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Expected Utility Based Decision Making under Z-Information and Its Application.

Rashad R Aliev1, Derar Atallah Talal Mraiziq1, Oleg H Huseynov2

  • 1Department of Mathematics, Eastern Mediterranean University, Famagusta, Northern Cyprus, Mersin 10, Turkey.

Computational Intelligence and Neuroscience
|September 15, 2015
PubMed
Summary

This study introduces Z-numbers to represent unreliable real-world information, offering a more descriptive approach than fuzzy numbers. Decision-making models using Z-information improve handling of imperfect data in economics.

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Area of Science:

  • Decision Analysis
  • Information Theory
  • Economics

Background:

  • Real-world information is frequently unreliable due to source limitations, biases, and errors.
  • Existing methods like fuzzy numbers offer limited descriptive power for imperfect information.
  • Z-numbers provide a more comprehensive framework for representing natural language-based information with reliability.

Purpose of the Study:

  • To introduce a novel approach for decision-making under Z-information.
  • To demonstrate the utility of direct computation over Z-numbers.
  • To apply this framework to a benchmark economic decision problem.

Main Methods:

  • Formalization of imperfect information using Z-numbers.
  • Development of a decision-making approach based on direct Z-number computation.
  • Application of the expected utility paradigm within the Z-information framework.

Main Results:

  • The proposed Z-information approach effectively handles partially reliable data.
  • Direct computation over Z-numbers provides a robust method for decision analysis.
  • The approach is successfully applied to a relevant economic decision problem.

Conclusions:

  • Z-information offers a superior representation of real-world imperfect information compared to fuzzy numbers.
  • Direct computation over Z-numbers enables effective decision-making under uncertainty.
  • This framework has significant implications for economic modeling and other fields dealing with unreliable data.