Applications of Quantum Probability Theory to Strategic Decision Making

Abstract

This proposal aims to apply quantum probability theory to explain several inconsistencies in human dynamic decision making. This will not involve quantum physics phenomena, but it will take advantage of the axiomatic foundations laid in 1932 by John von Neumann to represent quantum events as subspaces of a vector space, rather than to represent them according to classical probability theory, axiomatized at about the same time by Kolmogorov, as subsets of a sample space. The Kolmogorov axioms, based on Boolean logic, require events to be commutative and distributive in probability, but the quantum formalization does not. In particular the quantum formulation provides for a variety of situations, such as a superposition condition among preference states in a dynamic decisions. The PI and his co-workers have made progress in showing quantum probability applications to choice behavior in defined, constant conditions. This new research project attempts an extension to multi-stage decision processes and will test a reinforcement learning model based upon quantum probability.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 23, 2016
Source ID
FA95501510343

Entities

People

  • Jerome Busemeyer

Organizations

  • Air Force Office of Scientific Research
  • Indiana University
  • United States Air Force

Tags

Readers

  • Graph Algorithms and Convex Optimization.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing
  • Space