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Richard D. Gill, Bell mini-conference February 11 2023
Comment on
“Contextuality or Nonlocality: What
Would John Bell Choose Today?”
by Marian Kupczynski
Entropy 2023, 25, 280 (13pp.). https://doi.org/10.3390/e25020280
Entropy 2023, 25, 280 (13pp.). https://doi.org/10.3390/e25020280
• Random variables are represented by nodes in a DAG
• Their joint probability distribution is built up by composing
conditional distributions of the variable in each node given its
node parents
• (Variables in root nodes are statistically independent of one
another)
• Why this works: the nodes of a DAG can be ordered so that all
arrows are pointing the same way
Express in a graphical model
i, j 𝜆1, 𝜆2
𝜆i, 𝜆j
Ai(𝜆1, 𝜆i) Bj(𝜆2, 𝜆j)
~
~
Settings
(may be correlated)
Source hidden variables
(may be correlated)
Context dependent
instrument hidden variables
(may be correlated)
Alice’s outcome
+/–1
Bob’s outcome
+/–1
Notice:
• If the instrument hidden variables 𝜆i, 𝜆j are allowed to be
statistically dependent, then any four joint probability distributions
of pairs of setting outcomes (Ai, Bj) are allowed
• If the instrument hidden variables are assumed to be
uncorrelated, then CHSH holds (Gill and Lambare)
• MK’s escape route: the detection loophole. Outcome can also be
zero, and MK goes on to study correlations conditional on two
non-zero outcomes
• The cause of the muddle: bad notation
The dilemma
Kupczynksi’s quandrary

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Bell mini conference RDG.pptx

  • 1. Richard D. Gill, Bell mini-conference February 11 2023 Comment on “Contextuality or Nonlocality: What Would John Bell Choose Today?” by Marian Kupczynski Entropy 2023, 25, 280 (13pp.). https://doi.org/10.3390/e25020280
  • 2. Entropy 2023, 25, 280 (13pp.). https://doi.org/10.3390/e25020280
  • 3. • Random variables are represented by nodes in a DAG • Their joint probability distribution is built up by composing conditional distributions of the variable in each node given its node parents • (Variables in root nodes are statistically independent of one another) • Why this works: the nodes of a DAG can be ordered so that all arrows are pointing the same way Express in a graphical model
  • 4. i, j 𝜆1, 𝜆2 𝜆i, 𝜆j Ai(𝜆1, 𝜆i) Bj(𝜆2, 𝜆j) ~ ~ Settings (may be correlated) Source hidden variables (may be correlated) Context dependent instrument hidden variables (may be correlated) Alice’s outcome +/–1 Bob’s outcome +/–1 Notice:
  • 5. • If the instrument hidden variables 𝜆i, 𝜆j are allowed to be statistically dependent, then any four joint probability distributions of pairs of setting outcomes (Ai, Bj) are allowed • If the instrument hidden variables are assumed to be uncorrelated, then CHSH holds (Gill and Lambare) • MK’s escape route: the detection loophole. Outcome can also be zero, and MK goes on to study correlations conditional on two non-zero outcomes • The cause of the muddle: bad notation The dilemma Kupczynksi’s quandrary