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I have trouble understanding the mathematical arguments behind this view but I thought I would post it, in case anybody has any information/understanding/insights. The basic idea is that the mathematical assumptions on which the validity of Bell's inequality depends are that all the random variables are defined on a single probability space. These authors then go on to question this assumption using a Bohrian-type argument which they refer to as "the chameleon model". Note that this has nothing to do with questioning loopholes, etc. as they are suggesting that Bell’s argument fails even before the issue of these loopholes. Also, note, that they are not basing their arguments on the contextuality as per Kochen-Specker theorem, as they question the assumptions behind this theorem also. There are a number papers/books taking this perspective:
http://cds.cern.ch/record/445808/files/0007005.pdf
http://arxiv.org/pdf/quant-ph/0611259.pdf
http://dare.uva.nl/document/358619
Non-Kolmogorovian Approach to the Context-Dependent Systems Breaking the Classical Probability Law
http://link.springer.com/article/10.1007/s10701-013-9725-5
For anyone who has some understanding of Probability theory, do these Non-Kolmogorovian approaches/axioms seem reasonable/make sense?
Locality and Bell's inequalitySo: if you want to keep the inequality (1) you MUST realize all the random variables in (3) in the same probability space!
ii) the physicists never use statements such as "realizability on a single probability space" but they hide this mathematical assumption in their notations, i.e. they use the same symbol to denote the results of different mutually incompatible experiments.
iii) without the assumption: "realizability on a single probability space" (or one of the equivalent hidden formulations introduced in the physical literature), cannot prove Bell's inequality.
http://cds.cern.ch/record/445808/files/0007005.pdf
Recall the basic idea of the chameleon effect: the local dynamics influences the statistics and since the factorization of the dynamics (3:6), i.e. ((1;M1), (2;M2)), is different from the factorization of the state (3:7), i.e. ((1; 2); (M1;M2)), the result of the local interaction is a global dependence of the final state on the whole measurement setting, i.e. (a; b)...Such a deformation is perfectly compatible with the assumption of an 100 percent (ideal) efficiency of the detectors.
Chameleon effect, the range of values hypothesis and reproducing the EPR-Bohm correlationsIn particular, it was pointed out that the proof of Bell’s inequality is based on the implicit use of a single Kolmogorov probability space, see Accardi [7]–[9], Khrennikov [11]–[14], Hess and Philipp [17]. We can call such an assumption probabilistic non–contextuality. By probabilistic contextuality we understand dependence of probability on experimental settings...However, there exists a model in that probabilistic contextuality (i.e., dependence of probabilities on experimental settings) can be produced without losses of particles. Moreover, in that model probabilistic contextuality is not a consequence of the quantum contextuality and hence the model is local.
http://arxiv.org/pdf/quant-ph/0611259.pdf
Is the Contextuality Loophole Fatal for the Derivation of Bell Inequalities?In his opening address of the 2008 Växjö conference Foundations of Probability and Physics-5, Andrei Khrennikov took the position that violations of Bell inequalities [8] occur in Nature, but do not rule out local realism, due to lack of contextuality: the measurements needed to test Bell inequalities (BI) such as the BCHSH inequality cannot be performed simultaneously [9]. Therefore Kolmogorian probability theory starts and ends with having different probability spaces, and Bell inequality violation (BIV) just proves that there cannot be a reduction to one common probability space. This finally implies that no conclusion can be drawn on local realism, since incompatible information can not be used to draw any conclusion. As explained below,
the different pieces of the CHSH inequality involve fundamentally different distribution functions of the hidden variables, which cannot be put together in one over all covering distribution of all hidden variables of the set of considered experiments.
http://dare.uva.nl/document/358619
Non-Kolmogorovian Approach to the Context-Dependent Systems Breaking the Classical Probability Law
http://link.springer.com/article/10.1007/s10701-013-9725-5
For anyone who has some understanding of Probability theory, do these Non-Kolmogorovian approaches/axioms seem reasonable/make sense?