Substrate-Flexible Informational Quantum Mechanics: QBism, RQM, and the Question of Non-Human Epistemic Systems

Abstract

The QBist and Relational Quantum Mechanics (RQM) informational readings of quantum theory have long lacked a clear account of what kind of physical system qualifies as an “agent” or “observer.” Fuchs, Mermin, and Schack often treat the agent as a human physicist or Bayesian decision-maker; Rovelli suggests that any physical system may serve as a relational observer; Healey abstracts the agent into a Bayesian role without specifying its substrate; and the Khrennikov-Schack-Zwirn exchange sharpens, but does not resolve, the intersubjectivity problem. This paper argues that the QBism/RQM informational reading is best understood as substrate-flexible: any physical system whose input-output statistics exhibit quantum-probability structure, including non-trivial Contextuality-by-Default (CbD) signatures resistant to simplex-embeddable ontological models, is a candidate epistemic agent. This condition is necessary but not sufficient: agency also requires state-sensitive updating across measurement contexts. The argument preserves QBism’s rejection of view-from-nowhere objectivity while removing the assumption that agents must be human or conscious, and disciplines Rovelli’s “any physical system” claim by tying observerhood to measurable non-triviality. Engineered cortical wetware systems such as CL1 and DishBrain offer controllable, non-human, and not clearly conscious testbeds for evaluating this claim, without requiring phenomenal consciousness or quantum-coherent biological dynamics. The paper distinguishes substrate flexibility from Pienaar’s extension of the QBist agent, which expands the agent’s senses rather than its substrate; engages neo-Everettian objections; and rejects Edwards’s attempts to fold classical large language models into QBist formalism. Classical AI token-generation systems remain simplex-embeddable and therefore fail the non-triviality requirement. Substrate flexibility is thus narrower than proposals that include current AI systems.