Superdeterministic hidden-variables models I: nonequilibrium and signalling

• URL: http://arxiv.org/abs/2003.11989v4
• Date: Wed, 2 Dec 2020 16:53:58 GMT
• Title: Superdeterministic hidden-variables models I: nonequilibrium and signalling
• Authors: Indrajit Sen and Antony Valentini
• Abstract summary: We first give an overview of superdeterminism and discuss various criticisms of it raised in the literature. We take up Bell's intuitive criticism that these models are conspiratorial'
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This is the first of two papers which attempt to comprehensively analyse superdeterministic hidden-variables models of Bell correlations. We first give an overview of superdeterminism and discuss various criticisms of it raised in the literature. We argue that the most common criticism, the violation of `free-will', is incorrect. We take up Bell's intuitive criticism that these models are `conspiratorial'. To develop this further, we introduce nonequilibrium extensions of superdeterministic models. We show that the measurement statistics of these extended models depend on the physical system used to determine the measurement settings. This suggests a fine-tuning in order to eliminate this dependence from experimental observation. We also study the signalling properties of these extended models. We show that although they generally violate the formal no-signalling constraints, this violation cannot be equated to an actual signal. We therefore suggest that the so-called no-signalling constraints be more appropriately named the marginal-independence constraints. We discuss the mechanism by which marginal-independence is violated in superdeterministic models. Lastly, we consider a hypothetical scenario where two experimenters use the apparent-signalling of a superdeterministic model to communicate with each other. This scenario suggests another conspiratorial feature peculiar to superdeterminism. These suggestions are quantitatively developed in the second paper.

Related papers

• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models [51.66015254740692]
  We show that for an ensemble of deep learning based classification models, bias and variance are emphaligned at a sample level. We study this phenomenon from two theoretical perspectives: calibration and neural collapse.
  arXiv  Detail & Related papers  (2023-10-13T17:06:34Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Response: Kupczynski Contextual Locally Causal Probabilistic Models are constrained by Bell theorem [0.0]
  In our contextual model, statistical independence is violated, thus it is not constrained by Bell Theorem. In several Bell Tests, two time series of distant clicks are converted into finite samples containing pairs of non zero outcomes.
  arXiv  Detail & Related papers  (2023-05-19T20:09:01Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Experimentally adjudicating between different causal accounts of Bell inequality violations via statistical model selection [0.0]
  Bell inequalities follow from a set of seemingly natural assumptions about how to provide a causal model of a Bell experiment. Two types of causal models that modify some of these assumptions have been proposed. We seek to adjudicate between these alternatives based on their predictive power.
  arXiv  Detail & Related papers  (2021-07-30T19:33:02Z)
• Analysis of the superdeterministic Invariant-set theory in a hidden-variable setting [0.0]
  We build a hidden-variable model based on the Invariant-set theory proposal. We critically analyse several aspects of the proposal using the model. Our results lend further support to the view that superdeterminism is unlikely to solve the puzzle posed by the Bell correlations.
  arXiv  Detail & Related papers  (2021-07-10T04:50:36Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)
• A Weaker Faithfulness Assumption based on Triple Interactions [89.59955143854556]
  We propose a weaker assumption that we call $2$-adjacency faithfulness. We propose a sound orientation rule for causal discovery that applies under weaker assumptions.
  arXiv  Detail & Related papers  (2020-10-27T13:04:08Z)
• Superdeterministic hidden-variables models II: conspiracy [0.0]
  We prove that superdeterministic models of quantum mechanics are conspiratorial in a mathematically well-defined sense. We show how to quantify superdeterministic conspiracy without using nonequilibrium. Nonlocal and retrocausal models turn out to be non-conspiratorial according to both approaches.
  arXiv  Detail & Related papers  (2020-03-27T01:01:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.