DGT DOCS
  • 1. INTRODUCTION
    • 1.1 Executive Summary
    • 1.2 Why DGT
    • 1.3 Distributed Ledgers Technology
      • 1.3.1 Decentralization approach
      • 1.3.2 Consensus Mechanism
      • 1.3.3 Transactions
      • 1.3.4 Layered Blockchain Architecture
      • 1.3.5 Tokenomics
      • 1.3.6 Web 3 Paradigm
      • 1.3.7 Common Myths about Blockchain
    • 1.4 The DGT Overview
      • 1.4.1 Platform Approach
      • 1.4.2 DGT Functional Architecture
      • 1.4.3 Technology Roadmap
    • 1.5 How to create a Solution with DGT Networks
    • 1.6 Acknowledgments
  • 2. REAL WORLD APPLICATIONS
    • 2.1 Case-Based Approach
      • 2.1.1 DGT Mission
      • 2.1.2 The Methodology
      • 2.1.3 Case Selection
    • 2.2 Supply Chain and Vertical Integration
      • 2.2.1 Logistics Solution for Spare Parts Delivery
      • 2.2.2 DGT Based Solution for Coffee Chain Products
    • 2.3 Innovative Financial Services
      • 2.3.1 Crowdfunding Platform
      • 2.3.2 Real World Assets Tokenization
      • 2.3.3 Virtual Neobank over DGT Network
      • 2.3.4 DGT based NFT Marketplace
    • 2.4 Decentralized Green Energy Market
      • 2.4.1 Peer To Peer Energy Trading
      • 2.4.2 DGT based Carbon Offset Trading
    • 2.5 B2B2C Ecosystems and Horizontal Integration
      • 2.5.1 KYC and User Scoring
      • 2.5.2 Decentralized Marketing Attribution
      • 2.5.3 Case Decentralized Publishing Platform
      • 2.5.4 Value Ecosystem
    • 2.6 More Cases
  • 3. DGT ARCHITECTURE
    • 3.1 Scalable Architecture Design
      • 3.1.1 High Level Architecture
      • 3.1.2 DGT Approach
      • 3.1.3 Unique contribution
      • 3.1.4 Component Based Architecture
    • 3.2 Performance Metrics
    • 3.3 Network Architecture
      • 3.3.1 Nework Architecture in General
      • 3.3.2 Network Identification
      • 3.3.3 H-Net Architecture
      • 3.3.4 Transport Level
      • 3.3.5 Segments
      • 3.3.6 Static and Dynamic Topologies
      • 3.3.7 Cluster Formation
      • 3.3.8 Node Networking
      • 3.3.9 Permalinks Control Protocol
    • 3.4 Fault-Tolerant Architecture
      • 3.4.1 Introduction to Fault Tolerance
      • 3.4.2 F-BFT: The Hierarchical Consensus Mechanism
      • 3.4.3 Cluster Based Algorithms
      • 3.4.4 Arbitrator Security Scheme
      • 3.4.5 Heartbeat Protocol
      • 3.4.6 Oracles and Notaries
      • 3.4.7 DID & KYC
    • 3.5 Transactions and Performance
      • 3.5.1 Transaction Basics
      • 3.5.2 Transaction Processing
      • 3.5.3 Transaction and block signing
      • 3.5.4 Transaction Families
      • 3.5.5 Transaction Receipts
      • 3.5.6 Smart Transactions
      • 3.5.7 Private Transactions
      • 3.5.8 Multi signature
    • 3.6 Data-Centric Model
      • 3.6.1 Data layer overview
      • 3.6.2 Global State
      • 3.6.3 Genesis Record
      • 3.6.4 Sharding
      • 3.6.5 DAG Synchronization
    • 3.7 Cryptography and Security
      • 3.7.1 Security Architecture Approach
      • 3.7.2 Base Cryptography
      • 3.7.3 Permission Design
      • 3.7.4 Key Management
      • 3.7.5 Encryption and Decryption
      • 3.7.6 Secure Multi Party Computation
      • 3.7.7 Cryptographic Agility
      • DGTTECH_3.8.4 Gateway Nodes
    • 3.8 Interoperability
      • 3.8.1 Interoperability Approach
      • 3.8.2 Relay Chain Pattern
      • 3.8.3 Virtual Machine Compatibility
      • 3.8.4 Gateway Nodes
      • 3.8.5 Token Bridge
    • 3.9 DGT API and Consumer Apps
      • 3.9.1 Presentation Layer
      • 3.9.2 Application Architecture
    • 3.10 Technology Stack
    • REFERENCES
  • 4. TOKENIZATION AND PROCESSING
    • 4.1 Introduction to Tokenization
      • 4.1.1 DGT Universe
      • 4.1.2 Driving Digital Transformation with Tokens
      • 4.1.3 Real-World Tokenization
      • 4.1.4 Key Concepts and Definitions
    • 4.2 Foundations of Tokenization
      • 4.2.1 Definition and Evolution of Tokenization
      • 4.2.2 Tokenization in the Blockchain/DLT Space
      • 4.2.3 The Tokenization Process
      • 4.2.4 Tokenization on the DGT Platform
      • 4.2.5 Regulatory and Legal Aspects of Tokenization
      • 4.2.6 Typical Blockchain-Based Business Models
    • 4.3 The DEC Transaction Family
      • 4.3.1 DEC Transaction Family Overview
      • 4.3.2 DEC Token Features
      • 4.3.3 DEC Token Protocol
      • 4.3.4 DEC Account Design
      • 4.3.5 DEC Transaction Family Flow
      • 4.3.6 DEC Commands
      • 4.3.7 DEC Processing
      • 4.3.8 Payment Gateways
    • 4.4 Understanding Secondary Tokens
      • 4.4.1 The different types of tokens supported by DGT
      • 4.4.2 How secondary tokens are produced
  • 5. EXPLORING TOKENOMICS
    • 5.1 Introduction
      • 5.1.1 What does tokenomics mean?
      • 5.1.2 Goals of Building the Model for DGT Network
      • 5.1.3 Tokens vs Digital Money
      • 5.1.4 The Phenomenon of Cryptocurrency
      • 5.1.5 Basic Principles of Tokenomics
      • 5.1.6 AB2023 Model
    • 5.2 Node & User Growth
      • 5.2.1 Node Ecosystem
      • 5.2.2 User Growth and Retention Modeling
    • 5.3 Transactions
      • 5.3.1 Transaction Amount Components
      • 5.3.2 Shaping the Transaction Profile: A Three-pronged Approach
      • 5.3.3 Calculation of Transaction Number
    • 5.4 Network Performance Simulation
      • 5.4.1 Endogenous Model
      • 5.4.2 Network Entropy
      • 5.4.3 Network Utility
    • 5.5 Token Supply Model
      • 5.5.1 Introduction to Supply and Demand Dynamics
      • 5.5.2 Token distribution
      • 5.5.3 Supply Protocol
      • 5.5.4 Token Balance and Cumulative Supply
    • 5.6 Token Demand Model
      • 5.6.1 Node-Base Demand
      • 5.6.2 Transaction-Based Token Demand
      • 5.6.3 Staking Part Modeling
      • 5.6.4 Total Demand
    • 5.7 Token Price Simulation
      • 5.7.1 Nelson-Siegel-Svensson model
      • 5.7.2 The Price Model
    • 5.8 Decentralization Measurement
      • 5.8.1 Active Node Index
      • 5.8.2 Node Diversity in Hybrid Networks
      • 5.8.3 Token distribution
      • 5.8.4 Integral Calculation of Decentralization Metric
    • 5.9 Aggregated Metrics
      • 5.9.1 Transaction Throughput: Evaluating Network Performance and Scalability
      • 5.9.2 Market Capitalization: A Dimension of Valuation in Cryptocurrency
      • 5.9.3 Total Value Locked (TVL): A Spotlight on Network Engagement and Trust
  • 6. ADMINISTRATOR GUIDE
    • 6.1 Introduction
      • 6.1.1 Administrator Role
      • 6.1.2 Platform sourcing
      • 6.1.3 DGT Virtualization
      • 6.1.4 Using Pre-Built Virtual Machine Images
      • 6.1.5 Server Preparation
      • 6.1.6 OS Setup and initialization
    • 6.2 DGT CORE: Single Node Setup
      • 6.2.1 Launch the First DGT Node
      • 6.2.2 Dashboard setup
      • 6.2.3 Nodes Port Configuration
      • 6.2.4 Single Node Check
    • 6.3 DGT CORE: Setup Private/Public Network
      • 6.3.1 Network launch preparation
      • 6.3.2 A Virtual Cluster
      • 6.3.3 A Physical Network
      • 6.3.4 Attach node to Existing Network
    • 6.4 DGT Dashboard
    • 6.5 DGT CLI and base transaction families
    • 6.6 GARANASKA: Financial Processing
      • 6.6.1 Overview of DGT’s financial subsystem
      • 6.6.2 DEC emission
      • 6.6.3 Consortium account
      • 6.6.4 User accounts
      • 6.6.5 Payments
    • 6.7 Adjust DGT settings
      • 6.7.1 DGT Topology
      • 6.7.2 Manage local settings
    • 6.8 DGT Maintenance
      • 6.8.1 Stopping and Restarting the Platform
      • 6.8.2 Backing up Databases
      • 6.8.3 Network Performance
      • 6.8.4 Log & Monitoring
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  • 5.1.6.1 Core Attributes of AB2023 Model
  • 5.1.6.2 Existing Approaches
  • 5.1.6.3 A Look Towards Future Developments and Challenges
  1. 5. EXPLORING TOKENOMICS
  2. 5.1 Introduction

5.1.6 AB2023 Model

Previous5.1.5 Basic Principles of TokenomicsNext5.2 Node & User Growth

Last updated 1 year ago

This paper ventures into an explorative discourse on the AB2023 model, a pivotal archetype in the present-day tokenomics model for the DGT Network, emphasizing its role as a representative of dependencies in lieu of absolute outcomes. It navigates through the manifold territories of decentralized networks, specifically illuminating the complex interlinkages within the tokenomics of a DGT-based consortium by considering both intrinsic and extrinsic characteristics.

5.1.6.1 Core Attributes of AB2023 Model

The main topics covered by the model are selected to form a holistic view of the behavior of economically independent agents in a decentralized hybrid environment:

  • Analytical Proficiency. AB2023 emerges as an analytical lens, meticulously examining pivotal features such as:

    o Node and user growth conceptualized as sovereign parameters.

    o Consortium's marketing strategy's vigor and its navigation through the decentralized network's turbulent milieu.

  • Dynamic Nuances and Network Aggression. The model skillfully explores:

    o Token value dynamics.

    o Equilibrium in token demand and supply.

    o Decentralized metrics behavior, all interwoven by a pre-ordained network aggression, which crafts a reflective tapestry of the network’s economic and collaborative vitality.

  • Modeling Prowess. A significant hallmark propelling AB2023 is its capability to model intrinsic elements like:

    o Network entropy.

    o Utility, easing the computational traversal through network behavioral patterns across a stipulated timeframe (set to a 100-month period in the model).

An In-depth Exploration Through AB2023:

  • Hybrid Blockchain Cluster Analysis: The model critically examines a hybrid blockchain cluster network from diverse perspectives, offering a multi-angular analysis that traverses through terrains of network growth and economic dynamism.

  • Techno-Economic Conduit: It plays a vital role as a conduit, melding digital conduits of technological adeptness with the vibrant heart of economic ramifications.

  • Exploring Fundamental Network Parameters: Spanning from an exhaustive study of fundamental network parameters like nodes, users, and transactions to probing into the economic foundation sculpted by token demand and supply dynamics, the model isn’t a predictor of the future but a meticulous research apparatus.

5.1.6.2 Existing Approaches

Tokenomics, a vital domain merging economics and technology, proliferates through numerous models to decipher the dynamics of tokens within digital and decentralized platforms. With a multitude of approaches ranging from pricing models to business guides, each tokenomic model establishes a unique narrative, emphasizing various aspects like valuation, verification, and application of tokens in diverse ecosystems. A comparative view of such models can be enlightening, paving the way for understanding the nuances and foci that each methodology entails. Subsequently, the information is consolidated in the following comparative table, providing an insightful snapshot into these diversified models, and introducing a new model - AB2023.

Model/Paper

Main Focus

Methodology

Key Features/Findings

Tokenomics: Dynamic Adoption and Valuation

(Cong, Li, and Wang 2021)

Pricing Model of Tokens

Dynamic Asset Pricing Model

- Equilibrium price determined by transactional demand from heterogeneous users

- Investigates impacts of network externality, platform growth, and token supply on adoption and token price.

Formalization and Algebraic Modeling of Tokenomics Projects

(Letychevskyi et al. 2019)

Verification and Simulation of Tokenomics

Algebraic Programming and Insertion Modeling

- Focus on formalizing tokenomics model using algebraic structures

- Utilizes Maple system for implementation

- Example model: SKILLONOMY project

Tokenomics: Decentralized Incentivization in the Context of Data Spaces

(Jürjens et al. 2022)

Business Application of Tokens

Comprehensive Guide and Case Studies

- Outlines various aspects: token types, functions, valuation, distribution, governance, and regulation

- Presents case studies of successful token projects and best practices

Blockchain Networks: Token Design and Management Overview

(Lesavre, Varin, and Yaga 2021)

Token Design and Management

Overview and Guidelines

- Provides an overview and guidelines related to the design and management of tokens on blockchain networks.

- Emphasizes secure, reliable, and effective token management practices.

- Addresses technical, governance, and business considerations in token systems.

AB2023 Model (Current Model)

(Khvatov and Bogdanov 2023)

Analyzing and Projecting Tokenomics Behavior

Hybrid Blockchain Economy Modeling

- Scrutinizes through diverse lenses, offering a multi-angular analysis navigating through network growth and economic dynamism.

- A conduit linking technological proficiency with economic impact, not as a future predictor but as a meticulous research tool.

- Reflects potential behavior and network performance through variable initial and terminal conditions.

5.1.6.3 A Look Towards Future Developments and Challenges

Although the AB2023 model encapsulates a robust analytical methodology, it's quintessential to acknowledge its nascent stage of development, underpinned by various assumptions and simplifications. Future trajectories involve juxtaposing model results against open data, refining parameters, and coefficients, and possibly introducing additional theoretical perspectives to enhance analytical depth.

In its quest to evolve, the model aspires to weave Artificial Intelligence techniques into its fabric, aiming for a sophisticated alignment with real-world data. It must be noted, however, that the deterministic nature of the model, coupled with certain assumptions like equal power and contribution of nodes, and a direct correlation between nodes and users, frames its limitations.

Figure 71. AB2023 Tokenomic Model Scope