Consensus in innovation contest categorisation by means of fuzzy partitions

Consensus decision-making is fuzzy by nature, yet fuzzy consensus decision-making in a medium to large number of decisions is not widely used since it demands additional information that requires extra decision-maker effort. Consensus decision-making rests on properly measured agreement. This paper proposes a fuzzy measure of agreement through fuzzy kappa based on fuzzy partitions. These fuzzy partitions enable decision-makers to assess their decisions with a degree of confidence. A fuzzy partition is built for each decision-maker considering his/her confidence degrees when categorising a set of alternatives or solutions. This enables decision-makers to more easily capture the fuzzy nature of the decision. In addition, this paper presents a real-life experiment based on a innovation contest to show the feasibility of using confidence degrees in real-life applications compared to traditional consensus decision-making. The results suggest that the use of confidence degrees improves the level of agreement in the consensus decision-making process through fuzzy kappa coefficients, and it also improves the level of agreement in the consensus decision-making process.