Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods

Argument maps as policy memories for informed deliberation: A research note

This is an eGISE network paper. A significant area within eGovernment is concerned with systems to support democratic policy formation and decision making processes. In modern government, both local and national, consultation with interested parties is an important element in maintaining the democratic process. To date online consultation tools have has used existing software tools, which are simple text based tools that were not tailored to the process. This project proposes to develop an online tool that will visualise the issues and arguments graphically as the consultation process proceeds. Using Discourse Analysis and Ontological Engineering it will create Argument Maps that will server not only to inform participants but also the archive record of the consultation – the Policy Memory. We hypothesise that such a tool would allow citizens to be come more engaged with policy formation and enhance democratic participation

Big data in higher education: an action research on managing student engagement with business intelligence

This research aims to explore the value of Big Data in student engagement management. It presents an action research on applying BI in a UK higher education institution that has developed and implemented a student engagement tracking system (SES) for better student engagement management. The SES collects data from various sources, including RFID tracking devices across many locations in the campus and student online activities. This public funded research project has enhanced the current SES with BI solutions and raised awareness on the value of the Big Data in improving student experience. The action research concerns with the organizational wide development and deployment of Intelligent Student Engagement System involving a diverse range of stakeholders. The activities undertaken to date have revealed interesting findings and implications for advancing our understanding and research in leveraging the benefit of the Big Data in Higher Education from a socio-technical perspective

TEAM COMPOSITION, LEADERSHIP AND INFORMATION-PROCESSING BEHAVIOR A simulation game study of the locus-of-control personality trait

In this study, we relate the individual locus-of-control personality trait of team members to the team’s information gathering and processing behavior. We adopt a team information-processing approach arguing that a team’s information-processing capacity is a function of its composition with respect to the members’ locus of control and the leadership structure of the group. We develop models that go beyond analyzing simple main effects of differences in team locus-of-control composition. We hypothesize that (a) the impact of the team locus-of-control mean depends on the within-group locus-of-control diversity, and (b) the effect of both the team locus-of-control mean and its standard deviation is contingent upon the leadership structure of the group. The hypotheses were tested on 44 teams participating in an elaborate international management simulation over six time periods. As predicted, we find that teams with a high average internal locus-of-control score collect more information and make more informed decisions when the within-team locus-of-control spread is low, and when the team operates without a leader. The opposite is the case for teams with a high average external locus-of-control score. In addition, locus-of-control diversity induces team information search only in the case when the team has no leader. We also show that team financial performance is comparably affected by our focal independent team variables. On a general level, our results offer strong support for recent pleas to study theoretically relevant individual traits, use proper aggregation models and include structural moderator variables in team composition research.Economics ;

How Inflation Targeters (Can) Deal with Uncertainty

The paper argues that a well-designed methodology for dealing with uncertainty improves the quality of interest-rate decisions taken by inflation targeters. A well-planned methodology is also more easily communicated to the general public, and the subsequent greater transparency makes inflation targeting more efficient. Therefore, it is relevant for an inflation targeter to consult with or consider information from other inflation targeters, researchers, and relevant decision makers when designing or improving upon their methodology. The paper also summarizes the results of a recent survey on methods for dealing with uncertainty for inflation targeters. The results are presented in a framework designed in line with decision analysis. The paper summarizes which methods are commonly used by inflation targeters and what lessons can be learnt from economic research and from decision makers.inflation targeting, uncertainty, decision analysis, robustness analysis