scholarly journals Decision Making in Translation: Translator’s Strategies and Decision Models for Rich Points in Titles

2021 ◽  
Vol 31 (2) ◽  
pp. 811-833
Author(s):  
Lyudmila Enbaeva
Keyword(s):  
Decision Making ◽  
Decision Models

On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria

2016 ◽  
Vol 2 (3) ◽  
pp. 54
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Decision Models ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Method ◽  
Analytic Hierarchy ◽  
Comparison Model ◽  
Hierarchy Process

Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10].  The weight vectors from this comparison model are obtained by using extent analysis method.  This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

Judgment Based Marketing Decision Models: Problems and Possible Solutions

1981 ◽  
Vol 45 (4) ◽  
pp. 13-23 ◽  
Author(s):  
Dipankar Chakravarti ◽  
Andrew Mitchell ◽  
Richard Staelin
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Laboratory Experiments ◽  
Field Studies ◽  
Decision Models ◽  
Future Research ◽  
Marketing Decision ◽  
Recent Laboratory

This paper presents a comparative analysis of the findings of two field studies and three recent laboratory experiments that assessed the efficacy of judgment based models in aiding marketing decision making. This analysis indicates factors that may affect the effectiveness of these models. The implications of the findings for users of judgment based marketing decision models as well as model builders are discussed, and suggestions are made for future research to improve the models’ effectiveness.

scholarly journals Big Data, Decision Models, and Public Health

2020 ◽  
Vol 17 (18) ◽  
pp. 6723
Author(s):  
Chien-Lung Chan ◽  
Chi-Chang Chang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Medical Decision ◽  
Mining Machine ◽  
Special Issue ◽  
Trade Offs

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

scholarly journals Towards a quantum-like cognitive architecture for decision-making

2020 ◽  
Vol 43 ◽  
Author(s):  
Catarina Moreira ◽  
Lauren Fell ◽  
Shahram Dehdashti ◽  
Peter Bruza ◽  
Andreas Wichert
Keyword(s):  
Decision Making ◽  
Quantum Mechanics ◽  
Cognitive Biases ◽  
Cognitive Architecture ◽  
Decision Models ◽  
Classical Models ◽  
The Mind ◽  
Computational Resources

Abstract We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information compared to classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics or on assumptions of the computational resources of the mind.

scholarly journals DECISION CONCEPTS

2014 ◽  
pp. 18-22
Author(s):  
Miki Sirola
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Analysis ◽  
Decision Support Systems ◽  
Production Planning ◽  
Support Systems ◽  
Detailed Examination ◽  
Decision Models ◽  
Decision Science ◽  
Knowledge Based

Decision making is done in many application areas. Still most studies are done in such fields as economy and production planning. In methodologies used there exists more variation. This paper reviews the decision concepts discussed in the literature. Also some decision models by the author are commented. The field and practise in decision science is summarized. Although decision support systems are the final results of many projects, they are mostly based on the decision concepts behind the studies that deserve also more detailed examination. Decision analysis approach and knowledge-based technologies are examples of commonly used concepts.

scholarly journals Impact of weight indicators of TOPSIS technique in the selected district in the Slovak republic

2013 ◽  
Vol 2 (4) ◽  
pp. 61
Author(s):  
Roman Vavrek ◽  
Peter Adamišin
Keyword(s):  
Decision Making ◽  
Local Government ◽  
Decision Maker ◽  
Slovak Republic ◽  
Multiple Attribute Decision Making ◽  
Local Government Area ◽  
Decision Models ◽  
Multicriteria Decision ◽  
Multiple Attribute ◽  
Topsis Technique

The purpose of multicriteria decision models is to help decision maker to evaluate each alternative and to rank them in descending order of performance. This study analyses the concept of Multiple Attribute Decision Making for using in local government area. The aim of this paper is to analyse the concept of Multiple Attribute Decision Making for selecting the most efficient municipality in selected district in the Slovak Republic. Achieving this purpose, TOPSIS technique (in two variants) is used as decision making tools.

The Structuring Of The Strategic-Decision-Making Process

2011 ◽  
pp. 213-240
Author(s):  
Tamio Shimizu ◽  
Marley Monteiro de Carvalho ◽  
Fernando Jose Barbin
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Decision Trees ◽  
Strategic Decision ◽  
Decision Models ◽  
Decision Problems ◽  
Strategic Decision Making ◽  
Decision Making Process ◽  
Decision Matrix

The basic types of decision models presented in the previous chapter (rational, descriptive, political, and ambiguous models) relies on quantitative values (money, time, or probabilities) that are most suitable for structured and semi-structured decision problems. These basic models can be used as starting models to guide the structuring process of strategic decision problems. First, a systematic procedure for structuring the strategic decision making process is presented, using decision matrix and decision trees. The need for the sensitivity analysis is introduced, and will be illustrated with more detail in the next chapter. Some problems that must be considered in this structuring process are illustrated in form of hidden traps and paradoxes. The first step in the decision-making process is to formulate the problem. It is possible that an inadequate formulation of the problem leads to a result that reduces efficiency and efficacy, since an incorrect formulation can define a wrong problem.

Decision Making Model Based on the Z-Information

2020 ◽  
Vol 26 (11) ◽  
pp. 625-630
Author(s):  
O. M. Poleshchuk ◽  
Keyword(s):  
Decision Making ◽  
Multicriteria Decision Making ◽  
Decision Models ◽  
Probabilistic Assessment ◽  
Multicriteria Decision ◽  
Numerical Example ◽  
Model Based ◽  
Definition Of ◽  
Decision Making Model ◽  
The Ideal

A model of multicriteria decision making is developed taking into account the reliability of the data obtained. To formalize the information containing the data and assess their reliability, Z-numbers are used, the definition of which was given by Lotfie Zadeh in 2011. Most of the well-known decision models based on Z-numbers are limited by the assumption of a probabilistic assessment of the reliability of the data, which significantly narrows the scope of these models. This article partially removes the restrictive requirements when working with Z-numbers. For components of Z-numbers, aggregate indicators are calculated using a-cuts, based on which the similarity indicator between Z-numbers is determined. Choosing the best alternative is based on the minimum indicator of similarity with the ideal alternative. A numerical example is presented that shows the operation of the model and its effectiveness under conditions of multi-criteria selection.

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