Highlights• We evaluate Multi-criteria decision making tools for their usefulness • We used incentive-based experiments • The usefulness of different tools slightly varied but overall were found good • Participants followed the tool s recommendation whilst revising their ranking • Inconsistency level in judgments had no effect on the usefulness of these tools Abstract:Many decision makers still question the usefulness of multi-criteria decision-making methods and prefer to rely on intuitive decisions. In this study we evaluated a number of multi-criteria decision-making tools for their usefulness using incentive-based experiments, which is a novel approach in operations research but common in psychology and experimental economics. In this experiment the participants were asked to compare five coffee shops to win a voucher for their best-rated shop. We found that, although the usefulness of different multi-criteria decision-making tools varied to some extent, all the tools were found to be useful in the sense that, when they decided to change their ranking, they followed the recommendation of the multi-criteria decision-making tool. Moreover, the level of inconsistency in the judgements provided had no significant effect on the usefulness of these tools.

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Abstract-Channel bonding (CB) is a proven technique to increase bandwidth and reduce delays in wireless networks. It has been applied in traditional wireless networks such as cellular networks and wireless local area networks along with the emerging cognitive radio networks. This paper first focuses on providing a survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks and wireless sensor networks, and then provides a detailed discussion on the CB schemes proposed for cognitive radio networks. Finally, we highlight a number of issues and challenges regarding CB in cognitive radio sensor networks and also provide some guidelines on using CB schemes in these futuristic networks.

Article:Lundy, M, Siraj, S orcid.org/0000-0002-7962-9930 and Greco, S (2017) The mathematical equivalence of the "spanning tree" and row geometric mean preference vectors and its implications for preference analysis. Pairwise comparison is a widely used approach to elicit comparative judgements from a decision maker (DM), and there are a number of methods that can be used to then subsequently derive a consistent preference vector from the DM's judgements. While the most widely used method is the eigenvector method, the row geometric mean approach has gained popularity due to its mathematical properties and its ease of implementation. In this paper, we discuss a spanning tree method and prove the mathematical equivalence of its preference vector to that of the row geometric mean approach. This is an important nding due to the fact that it identies an approach for generating a preference vector which has the mathematical properties of the row geometric mean preference vector, and yet, in its entirety, the spanning tree method has more to oer than the row geometric mean method, in that, it is inherently applicable to incomplete sets of pairwise comparison judgements, and also facilitates the use of statistical and visual techniques to gain insights into inconsistency in the DM's judgements.