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This paper presents a practical case involving a shopping mall location problem in the northeast countryside of Brazil. In this problem, conflicting objectives have been expressed in terms of seven criteria. Then, ten cities of the northeastern countryside have been selected to compose the space of actions. The problem plays a special role since Brazil is a big country that requires investments in the countryside. Thus, the shopping mall aims to stimulate economic growth in the respective region. In the study, this multi-objective problem is solved using the FITradeoff method. In FITradeoff, the combination of the paradigms of holistic evaluation and elicitation by decomposition in preference modeling are well explored, bringing different perspectives for the decision-maker during the decision process.

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The COVID-19 pandemic has brought health systems to the brink of collapse in several regions around the world, as the demand for health care has outstripped the capacity of their services, especially regarding intensive care. In this context, health system managers have faced a difficult question: who should be admitted to an intensive care unit (ICU), and who should not? This paper addresses this decision problem using Expected Utility Theory and Bayesian decision analysis. In order to estimate the chances of survival for patients, a structured protocol has been proposed conjointly with physicians, based on the Sequential Organ Failure Assessment (SOFA) score. A portfolio selection approach is proposed to support tackling the ICU allocation problem. A simulation study shows that the proposed approach is more advantageous than other approaches already presented in the literature, with respect to the number of lives saved. The patients’ probabilities of survival inside and outside the ICU are important parameters of the model. However, assessing such probabilities can be a difficult task for health professionals. In order to give due treatment to the imprecise information regarding these probabilities, a Monte Carlo simulation is used to estimate the probabilities of recommending a patient be admitted to the ICU is the most appropriate decision, given the conditions presented. The methodology was implemented in an Information and Decision System called SIDTriagem, which is available online for free. With regards to managerial implications, SIDTriagem has a great potential to help in the response to public health emergencies systems as it facilitates rational decision-making regarding allocating ICU beds when resources are scarce.

COVID-19 pandemic has put health systems worldwide under pressure. Thus, establish a triage protocol to support the allocation of resources is important to deal with this public health crisis. In this paper, a structured methodology to support the triage of suspected or confirmed COVID-19 patients has been proposed, based on the utilitarian principle. A decision model has been proposed for evaluating three treatment alternatives: intensive care, hospital stay and home isolation. The model is developed according to multi-attribute utility theory and considers two criteria: the life of the patient and the overall cost to the health system. A screening protocol is proposed to support the use of the decision model, and a method is presented for calculating the probability of which of three treatment is the best one. The proposed methodology was implemented in an information and decision system. The originality of this study is using of the multi-attribute utility theory to support the triage of suspected COVID-19 and implement the decision model in an information and decision system.

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Neuroscience is a new and interdisciplinary field, so it is no surprise that its ideas and principles have been applied to the study of human decision processes in many different contexts. Understanding the communication and response characteristics of the brain can help to explain the heterogeneity of individual behavior and improve predictions about human interaction. Group decision and negotiation (GDN) is one area in which neuroscience may be especially helpful, as neural data can be used to improve predictions about human choices and the outcomes they produce. One goal is to understand how actors' interactions are registered directly in the brain, to identify affective factors, and thereby to explain humans' ability or inability to evaluate the actions and intentions of others. The