5.8 Decentralization Measurement

Navigating through the intricate layers of DGT networks presents a fascinating exploration into the world of blockchain and cryptocurrency. The dichotomy between decentralized (Boko 2002) and centralized models is prominently showcased in DGT’s consortium-based solution, meticulously crafted to harmonize the efficiency and scalability of centralized models with the trust and transparency of decentralized networks. In this chapter, we plunge into the depth of modeling DGT characteristics such as Node Distribution, Transaction Distribution, Token Distribution, and Consortium Impact, thereby unveiling the critical metrics that illuminate the network’s equilibrium in the context of decentralization.

DGT networks astutely reject the peer-to-peer model of node interaction, opting instead for the creation of clusters and embracing a hybrid network nature, intertwining private and public segments. This thoughtful design enhances communication efficiency and amplifies overall scalability, yet brings to light an essential query: How can decentralization be preserved amidst this efficiency? This question paves the way for the introduction of redundancy within verification mechanisms, particularly observable when achieving consensus. As we traverse this path, we encounter the necessity of meticulous calculations to justify such redundancy, extending beyond the conventional Nakamoto metric to embrace a multitude of considerations. These span across various network "forces", such as the distribution of computing power and tokens, creating a nuanced landscape where economic characteristics and the advantages of decentralized solutions precariously balance on the tightrope of strategic network planning.

Embarking on this journey, we acknowledge that not all nodes or user accounts can stand as independent economic agents within the network. Thus, our exploration is underscored by a pivotal quest: To discern the degree of dependency that not only preserves the network’s decentralized integrity (Zhang, Ma, and Liu 2023) but also nurtures its evolution and development in the burgeoning world of blockchain technology.

Below are the main calculated results according to the AB2023 model (Khvatov and Bogdanov 2023)