generalizes many aggregation models in social choice. In our analysis, two incompatible notions of proportionality emerge: one similar to standard notions of proportionality in social choice, the other more in tune with the logic-based merging setting. Since established merging operators meet neither of these proportionality requirements, we design new proportional belief merging operators. We analyze the proposed operators against established rationality postulates, finding that current approaches to proportionality from the field of social choice are, at their core, incompatible with standard rationality postulates in belief merging. We provide characterization results that explain the underlying conflict, and provide a complexity analysis of our novel operators.




How to Cite

Haret, A., Lackner, M., Pfandler, A., & Wallner, J. P. (2020). Proportional Belief Merging. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2822-2829.



AAAI Technical Track: Knowledge Representation and Reasoning