Physics Is Pointing Inexorably to Mind

Couple related papers by physicist Markus Muller of the Institute for Quantum Optics and Quantum Information (IQOQI):

Could the physical world be emergent instead of fundamental, and why should we ask? (short version)

In physics, there is the prevailing intuition that we are part of a unique external world, and that the goal of physics is to understand and describe this world. This assumption of the fundamentality of objective reality is often seen as a major prerequisite of any kind of scientific reasoning. However, here I argue that we should consider relaxing this assumption in a specific way in some contexts. Namely, there is a collection of open questions in and around physics that can arguably be addressed in a substantially more consistent and rigorous way if we consider the possibility that the first-person perspective is ultimately more fundamental than our usual notion of external world. These are questions like: which probabilities should an observer assign to future experiences if she is told that she will be simulated on a computer? How should we think of cosmology's Boltzmann brain problem, and what can we learn from the fact that measurements in quantum theory seem to do more than just reveal preexisting properties? Why are there simple computable laws of physics in the first place? This note summarizes a longer companion paper which constructs a mathematically rigorous theory along those lines, suggesting a simple and unified framework (rooted in algorithmic information theory) to address questions like those above. It is not meant as a "theory of everything" (in fact, it predicts its own limitations), but it shows how a notion of objective external world, looking very much like our own, can provably emerge from a starting point in which the first-person perspective is primary, without apriori assumptions on the existence of "laws" or a "physical world". While the ideas here are perfectly compatible with physics as we know it, they imply some quite surprising predictions and suggest that we may want to substantially revise the way we think about some foundational questions.

Law without law: from observer states to physics via algorithmic information theory

According to our current conception of physics, any valid physical theory is assumed to describe the objective evolution of a unique external world. However, this assumption 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 possible limitations of our current perspective and motivate the exploration of alternatives. Thus, in this paper, I propose such an alternative approach (related to Solomonoff induction) which starts with a (rigorously formalized) concept of "observer state" as its primary notion, and does not from the outset assume the existence of a "world" or physical laws. Using tools from algorithmic information theory, I show that the resulting theory predicts, as a consequence of this, that it appears to observers as if there is a world that evolves according to algorithmically 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 involving the duplication and computer simulation of observers, and predicts novel phenomena such as "probabilistic zombies" governed by observer-dependent probabilistic chances. It identifies some phenomena of quantum theory (Bell inequality violation and no-signalling) as typical consequences of information-theoretic features of an agent's memory, and suggests that we shift our attention in the foundations of quantum mechanics from "what is really going on?" to questions about algorithms, causality and computational models.

I'll have to revisit the math at some point when I've reviewed the underlying subjects but the papers were still interesting.

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