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相关概念视频

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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Decision Making: P-value Method01:09

Decision Making: P-value Method

5.5K
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...
5.5K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.1K
Decision Making01:20

Decision Making

152
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...
152
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

458
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
458
Heuristics01:21

Heuristics

113
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.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
113

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相关实验视频

Updated: Jul 27, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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创新的直观的模糊的公平聚合运算符与基于线性编程的决策方法.

Muhammad Riaz1, Hafiz Muhammad Athar Farid1, Rukhsana Kausar1

  • 1Department of Mathematics, University of the Punjab, Lahore, Pakistan.

Journal of ambient intelligence and humanized computing
|June 8, 2023
PubMed
概括

本研究为直观模糊集 (InFS) 引入了新的操作规则和聚合运算符,以改善不完全信息的决策. 开发的方法为多标准决策 (MCDM) 问题提供了公平的解决方案.

科学领域:

  • 决策科学 决策科学
  • 模糊逻辑系统 模糊逻辑系统
  • 运营研究 运营研究

背景情况:

  • 直觉模糊集合 (InFS) 理论为战略规划和决策提供了一个强大的框架,特别是在处理不确定性时.
  • 聚合运营商 (AO) 对于决策过程中综合信息至关重要,但不完整数据带来了挑战.
  • 现有的方法在有限信息的复杂决策环境中难以提供公平和中立的解决方案.

研究的目的:

  • 在直观的模糊集环境中开发新的操作规则和聚合运算符.
  • 引入一种公平的方法来处理直观的模糊信息,利用比例分布.
  • 构建一个公平的多标准决策 (MCDM) 方法,使用拟议的运算符,并处理部分权重信息.

主要方法:

  • 根据比例分配,为InFS制定新的运行法律.
  • 为直观模糊数构建公平聚合运算符.
  • 整合线性编程模型以确定具有部分信息的标准权重.
  • 制定一个公平的MCDM方法,包括多个决策者的评估.

主要成果:

  • 为InFS成功建立了新的操作规则和公平聚合运营商.
  • 建立了一个公平的MCDM方法,在处理不完整的信息和部分权重方面表现出有效性.
关键词:
公平的聚合运营商.直觉的模糊集是一个模糊集.线性编程模型的线性编程模型.在MCDM中,MCDM是MCDM.

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  • 拟议的方法为InFS环境中的决策挑战提供了中立或公平的补救措施.
  • 严格的实施验证了开发的聚合运营商的有效性.
  • 结论:

    • 拟议的操作规则和聚合运算符在直观模糊集框架内增强了决策能力.
    • 开发的公平的MCDM方法有效地解决了不完整信息和部分权重数据所带来的挑战.
    • 这项研究为战略规划和不确定性下复杂决策分析提供了创新工具.