  The aim of this paper is to propose a theoretical construct, smart network
field theory, for the characterization, monitoring, and control of smart
network systems. Smart network systems are intelligent autonomously-operating
networks, a new form of global computational infrastructure that includes
blockchains, deep learning, and autonomous-strike UAVs. These kinds of
large-scale networks are a contemporary reality with thousands, millions, and
billions of constituent elements, and entail a foundational and
theoretically-robust model for their design and operation. Hence this work
proposes smart network field theory, drawing from statistical physics,
effective field theories, and model systems, for criticality detection and
fleet-many item orchestration in smart network systems. Smart network field
theory falls within the broader concern of technophysics (the application of
physics to the study of technology), in which a key objective is deriving
standardized methods for assessing system criticality and phase transition, and
defining interim system structure between the levels of microscopic noise and
macroscopic labels. The farther implications of this work include the
possibility of recasting the P/NP computational complexity schema as one no
longer based on traditional time (concurrency) and space constraints, due to
the availability of smart network computational resources.
