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Subjective expected utility on orthomodular lattices.

Marcus Pivato1

  • 1Centre d'Économie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, Paris, Île-de-France, France.

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Summary

This study introduces a category-theoretic framework for decision theory, creating new models for decision-making under classical and quantum uncertainty using orthomodular lattices.

Keywords:
Boolean algebracategory theoryquantum uncertaintysyntactic decision theory

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Area of Science:

  • Decision Theory
  • Category Theory
  • Quantum Mechanics
  • Lattice Theory

Background:

  • A general category-theoretic framework for decision theory has been developed.
  • Orthomodular lattices (OMLs) are a class of lattices with applications in quantum mechanics and logic.
  • Boolean algebras are a subset of OMLs, representing classical uncertainty.

Purpose of the Study:

  • To apply a general category-theoretic framework to the category of orthomodular lattices (OMLs).
  • To develop new syntactic models for decision-making incorporating classical and quantum uncertainty.
  • To explore the intersection of quantum theory, topology, and decision-making models.

Main Methods:

  • Utilizing a recently developed general category-theoretic framework for decision theory.
  • Applying this framework to the specific category of orthomodular lattices (OMLs).
  • Leveraging the properties of Boolean algebras and Hilbert space lattices as instances of OMLs.

Main Results:

  • A novel syntactic model for decision-making with classical uncertainty is established via Boolean algebras as OMLs.
  • A new model for decision-making with quantum uncertainty is presented using the lattice of closed subspaces of a Hilbert space as an OML.
  • Demonstrates the utility of category theory in unifying models of decision-making across different uncertainty types.

Conclusions:

  • The category-theoretic framework provides a unified approach to decision-making under various forms of uncertainty.
  • Orthomodular lattices offer a rich structure for modeling both classical and quantum uncertainty in decision processes.
  • This work bridges theoretical computer science, quantum physics, and decision theory, contributing to a deeper understanding of uncertainty quantification.