Algorithmic Idealism I: Reconceptualizing Reality Through Information and Experience

TL;DR

Algorithmic idealism proposes a new way of understanding reality based on informational structures and self-state transitions, challenging traditional views and addressing fundamental questions in physics and metaphysics.

Contribution

It introduces a unified, information-centric framework that redefines reality through algorithmic transitions, integrating insights from quantum mechanics, cosmology, and philosophy.

Findings

Provides a solution to the measurement problem

Addresses the Boltzmann brain paradox

Offers epistemic interpretations of physical theories

Abstract

Algorithmic idealism represents a transformative approach to understanding reality, emphasizing the informational structure of self-states and their algorithmic transitions over traditional notions of an external, objective universe. Rooted in algorithmic information theory, it redefines reality as a sequence of self-state transitions governed by principles such as Solomonoff induction. This framework offers a unified solution to longstanding challenges in quantum mechanics, cosmology, and metaphysics, addressing issues like the measurement problem, the Boltzmann brain paradox, and the simulation hypothesis. Algorithmic idealism shifts the focus from describing an independent external world to understanding first-person experiences, providing epistemic interpretations of physical theories and dissolving metaphysical divides between "real" and simulated realities. Beyond resolving these conceptual challenges, it raises profound ethical questions regarding the continuity, duplication, and termination of informational entities, reshaping discussions on identity, consciousness, and existence in the digital and quantum age. By offering a mathematically rigorous yet philosophically innovative framework, algorithmic idealism invites a rethinking of reality as an emergent property of informational dynamics rather than a static external construct.