Recognizing and Eliciting Weakly Single Crossing Profiles on Trees
This work addresses computational challenges in social choice theory for researchers and practitioners, though it appears to be an incremental extension of existing single-crossing domain concepts.
The paper tackles the problem of recognizing and eliciting weakly single-crossing preference profiles on trees in social choice theory, introducing a polynomial-time recognition algorithm and an efficient sequential elicitation algorithm with matching lower bounds on query complexity.
We introduce and study the weakly single-crossing domain on trees which is a generalization of the well-studied single-crossing domain in social choice theory. We design a polynomial-time algorithm for recognizing preference profiles which belong to this domain. We then develop an efficient elicitation algorithm for this domain which works even if the preferences can be accessed only sequentially and the underlying single-crossing tree structure is not known beforehand. We also prove matching lower bound on the query complexity of our elicitation algorithm when the number of voters is large compared to the number of candidates. We also prove a lower bound of $Ω(m^2\log n)$ on the number of queries that any algorithm needs to ask to elicit single crossing profile when random queries are allowed. This resolves an open question in an earlier paper and proves optimality of their preference elicitation algorithm when random queries are allowed.