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Examines individuation based on environmental settings and agency in artificial agents, suggesting key insights for consciousness frameworks.
Whether two instances of an artificial system are individuals or merely configurations of one system is usually asserted rather than measured. We introduce a controlled transplant assay that answers it empirically: a component is moved from one instance into another, and the resulting performance change is compared against a self-transplant control. If foreign components cost more than native ones, the components were adapted to their own instance, and the instances are not interchangeable. Applying this assay to a 2×2 design (shared vs. divergent environment × passive perception vs. closed sensorimotor loop; n = 20 replicates per cell), we find that environmental divergence is necessary for individuation — agents in a shared environment score exactly zero regardless of agency — while a closed action loop amplifies individuation by 1.61× when environments differ (d = 0.96, p = .005) and produces none on its own (p = .359). We further show that individuation is localised: transplanting components separately, the two that touch the environment carry all of it (64% predictor, 36% policy) while purely internal components carry exactly zero in every replicate; that it saturates, showing no growth between 200 and 1200 cycles of life (p = .26); and that numerically distinct states need not be individuated at all, since same-world agents whose weights differ substantially score zero because their states remain functionally interchangeable. We report a failed replication, a mechanism that did not work, and a measurement confound discovered and corrected during this work — including a version of the assay that scored +0.081 on bit-identical agents. The assay, the environment, and the analysis are released in full. We make no claim about phenomenal consciousness; individuation is a distinct and measurable property, and it bears on the agency and embodiment indicators in current consciousness-assessment frameworks.
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Junai P. Felix (2026) studied this question.
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