Smart Network Field Theory: The Technophysics of Blockchain and Deep Learning

TL;DR

This paper introduces smart network field theory, a physics-inspired framework for understanding, monitoring, and controlling large-scale autonomous networks like blockchains and deep learning systems, with implications for computational complexity.

Contribution

It proposes a novel theoretical model based on physics principles to analyze and manage complex smart network systems, bridging microscopic noise and macroscopic behavior.

Findings

Framework for criticality detection in smart networks

Method for orchestrating large-scale autonomous systems

Potential to redefine computational complexity paradigms

Abstract

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.