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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Related Experiment Videos

A dynamic complex intuitionistic fuzzy Dombi framework for multi-attribute decision-making with IoT applications.

Maryam Liaqat1, Ghaliah Alhamzi2, Dilshad Alghazzawi3

  • 1Department of Mathematics, Division of Science and Technology, University of Education, Lahore, 54770, Pakistan.

Scientific Reports
|May 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new dynamic decision-making model using complex intuitionistic fuzzy sets to handle uncertainty. The proposed framework offers a robust tool for complex, time-dependent problems, improving upon existing methods.

Keywords:
Aggregation operatorComplex intuitionistic fuzzy setsInternet of ThingsMultiple attribute decision-making

Related Experiment Videos

Area of Science:

  • Decision Sciences
  • Fuzzy Set Theory
  • Artificial Intelligence

Background:

  • Real-world decision-making involves uncertainty and time-dependent data.
  • Existing fuzzy and intuitionistic fuzzy models struggle with dynamic, multidimensional uncertainty.
  • A need exists for advanced models to handle complex decision environments.

Purpose of the Study:

  • To develop a novel dynamic multi-attribute decision-making model.
  • To address limitations in modeling multidimensional and dynamic uncertainty.
  • To enhance decision-making in uncertain and time-dependent scenarios.

Main Methods:

  • Utilized complex intuitionistic fuzzy sets for dynamic uncertainty modeling.
  • Proposed a powerful score function to resolve comparison ambiguities.
  • Introduced complex intuitionistic fuzzy dynamic Dombi aggregation operators for time-dependent data.
  • Developed a systematic decision-making algorithm.

Main Results:

  • The new score function and aggregation operators effectively handle complex fuzzy data.
  • The proposed decision-making algorithm demonstrated practicality and effectiveness.
  • Comparative analyses confirmed the method's stability, reliability, and discriminative power.

Conclusions:

  • The developed framework provides a flexible and robust tool for dynamic decision-making.
  • This approach overcomes limitations of traditional fuzzy models in uncertain environments.
  • The study offers significant advancements for real-world decision support systems.