Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism
This is a conceptual contribution and novel framework that could help address causation challenges in complex data analysis, complementing statistical approaches.
The paper introduces an unsupervised, parameter-free approach based on algorithmic probability to decompose complex data into its most likely algorithmic generative sources, demonstrating its ability to deconvolve interacting mechanisms in strings, images, and networks with numerical evidence from discrete dynamical systems.
Complex data usually results from the interaction of objects produced by different generating mechanisms. Here we introduce a universal, unsupervised and parameter-free model-oriented approach, based upon the seminal concept of algorithmic probability, that decomposes an observation into its most likely algorithmic generative sources. Our approach uses a causal calculus to infer model representations. We demonstrate its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are strings, space-time evolution diagrams, images or networks. While this is mostly a conceptual contribution and a novel framework, we provide numerical evidence evaluating the ability of our methods to separate data from observations produced by discrete dynamical systems such as cellular automata and complex networks. We think that these separating techniques can contribute to tackling the challenge of causation, thus complementing other statistically oriented approaches.