Preliminary Concepts

This chapter presents some of the basic concepts required to describe the Internet. The Internet has to meet functional specifications, but it also needs to work as a data transportation network. The concept of a network is introduced, because the Internet is a network of computer networks, controlled by the Internet Protocol Suite TCP/IP. The value of a network increases as the number of members, and the number of connections between them, increase, and a network is more efficient at communication than a hierarchy. Metcalfe’s Law, quantifying positive network effects, is introduced. Fragmentation is defined as incompatibility of technical standards across different parts of the Internet. The Internet transports data, defined as uninterpreted symbols, which is contrasted with information, defined as interpreted data. Finally, digital modernity is introduced as the evolution of modernity driven by networks and data, disrupting offline norms, processes, and institutions.

Malware and market share: a comment on Arce

Arce (Malware and market share. J Cybersecur 2018;4:tyy010) presents a game-theoretic model in which users select and hackers target one of two IT platforms based upon the platforms’ network benefits and security levels. Unfortunately, in modelling the network benefits, Arce misinterprets Metcalfe’s law. In particular, he assumes that the utility that a user obtains from a platform increases quadratically with the number of users, whereas Metcalfe’s law holds that utility increases linearly with network size.

Are Bitcoin bubbles predictable? Combining a generalized Metcalfe’s Law and the Log-Periodic Power Law Singularity model

We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analysing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe’s Law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel’s back) is exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways Bitcoin prices ahead (from the time of writing, March 2018).