Propositional knowledge base revision and minimal change
Artificial Intelligence
Social choice axioms for fuzzy set aggregation
Fuzzy Sets and Systems - Special issue: Aggregation and best choices of imprecise opinions
Handbook of logic in artificial intelligence and logic programming (Vol. 4)
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
A Practical Approach to Fusing Prioritized Knowledge Bases
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Social Choice, Merging, and Elections
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Merging with Integrity Constraints
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Logic-based approaches to information fusion
Information Fusion
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
The strategy-proofness landscape of merging
Journal of Artificial Intelligence Research
A logic-based axiomatic model of bargaining
Artificial Intelligence
Review: logical mechanism design
The Knowledge Engineering Review
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Quota-Based merging operators for stratified knowledge bases
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Some representation and computational issues in social choice
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Complexity of judgment aggregation
Journal of Artificial Intelligence Research
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Intelligent agents have to be able to merge informational inputs received from different sources in a coherent and rational way. Several proposals have been made for information merging in which it is possible to encode the preferences of sources [5,4,19,24,25,1]. Information merging has much in common with social choice theory, which aims to define operations reflecting the preferences of a society from the individual preferences of the members of the society. Given this connection, frameworks for information merging should provide satisfactory resolutions of problems raised in social choice theory. We investigate the link between the merging of epistemic states and some results in social choice theory. This is achieved by providing a consistent set of properties-akin to those used in Arrow's theorem [2]-for merging. It is shown that in this framework there is no Arrow-like impossibility result. By extending this to a consistent framework which includes properties corresponding to the notion of being strategy-proof, we show that results due to Gibbard and Satterthwaite [13,31,32] and others [6,3] do not hold in merging frameworks.