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Awareness: An empirical model.

Federico Bizzarri1, Alessandro Giuliani2, Chiara Mocenni1

  • 1Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.

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Summary
This summary is machine-generated.

This study models how analytical and intuitive knowledge interact to create awareness in decision-making. Emotions and self-observation drive the shift from habitual to optimal behavior.

Keywords:
Markov modelscognitiondecision makingintuitionmachine intelligenceoptimizationtacit knowledgeuncertainty

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Area of Science:

  • Cognitive Science
  • Decision Theory
  • Philosophy of Mind

Background:

  • The integration of analytical (explicit) and intuitive (implicit) knowledge is a fundamental challenge in understanding decision-making.
  • Concepts like intuition, tacit knowledge, metacognition, and emotions have been explored across philosophy, psychology, and economics.

Purpose of the Study:

  • To propose a mathematical model integrating analytical and intuitive knowledge within decision-making processes.
  • To investigate the impact of this integration on the emergence of awareness.
  • To evaluate the role of various factors in different reasoning styles and decision stages.

Main Methods:

  • Developed a mathematical model based on Markov Decision Processes.
  • Incorporated definitions of intuition, tacit knowledge, uncertainty, metacognition, and emotions.
  • Analyzed emergent behavioral patterns from model solutions.

Main Results:

  • Identified key properties of the interaction between explicit and implicit knowledge.
  • Demonstrated that awareness emerges dynamically through a self-observation feedback mechanism.
  • Showcased how emotions can foster the onset of awareness.

Conclusions:

  • Awareness in decision-making is a dynamic process, not a static state.
  • The model provides insights into the interplay of different knowledge types and their influence on behavior.
  • Emotions play a crucial role in facilitating awareness and optimal decision-making.