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This short article aims to reflect on the definition of the term politics in general. To do this, after applying the scientific method and structuring a short work on the problem, a solution model is used and a form of hypothesis development is tested in different social fields. It concludes with the inference of a generic concept of proven policy through the proposed model. ; Este breve artículo tiene el objetivo de reflexionar sobre la definición del término política en general.Para ello luego de aplicar el método científico y estructurar un trabajo corto sobre la problemática, se utiliza un modelo de solución y se prueba una forma de desarrollo de hipótesis en diferentes campos sociales. Se concluye con la inferencia de un concepto genérico de política probado a través del modelo propuesto.

ABSTRACTIn multi‐attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative's intensity of dominance, known as dominance measuring methods. Different dominance measuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we use Monte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions or weights represented by fuzzy numbers. Moreover, dominance measuring method performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one. Copyright © 2012 John Wiley & Sons, Ltd.

AbstractThis paper describes a decision support system based on an additive multi‐attribute utility model for identifying the optimal strategy in complex decision‐making problems. The system allows for incomplete information on the component utility function and weight assessment, which leads to classes of utility functions and weight intervals, respectively. This makes the system suitable for group decision support, because individual conflicting views or judgements in a group of decision‐makers can be captured through imprecise responses in the respective assessment methods. Moreover, the system admits uncertainty about the multi‐attribute strategy consequences, which can be defined in term of ranges for each attribute instead of single values. The system computes non‐dominance and potential optimality to identify the most preferred strategy. We also propose an approach based on Monte Carlo simulation techniques for group decision‐making problems, which could be specially useful for e‐democracy, where several decision‐makers or groups of decision‐makers elicit their own preferences separately, which are then compared to try to reach a consensus on their respective preferences. Copyright © 2004 John Wiley & Sons, Ltd.