Relating causal and probabilistic approaches to contextuality. (16th September 2019)
- Record Type:
- Journal Article
- Title:
- Relating causal and probabilistic approaches to contextuality. (16th September 2019)
- Main Title:
- Relating causal and probabilistic approaches to contextuality
- Authors:
- Jones, Matt
- Abstract:
- Abstract : A primary goal in recent research on contextuality has been to extend this concept to cases of inconsistent connectedness, where observables have different distributions in different contexts. This article proposes a solution within the framework of probabi- listic causal models, which extend hidden-variables theories, and then demonstrates an equivalence to the contextuality-by-default (CbD) framework. CbD distinguishes contextuality from direct influences of context on observables, defining the latter purely in terms of probability distributions. Here, we take a causal view of direct influences, defining direct influence within any causal model as the probability of all latent states of the system in which a change of context changes the outcome of a measurement. Model-based contextuality (M-contextuality) is then defined as the necessity of stronger direct influences to model a full system than when considered individually. For consistently connected systems, M-contextuality agrees with standard contextuality. For general systems, it is proved that M-contextuality is equivalent to the property that any model of a system must contain 'hidden influences', meaning direct influences that go in opposite directions for different latent states, or equivalently signalling between observers that carries no information. This criterion can be taken as formalizing the 'no-conspiracy' principle that has been proposed in connection with CbD. M-contextuality is then proved toAbstract : A primary goal in recent research on contextuality has been to extend this concept to cases of inconsistent connectedness, where observables have different distributions in different contexts. This article proposes a solution within the framework of probabi- listic causal models, which extend hidden-variables theories, and then demonstrates an equivalence to the contextuality-by-default (CbD) framework. CbD distinguishes contextuality from direct influences of context on observables, defining the latter purely in terms of probability distributions. Here, we take a causal view of direct influences, defining direct influence within any causal model as the probability of all latent states of the system in which a change of context changes the outcome of a measurement. Model-based contextuality (M-contextuality) is then defined as the necessity of stronger direct influences to model a full system than when considered individually. For consistently connected systems, M-contextuality agrees with standard contextuality. For general systems, it is proved that M-contextuality is equivalent to the property that any model of a system must contain 'hidden influences', meaning direct influences that go in opposite directions for different latent states, or equivalently signalling between observers that carries no information. This criterion can be taken as formalizing the 'no-conspiracy' principle that has been proposed in connection with CbD. M-contextuality is then proved to be equivalent to CbD-contextuality, thus providing a new interpretation of CbD-contextuality as the non-existence of a model for a system without hidden direct influences. This article is part of the theme issue 'Contextuality and probability in quantum mechanics and beyond'. … (more)
- Is Part Of:
- Philosophical transactions. Volume 377:Number 2157(2019)
- Journal:
- Philosophical transactions
- Issue:
- Volume 377:Number 2157(2019)
- Issue Display:
- Volume 377, Issue 2157 (2019)
- Year:
- 2019
- Volume:
- 377
- Issue:
- 2157
- Issue Sort Value:
- 2019-0377-2157-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-09-16
- Subjects:
- contextuality -- probabilistic causal models -- contextuality-by-default -- direct influence
Physical sciences -- Periodicals
Engineering -- Periodicals
Mathematics -- Periodicals
500 - Journal URLs:
- https://royalsocietypublishing.org/loi/rsta ↗
- DOI:
- 10.1098/rsta.2019.0133 ↗
- Languages:
- English
- ISSNs:
- 1364-503X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library STI - ELD Digital store
- Ingest File:
- 11779.xml