A Computable Piece of Uncomputable Art whose Expansion May Explain the Universe in Software Space
This addresses the inverse problem in computational epistemology, potentially offering a new method for scientific discovery, though it appears incremental as an application of existing frameworks.
The paper tackles the inverse problem of finding causes and generative models from data by exploring software space using Algorithmic Information Dynamics, claiming this approach can advance scientific discovery with complementary tools.
At the intersection of what I call uncomputable art and computational epistemology, a form of experimental philosophy, we find an exciting and promising area of science related to causation with an alternative, possibly best possible, solution to the challenge of the inverse problem. That is the problem of finding the possible causes, mechanistic origins, first principles, and generative models of a piece of data from a physical phenomenon. Here we explain how generating and exploring software space following the framework of Algorithmic Information Dynamics, it is possible to find small models and learn to navigate a sci-fi-looking space that can advance the field of scientific discovery with complementary tools to offer an opportunity to advance science itself.