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.
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.
Abstract I argue for an approach to the Foundations of Physics that puts the question in the title center stage, rather than asking “what is the case in the world?”. This approach, algorithmic idealism, attempts to give a mathematically rigorous in-principle -answer to this question both in the usual empirical regime of physics and in some more exotic regimes within cosmology, philosophy, and science fiction (but soon perhaps real) technology. I begin by arguing that quantum theory, in its actual practice and in some interpretations, should be understood as telling an agent what they should expect to observe next (rather than what is the case), and that the difficulty of answering this former question from the usual “external” perspective is at the heart of persistent enigmas such as the Boltzmann brain problem, extended Wigner’s friend scenarios, Parfit’s teletransportation paradox, or our understanding of the simulation hypothesis. Algorithmic idealism is a conceptual framework, based on two postulates that admit several possible mathematical formalizations, cast in the language of algorithmic information theory. Here I give a non-technical description of this view and show how it dissolves the aforementioned enigmas: for example, it claims that you should never bet on being a Boltzmann brain regardless of how many there are, that shutting down computer simulations does not generally terminate its inhabitants, and it predicts the apparent embedding into an objective external world as an approximate description.
According to our current conception of physics, any valid physical theory is supposed to describe the objective evolution of a unique external world. However, this condition is challenged by quantum theory, which suggests that physical systems should not always be understood as having objective properties which are simply revealed by measurement. Furthermore, as argued below, several other conceptual puzzles in the foundations of physics and related fields point to limitations of our current perspective and motivate the exploration of an alternative: to start with the first-person (the observer) rather than the third-person perspective (the world).In this work, I propose a rigorous approach of this kind on the basis of algorithmic information theory. It is based on a single postulate: that universal induction determines the chances of what any observer sees next. That is, instead of a world or physical laws, it is the local state of the observer alone that determines those probabilities. Surprisingly, despite its solipsistic foundation, I show that the resulting theory recovers many features of our established physical worldview: it predicts that it appears to observers as if there was an external world that evolves according to simple, computable, probabilistic laws. In contrast to the standard view, objective reality is not assumed on this approach but rather provably emerges as an asymptotic statistical phenomenon. The resulting theory dissolves puzzles like cosmology's Boltzmann brain problem, makes concrete predictions for thought experiments like the computer simulation of agents, and suggests novel phenomena such as ``probabilistic zombies'' governed by observer-dependent probabilistic chances. It also suggests that some basic phenomena of quantum theory (Bell inequality violation and no-signalling) might be understood as consequences of this framework.
This work introduces Algorithmic Idealism a framework that reinterprets quantum mechanics as a computational process governed by algorithmic probability informational simplicity and utility optimization Reality is modeled as an agentenvironment feedback loop where agents maximize utility by predicting selfstate transitions Core quantum phenomena such as measurement entanglement and probabilities are explained through informational constructs The paper presents a set of quantum mechanics postulates inspired by this framework and explains their mathematical meaning in detail These postulates include measurement as Bayesian updating entanglement as joint utility optimization and quantum probabilities as utilityweighted predictions Physical laws are shown to emerge as informational regularities from algorithmic constraints By defining identity through decisionmaking consistency and treating simulated and base realities as indistinguishable the framework also resolves philosophical questions about identity and reality This approach unifies quantum mechanics with computational theory offering a novel perspective on foundational physics and its connection to information and computation
The hard problem in consciousness is the problem of understanding how physical processes in the brain could give rise to subjective conscious experience. In this paper, I suggest that in order to understand the relationship between consciousness and the physical world, we need to probe deeply into the nature of physical reality. This leads us to quantum physics and to a second explanatory gap: that between quantum and classical reality. I will seek a philosophical framework that can address these two gaps simultaneously. Our analysis of quantum mechanics will naturally lead us to the notion of a hidden reality and to the postulate that consciousness is an integral component of this reality. The framework proposed in the paper provides the philosophical underpinnings for a theory of consciousness while satisfactorily resolving the interpretation problem in quantum mechanics without the need to alter its mathematical structure. I also discuss some implications for a scientific theory of consciousness.
Modal idealism is a Theory of Everything, based on metaphysical abstractions of the physical principles of hidden symmetries, entanglement, and quantum field theory, considered in the context of the Many Worlds Interpretation of quantum mechanics. These abstractions are used to extend the scope of existing philosophical positions on idealism, consciousness and possible world semantics, to rationally explain the fundamental mysteries of our existence. While it conceptually aligns with the Many Minds Interpretation of quantum mechanics, modal idealism posits a more comprehensive characterization of the mind, and thereby addresses many of the objections to MMI and MWI. Consequently, it can provide unequivocal, logical answers to our most enduring existential questions. To demonstrate the explanatory power of modal idealism, this article will present seven of our most meaningful physical and metaphysical questions, enumerate the principles of this framework, and use them to rationally answer these questions.
The Informational Observer proposes a unified informational framework in which perception, measurement, and experienced reality emerge from the compression and reconstruction processes performed by observers. Instead of treating reality as an external, fixed entity, the model defines reality as the intersection of informational invariants preserved across multiple independent observers. Each biological, artificial, or simulated observer applies a compression operator to the underlying informational field and reconstructs an internal model shaped by priors, limitations, and energetic constraints.This framework explains perceptual variability across species, hallucinations in artificial and biological systems, observer-dependent measurement, and stability of physical laws. The model integrates cognitive science, AI interpretability, quantum contextuality, and simulation theory into a single informational principle, expanding the author’s Informational State Dynamics (ISD) framework. It introduces a general mechanism capable of unifying cross-domain phenomena through informational transformations. This work introduces The Informational Observer, a theoretical framework describing perception, measurement, and reality as the result of informational transformations performed by observing systems. The observer is conceptualized not as a passive receiver of sensory input, but as a compression system that selectively reduces the informational field into internal representations and reconstructs coherent experiential states. The model formalizes this process through two operators: Cᵢ (Compression): reduction of the informational field Ψᵢ (Reconstruction): predictive reconstruction of the compressed state Reality is defined as the set of informational invariants found in the intersection of reconstructions produced by different observers: Reality = ⋂ Ψᵢ(Cᵢ(I₀)) This perspective unifies phenomena that traditionally belong to different scientific domains: perceptual variability (human, animal, artificial systems) hallucinations and confabulation sensory filtering and cognitive priors observer-dependent physics and measurement nested or simulated universes informational emergence of the “self” The Informational Observer extends the author’s Informational State Dynamics (ISD) model and provides a foundation for future research in AI interpretability, cognitive modeling, neuroscientific theory, and information-based physics.