Decision Sciences or decision-making is one of the branches of advanced management science, and it focus on making decisions and defining suitable policies for various companies and institutions. It also cares about developing the knowledge, machine learning, statistical decision theory, operations research, forecasting, behavioral decision theory and cognitive psychology, tools, and methodologies necessary for projects management, in a way that provides economic and moral addition to the companies or institutions. As well as solving complicated problems related to the globalized economy, whether it was financial or legal, or related to communication problems, according to the methods of quantitative and statistical analysis and data processing and modern methodologies.
So, this science provides us with a set of answers to the following important questions such as :
Organizations make many informed decisions, such as increasing production capacity, improving human capital, entering a new market, etc. This indicates that managers make one of two main types of decisions: programmed (structured) and non-programmed (unstructured) decisions. While programmed decisions concern perfectly stable situations, non-programmed decisions concern the real situation, surrounded by uncertainties, risks and ambiguities. For optimal value creation, this paper succinctly states that robust decision sciences and analysis serve as a precursor.
The decision-making environment is constantly changing and organizations must make decisions that change in proportion to these environmental changes if they are to survive. The decision maker uses probability values to convert uncertainties and risks into perfect knowledge poles for making informed decisions. Models are true decision-making tools and are deterministic and probabilistic (or stochastic) for scheduled and unscheduled decisions respectively.
So that, real-world value optimization focuses on decisions in situations of pure uncertainty and risk, generating model adjustments for optimal value creation. So, models for optimal value creation under uncertainty and risk are suggested and organizations are advised to use professional decision scientists and analysts if the need arises.
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Multiple Attribute Decision Analysis (MADA) problems in the situation of Belief Group Decision Making (BGDM) are a special class of decision problems, where the attribute evaluations of each decision maker (DM) are represented by belief functions. In order to solve these special problems, the TOPSIS model (technique for order preference by similarity to ideal solution) is extended by three approaches, whereby group preferences are aggregated in different ways. Based on these three approaches, three extended TOPSIS models, the pre-model, post-model and inter-model, are developed and their procedures are elaborated step by step. The aggregation of group preferences in the three extended models depends on some rules or its modifications, certain social choice functions and certain average approaches. Furthermore, a numerical example clearly illustrates the procedures of the three extended models for the BGDM.
Indeed, it can be said that overall, the decision science that is interested in making decisions could be a handle including choices. The process in general, comprises of a few steps: recognizing issues, producing options, assessing choices, choosing an elective, actualizing the decision, and assessing decision adequacy (Lunenburg, 2010). Every step in decision making prepare is imperative and directors ought to consider it. Typically since administration objectives or targets cannot be accomplished without legitimate choice making process.