Seminar series on crime and violence prevention in Kenya

Nairobi- August 12th 2011: USIUhas partnered with the Open Society Initiative of East Africa andthe Kenya Institutie of Administration conduct a seminar series in crime and violence prevention in Kenya. The program will be launched at the Kenya Institute of Administration on Friday, August 12that 10:00am, in a ceremony to be presided over by the Permanent Secretary Ministry of Internal Security and Provincial Administration, Mr. Francis Kimemia. The seminar course will utilize the opportunity provided by the promulgation of the new constitution to sensitize policy makers and policy implementers in Kenya to the effective use that can be made of community actors and local assemblies to prevent crime, thereby redressing the lack of depth and sophistication of crime prevention discourse in Kenya. The seminars will involve key government officials as well as representatives of non-state actors including the private sector, religious organizations, community groups, non-governmental organizations, academia and the media, who have worked or interacted with issues of security or worked in an area relevant to crime prevention

Trust and Understanding in Face-to-Face and Synchronous Online Negotiations

This study investigates to what extent the synchronous character of chat communication overcomes the problems in creating mutual understanding and trust between conflicting parties. An experimental study was conducted in which participants negotiated with a confederate in either a face-to-face or online setting. The results show that negotiators feel equally understood and trusted by the other party in both conditions. However, their own ideas about understanding and trusting the other party are higher after a FtF negotiation than after an online negotiation

Literature Can: A Sociological Reading of Ngozi Chuma-Udeh's The Presidential Handshake

[EN] Decision making for farms is a complex task. Farmers have to fix the price of their production but several parameters have to be taken into account: harvesting, seeds, ground, season etc. This task is even more difficult when a group of farmers must make the decision. Generally, optimization models support the farmers to find no dominated solutions, but the problem remains difficult if they have to agree on one solution. In order to support the farmers for this complex decision we combine two approaches. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture domain.The authors acknowledge the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015. One of the authors acknowledges the partial support of the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595).Zaraté, P.; Alemany Díaz, MDM.; Del Pino, M.; Esteso, A.; Camilleri, G. (2019). How to Support Group Decision Making in Horticulture: An Approach Based on the Combination of a Centralized Mathematical Model and a Group Decision Support System. Lecture Notes in Business Information Processing. 348:83-94. https://doi.org/10.1007/978-3-030-18819-1_7S839434

Choosing a Voting Procedure for the GDSS GRUS

In group decision-making, the use of Group Decision Support Systems is increasing and in some groups, a facilitator is required to improve communication among participants. The facilitator has several roles in this situation, which include helping decision makers (DMs) to decide which type of aggregation they would prefer in each decision context. Whenever DMs have different objectives regarding the same problem, they might decide a consensual decision is no longer possible. Therefore, other types of aggregation are required. Voting rules are strongly applied in this type of situation. However, the question that arises is: who should decide the voting method? In this article, a framework for choice of a voting procedure in a business decision context is used. It takes the facilitator’s preferences into account while it seeks to choose which voting procedure best suits the environment of the Group Decision Support System GRoUp Support (GRUS)

Supporting decision-making through technology

Decision-making is a process through which an alternative is selected among others relied on gathered information. In order to select this alternative, it should have a reasonable priority based on its weight. Within the context of everyday practice in healthcare, clinical decision-making holds a fundamental place. Decision-making occurs numerous times throughout the management of patients (across the disease trajectory) and consists of a complex task which often involves diverse disciplines (i.e., decision-making within multidisciplinary team meetings). The complexity of the task can be further reinforced in patients with multimorbidity. In any clinical context, the task of decision-making carries with it the burden of responsibility for a patient's health and well-being. Echoing these challenging circumstances in healthcare, computerized clinical decision support systems (CDSSs) and medical diagnostic decision support systems (MDSS) have been developed to augment clinicians in their complex decision-making processes. These decision support systems represent a paradigm shift in healthcare today and their contribution to effective clinical decision-making is invaluable, however not without challenges. The scope of this chapter includes a review on the utilization of technological decision support systems within healthcare (and within specific diseases), an analysis of the limitations presented by these technologies, and finally present the features that the design of efficient, effective, and safe CDSS should incorporate