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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Quantum Probability Approach to Improving Human-AI Decision Making.

Scott Humr1, Mustafa Canan1, Mustafa Demir2

  • 1Department of Information Sciences, Naval Postgraduate School, Monterey, CA 93943, USA.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Quantum probability theory (QPT) offers a novel framework for understanding human-AI decision-making interactions. Applying QPT can enhance human-AI collaboration by improving predictability and rationalization processes in decision support systems.

Keywords:
artificial intelligencedecision makinggenerative AIhuman-in-the-loopquantum decision theory

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Computer Interaction
  • Quantum Probability Theory

Background:

  • Artificial intelligence (AI) is increasingly involved in human decision-making processes.
  • Human rationalization of AI outputs can lead to uncertainties due to incongruencies.
  • Existing models need enhancement to predict human-AI interaction outcomes effectively.

Purpose of the Study:

  • To explore the application of quantum probability theory (QPT) to human-AI decision-making.
  • To integrate QPT concepts into human-in-the-loop decision-making frameworks.
  • To provide a novel conceptualization for improving human-AI interaction predictability.

Main Methods:

  • Perspective paper analyzing the potential of QPT in human-AI interactions.
  • Utilizing human-in-the-loop constructs to explicate QPT applications.
  • Reviewing recent QPT applications and their relevance to cognitive modeling.

Main Results:

  • QPT offers a promising formalism to model the cognitive aspects of human-AI interactions.
  • Integration of QPT can ameliorate human-in-the-loop models by capturing complex behaviors.
  • Quantum probability formalisms can advance cognitive system models in decision support.

Conclusions:

  • QPT presents a novel approach to address uncertainties in human-AI decision-making.
  • Further research into QPT applications can significantly improve human-AI collaboration.
  • Future work should focus on developing and testing QPT-based models for human-AI systems.