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Fuzzy utility and equilibria.

Philippe De Wilde1

  • 1Intelligent and Interactive Systems Group, Department of Electrical Engineering, Imperial College London, London SW7 2BT, UK. p.dewilde@imperial.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2004
PubMed
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This study introduces a fuzzy decision-making framework for situations with uncertain competitors and fuzzy constraints. It shows fuzzy utility maximization yields the same equilibria as traditional methods.

Area of Science:

  • Decision Sciences
  • Game Theory
  • Fuzzy Logic

Background:

  • Decision-makers often face ambiguous constraints and uncertainty regarding competitors' actions.
  • Traditional economic models struggle with inherent fuzziness in decision-making scenarios.

Purpose of the Study:

  • To develop a robust framework for fuzzy decision-making.
  • To analyze fuzzy choice rationality and fuzzy utility maximization.
  • To model and analyze games with competitor uncertainty using fuzzy logic.

Main Methods:

  • Integration of fuzzy logic, game theory, and micro-economics.
  • Introduction and analysis of fuzzy constraints and fuzzy utility maximization.
  • Implementation of fuzzy Cournot adjustment and stability analysis of equilibria.

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Main Results:

  • Fuzzy constraints can be effectively combined with fuzzy utility maximization.
  • Fuzzy Cournot adjustment is implemented, and equilibria are defined and studied.
  • Uncertainty about competitor states and utilities does not alter equilibria under likely utility maximization procedures.

Conclusions:

  • The proposed fuzzy decision-making framework accommodates ambiguity effectively.
  • The framework demonstrates that fuzzy utility maximization aligns with traditional game theory outcomes in uncertain environments.
  • This research provides a valuable tool for strategic decision-making under uncertainty.