We have been fortunate enough to have the following papers accepted. The first, which was accepted to the Journal of Applied Soft Computing, is a contribution that we were invited to work on after presenting some work at FUZZIEEE in Beijing (A special session run by Paco Chiclana, Enrique Herrera-Viedma, Jian Wu and Yucheng Dong. I think there are a lot of interesting problems in this area – although we focus on some different aspects rather than the entire process. The second paper was accepted to Information Sciences and is based on collaborative research with the Environmental Ecology group at Deakin University. A nice surprise in this paper was a real dataset which was genuinely suited to the Bonferroni mean.
Title: Unifying approaches to consensus across different preference representations
Authors: Gleb Beliakov, Simon James
Consensus measures can be useful in group decision making problems both to guide users toward more reasonable judgements and to give an overall indication of the support for the final decision. The level of consensus between decision makers can be measured in contexts where preferences over alternatives are expressed either as evaluations or scores, pairwise preferences, and weak orders, however these different representations often call for different approaches to consensus measurements. In this paper, we look at the distance metrics used to construct consensus measures in each of these settings and how consistent these are for preference profiles when they are converted from one representation to another. We develop some methods for consistent approaches across decision making settings and provide an example to help investigate differences between some of the commonly used distances.
Title: Using aggregation functions to model human judgements of species diversity
Authors: Gleb Beliakov, Simon James and Dale G. Nimmo
In environmental ecology, diversity indices attempt to capture both the number of species in a community and the relative abundance of each. Many indices have been proposed for quantifying diversity, often based on calculations of dominance, equity and entropy from other research fields. Here we use linear fitting techniques to investigate the use of aggregation functions, both for evaluating the relative biodiversity of different ecological communities, and for understanding human tendencies when making intuitive diversity comparisons. The dataset we use was obtained from an online exercise where individuals were asked to compare hypothetical communities in terms of diversity and importance for conservation.