PIRSA:15050080

Bringing General Relativity into the Operational Probabilistic Framework

APA

Hardy, L. (2015). Bringing General Relativity into the Operational Probabilistic Framework. Perimeter Institute for Theoretical Physics. https://pirsa.org/15050080

MLA

Hardy, Lucien. Bringing General Relativity into the Operational Probabilistic Framework. Perimeter Institute for Theoretical Physics, May. 12, 2015, https://pirsa.org/15050080

BibTex

          @misc{ scivideos_PIRSA:15050080,
            doi = {10.48660/15050080},
            url = {https://pirsa.org/15050080},
            author = {Hardy, Lucien},
            keywords = {Mathematical physics, Quantum Foundations, Quantum Gravity, Quantum Information},
            language = {en},
            title = {Bringing General Relativity into the Operational Probabilistic Framework},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2015},
            month = {may},
            note = {PIRSA:15050080 see, \url{https://scivideos.org/pirsa/15050080}}
          }
          

Lucien Hardy Perimeter Institute for Theoretical Physics

Talk numberPIRSA:15050080

Abstract

I will discuss my work (in progress) to formulate General Relativity as an operational theory which includes probabilities and also agency (knob settings). The first step is to find a way to discuss operational elements of GR. For this I adapt an approach due to Westman and Sonego. I assert that all directly observable quantities correspond to coincidences in the values of scalar fields. Next we need to include agency. Usually GR is regarded as a theory in which a solution is simply stated for all space and time (the Block Universe view). Here, instead, we find a way to treat agents as making choices. Finally, we need to incorporate probabilities. For this purpose we take a compositional point of view. We associate a generalized state with regions of the space consisting of coincidences in the values of scalars. We then show how to combine these generalized states to make probabilistic predictions using the duotensor machinery developed previously.