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Decision Making01:20

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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.
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Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
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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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Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as...
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A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective.

PloS one·2024
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N-bipolar hypersoft sets: Enhancing decision-making algorithms.

Sagvan Y Musa1

  • 1Department of Mathematics, Faculty of Education, University of Zakho, Zakho, Iraq.

Plos One
|January 16, 2024
PubMed
Summary

This study introduces N-bipolar hypersoft (N-BHS) sets, a novel framework for handling mixed data types. N-BHS sets offer enhanced versatility and address limitations in current models for uncertainty management.

Area of Science:

  • Fuzzy Set Theory
  • Uncertainty Quantification
  • Decision Support Systems

Background:

  • Traditional bipolar hypersoft (BHS) sets struggle with mixed data types.
  • Existing N-bipolar soft sets have limitations in handling multi-argument approximate functions.
  • A need exists for a more versatile framework to manage evaluations with both binary and non-binary data.

Purpose of the Study:

  • Introduce N-bipolar hypersoft (N-BHS) sets as an extension of BHS sets.
  • Develop a parameterized representation for nuanced attribute perception.
  • Address limitations in N-bipolar soft sets for multi-argument functions and uncertainty.

Main Methods:

  • Defined parameterized representation of the universe for finite granularity.
  • Partitioned attributes into disjoint subattribute values.

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  • Outlined algebraic definitions: incomplete, efficient, normalized N-BHS sets, complements, and threshold-derived BHS sets.
  • Explored set-theoretic operations: relative null/whole, subsets, extended/restricted union and intersection.
  • Proposed and compared decision-making methodologies.

    • Main Results:

    • Demonstrated the enhanced versatility of N-BHS sets for mixed data evaluations.
    • Showcased the ability of N-BHS sets to provide nuanced attribute perception.
    • Illustrated the effectiveness of N-BHS sets in addressing uncertainty-related problems through algebraic definitions and operations.
    • Presented comparative decision-making approaches.

    Conclusions:

    • N-bipolar hypersoft (N-BHS) sets offer a powerful and versatile extension for managing complex data evaluations.
    • The parameterized representation and attribute partitioning enhance precision in handling uncertainty.
    • N-BHS sets provide a robust framework for decision-making, outperforming existing models in specific applications.