Parallel Belief Revision via Order Aggregation
This work addresses a gap in iterated parallel belief revision for AI and logic communities, offering a principled extension that is incremental in nature.
The paper tackles the problem of extending serial iterated belief revision operators to handle parallel change, proposing a method based on TeamQueue aggregators that recovers plausible properties from the literature while avoiding dubious ones.
Despite efforts to better understand the constraints that operate on single-step parallel (aka "package", "multiple") revision, very little work has been carried out on how to extend the model to the iterated case. A recent paper by Delgrande & Jin outlines a range of relevant rationality postulates. While many of these are plausible, they lack an underlying unifying explanation. We draw on recent work on iterated parallel contraction to offer a general method for extending serial iterated belief revision operators to handle parallel change. This method, based on a family of order aggregators known as TeamQueue aggregators, provides a principled way to recover the independently plausible properties that can be found in the literature, without yielding the more dubious ones.