Laura is a student at Deakin University in Melbourne. She completed her Bachelor of Science with a major in mathematics this year and is looking forward to doing her Honours in mathematics in 2015. Laura became interested in pursuing a career in mathematical research while completing a short faculty funded summer project in 2013/2014.
Research interests include aggregation functions, fuzzy logic and optimization.
The impact of biased experts in the aggregation of fuzzy preference relations
Current models for the aggregation of fuzzy preference relations(FPRs) usually contain a consensus model which focuses on achieving some level of agreement amongst experts before the FPRs are aggregated and an alternative is chosen. These consensus models look at the distances between the experts’ preferences and provide each expert with a consensus level. If an expert’s consensus level is below a predefined threshold (usually around 0.8), the expert will be given recommendations to update certain preferences until they receive a consensus level that is considered acceptable when compared to the group. We will look at situations where the current methods for calculating the consensus of FPRs can be exploited by a biased expert and then explore some of the ways to reduce the impact of such situations.