Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference

• Autor(a) principal: Janzing, Dominik
• Data de Publicação: 2016
• Outros Autores: Araújo, Rafael Chaves Souto, Schölkopf, Berhnard
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/handle/123456789/30398
• topic: Arrow of time; Causal inference; Kolmogorov complexity; Physical entropy; Algorithmic randomness

Wepostulate a principle stating that the initial condition of a physical system is typically algorithmically independent of the dynamical law.Wediscuss the implications of this principle and argue that they link thermodynamics and causal inference. On the one hand, they entail behavior that is similar to the usual arrow of time. Onthe other hand, they motivate a statistical asymmetry between cause and effect that has recently been postulated in the field of causal inference, namely, that the probability distribution Pcause contains no information about the conditional distribution Peffect cause and vice versa, while Peffect may contain information about Pcause effect