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Keywords = Reichenbach’s Principle

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29 pages, 693 KB  
Article
Beyond Causal Explanation: Einstein’s Principle Not Reichenbach’s
by Michael Silberstein, William Mark Stuckey and Timothy McDevitt
Entropy 2021, 23(1), 114; https://doi.org/10.3390/e23010114 - 16 Jan 2021
Cited by 7 | Viewed by 7209
Abstract
Our account provides a local, realist and fully non-causal principle explanation for EPR correlations, contextuality, no-signalling, and the Tsirelson bound. Indeed, the account herein is fully consistent with the causal structure of Minkowski spacetime. We argue that retrocausal accounts of quantum mechanics are [...] Read more.
Our account provides a local, realist and fully non-causal principle explanation for EPR correlations, contextuality, no-signalling, and the Tsirelson bound. Indeed, the account herein is fully consistent with the causal structure of Minkowski spacetime. We argue that retrocausal accounts of quantum mechanics are problematic precisely because they do not fully transcend the assumption that causal or constructive explanation must always be fundamental. Unlike retrocausal accounts, our principle explanation is a complete rejection of Reichenbach’s Principle. Furthermore, we will argue that the basis for our principle account of quantum mechanics is the physical principle sought by quantum information theorists for their reconstructions of quantum mechanics. Finally, we explain why our account is both fully realist and psi-epistemic. Full article
(This article belongs to the Special Issue Quantum Theory and Causation)
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24 pages, 165 KB  
Article
Information-Theoretic Inference of Common Ancestors
by Bastian Steudel and Nihat Ay
Entropy 2015, 17(4), 2304-2327; https://doi.org/10.3390/e17042304 - 16 Apr 2015
Cited by 43 | Viewed by 8264
Abstract
A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of [...] Read more.
A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version. Full article
(This article belongs to the Special Issue Information Processing in Complex Systems)
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