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A quantum-like cognitive approach to modeling human biased selection behavior.

Aghdas Meghdadi1, M R Akbarzadeh-T2, Kurosh Javidan3

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This study introduces a novel quantum-like Bayesian network (QBN) to model cognitive biases in human decision-making. The biased entangled QBN (BEQBN) model shows superior predictive accuracy for human behaviors compared to classical and other QBN approaches.

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Area of Science:

  • Cognitive Science
  • Quantum Physics
  • Computational Social Science

Background:

  • Human decision-making is significantly influenced by cognitive biases, which are often overlooked in behavioral modeling.
  • Classical models struggle to capture the irrational aspects of human choices driven by biases.
  • Existing quantum-like approaches do not fully account for social influences on individual biases.

Purpose of the Study:

  • To develop a novel cognitive quantum-like approach for modeling human biases in decision-making.
  • To introduce a biased entangled Quantum-like Bayesian Network (BEQBN) model incorporating social entanglement.
  • To enhance the prediction of human selection behaviors by accounting for cognitive biases.

Main Methods:

  • Simulated society as a quantum system using a Quantum-like Bayesian Network (QBN) structure.
  • Developed an improved entangled QBN approach inspired by electric fields to model initial biases.
  • Introduced a bias potential function and a quantum-like entanglement witness in Hilbert space for the BEQBN model.

Main Results:

  • The proposed biased entangled QBN (BEQBN) model effectively incorporates social entanglement and bias potential.
  • BEQBN achieved the first rank in predictive accuracy on two empirical tasks.
  • The model demonstrated superior performance compared to classical Bayesian Networks and six other QBN approaches.

Conclusions:

  • The BEQBN model provides a more realistic prediction of human behaviors by integrating cognitive biases.
  • Quantum-like modeling, particularly with social entanglement, offers a powerful framework for understanding decision-making.
  • This approach highlights the importance of considering agents as part of a social system rather than isolated entities.