Jordan Santana

Contemporary consciousness science confronts a persistent emergence problem: microscopic descriptions are often insufficient for explanatory coherence, yet macroscopic explanatory variables are frequently introduced heuristically rather than derived systematically from underlying dynamics. While recent developments in causal emergence provide quantitative tools for evaluating relationships between microscopic and macroscopic descriptions, a general framework for constructing informative macroscopic descriptions remains underdeveloped. Here, we explore a data-driven approach to theory construction in emergent systems. Drawing on recent work in contextual emergence, causal emergence, and statistical physics, we investigate how candidate macroscopic variables can be identified directly from microscopic dynamics according to criteria of informational efficiency, predictability, and causal efficacy. As a fully tractable pedagogical example, we analyze the two-spin Ising model under Glauber dynamics across parameter space (J,h,T). Several competing macroscopic descriptions—including magnetization, staggered magnetization, and alignment variables—are evaluated using three complementary perspectives: (1) static informational efficiency derived from equilibrium distributions, (2) Effective Information computed from exact interventional transition dynamics, and (3) observational causal-emergence metrics evaluated on simulated trajectories. Across distinct dynamical regimes, different macroscopic variables emerge as preferential descriptions according to these criteria, illustrating how informative macroscopic representations can be identified directly from the statistical and dynamical structure of a system. Rather than advancing a specific theory of consciousness, this work addresses a more foundational problem relevant to consciousness science broadly: how principled macroscopic explanatory vocabularies may be constructed from complex microscopic systems. The results suggest that information-theoretic and causal approaches to emergence may provide a systematic basis for identifying explanatory scales in physical and biological systems.

Jordan Santana is a Physics PhD student at the California Institute of Technology. After earning a BS in Physics from MIT, he spent five years at Moderna developing high-throughput imaging assays, quantitative image analysis pipelines, and mathematical models of intracellular drug delivery. His current research explores how informative macroscopic descriptions can be systematically constructed from microscopic dynamics using ideas from statistical physics, information theory, and quantitative emergence. More broadly, he is interested in theory construction, multiscale organization, and the emergence of higher-level scientific descriptions in physical and biological systems.