Causal Computation by Physical Fields: Electronic Realization and Narrative Interpretation

This paper introduces causal computation as a third paradigm of computation, distinct from symbolic digital computation and probabilistic quantum computation. Computation is defined not as a sequence of logical operations, but as the physical relaxation of a dissipative field toward stable causal attractors under imposed constraints. We provide a precise definition of a computing field as a physical system whose autonomous dynamics minimize a global functional and converge toward invariant states, each corresponding bijectively to a solution of a well-defined problem. In this framework, measurement does not perform computation; it merely reveals which invariant state has already been selected by physical evolution. The paper presents a realizable electronic architecture based on networks of coupled electromagnetic oscillators implementing an Ising-like energy functional. Dissipation ensures convergence, while phase-locking encodes binary causal states. The resulting system functions as a physical constraint solver rather than an algorithmic machine. A narrative interpretation is developed to clarify the conceptual shift from instruction-execution models to constraint-relaxation dynamics. Computation is reframed as an irreversible causal process structured by correction, expansion, and dissipation, forming a closed causal cycle. This work establishes causal computation as a physically grounded model of problem-solving, with implications for alternative computing architectures, optimization hardware, and the interpretation of measurement in physics.