We address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the extent of disagreement with the respective inputs. Specifically, the problems we address are rank aggregation, the feedback arc set problem on tournaments, and correlation and consensus clustering. We show that for all these problems (and various weighted versions of them), we can obtain improved approximation factors using essentially the same remarkably simple algorithm. Additionally, we almost settle a long-standing conjecture of Bang-Jensen and Thomassen and show that unless NP⊆BPP, there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments.

Aggregating inconsistent information: Ranking and clustering

https://elischolar.library.yale.edu/cgi/viewcontent.cgi?article=1175&context=egcenter-discussion-paper-series

Rural-urban migration, urban unemployment and underemployment, and job-search activity in LDCs.

Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members' weightages

Decision science or decision-aid science?

A challenging problem is to identify the most central agents in interconnected multilayer networks. Here, De Domenico et al. present a mathematical framework to calculate centrality in such networks—versatility—and rank nodes accordingly.

/pdf/ranking-in-interconnected-multilayer-networks-reveals-57fixcdvtj.pdf

Ranking in interconnected multilayer networks reveals versatile nodes

(1963). Mathematical Models in the Social Sciences. Technometrics: Vol. 5, No. 2, pp. 288-288.

Mathematical Models in the Social Sciences

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Mathematical Models in the Social Sciences