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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  1. 5. EXPLORING TOKENOMICS
  2. 5.8 Decentralization Measurement

5.8.3 Token distribution

Previous5.8.2 Node Diversity in Hybrid NetworksNext5.8.4 Integral Calculation of Decentralization Metric

Last updated 1 year ago

Token distribution is pivotal in ensuring a balanced and functional blockchain network, primarily when it entails a multitude of participants: consortiums, nodes, and users. Monitoring the dispersion of tokens enables the understanding of network dynamics and aids in making strategic decisions, typically employing measures like the Gini coefficient to ascertain equality or disparity in distribution.

Participant Categories:

  • Nodes:

o Nodes, accumulating tokens via minting, follow a predefined model where initially dominant nodes (often consortium-owned) with higher transaction processing rates, gradually make way for a diverse range of participants.

o Eventually, a handful of leader nodes become prominent, managing a substantial portion of network transactions, and therefore, accumulating a noteworthy quantity of tokens.

o Tokens are perpetually circulated to users by nodes, fostering an active economy within the network.

  • Users:

o Users maintain tokens, sometimes utilizing them in Decentralized Finance (DeFi) applications.

o The wealth among users, initially concentrated, dilutes over time, even while adhering roughly to a normal distribution.

  • Consortium: Initially holding 20% of tokens, the consortium progressively reduces its dominance by systematically distributing tokens as per a preset off-chain policy.

While the simplified approach of token distribution and usage of the Gini coefficient might lack the granular accuracy of alternative metrics like Theil indices, it presents a feasible means of understanding and making decisions based on visible trends, especially in resource-limited scenarios.

Transaction Reward Rate (TRR):

Transaction distribution is vital to ensure fairness and sustainability in a blockchain economic model. Nodes, categorized into "highly active" and "moderately active" based on their activity level, handle transaction validations.

  • Highly Active Nodes: Initially 100%, decreasing to 20% as the network evolves.

  • Moderately Active Nodes: Increasing in proportion conversely to the highly active nodes.

Transaction distribution adapts over time, shifting from initially all being processed by highly active nodes, to a more distributed model as the network matures. Introducing stochastic processes, like a normal distribution with Gaussian noise, simulates the intrinsic variability in transaction processing, reflecting a more accurate portrayal of a live blockchain network.

The TRR is integral to understanding the economic and operational dynamics within the blockchain network. Defined as the average number of tokens rewarded per transaction processed, it's formulated as:

This metric is pivotal as it encapsulates the economic motivation for nodes to engage in transaction processing, ensuring both the security and functionality of the network.

Nodes-Users Token Flow:

Understanding the flow and distribution of tokens among nodes and users is crucial in maintaining a balanced blockchain economy. Tokens are not perennially hoarded by nodes. A particular percentage is expended monthly, ensuring token circulation and economic vitality. Conversely, token accumulation occurs, indicative of wealth accumulation by nodes for their network services. Users acquire tokens directly from the consortium and through node transactions. Visualizing this dynamic over time, our model calculates and plots token balances for nodes and users monthly, providing insights into the network's economic health and viability.

  • Nodes verify transactions and are rewarded through a minting mechanism, determined by the TRR. They spend a certain percentage of their token holdings monthly, contributing to the network's economic liquidity.

  • Users acquire tokens both directly from the consortium and via transactions with nodes.

Visualizing the dynamic interplay between nodes and users, as well as comprehending the ensuing token flow, is imperative for evaluating the health and long-term viability of a blockchain network. This model underscores the economic interplay in the network, providing a roadmap for understanding and potentially navigating the economic phenomena within the blockchain environment.

Consortium-Users Token Flow:

The flow between consortium and users is another vital aspect to explore, as it reveals how the initial token distribution strategy and subsequent allocations unfold in the practical functioning of the network. A detailed exploration would consider parameters like pre-established distribution policies, transactional dynamics, and behavioral economics of users in token utilization and storage, providing a comprehensive overview of token movement and accumulation in this subset of the network.

User's token distribution:

User token distribution may vary significantly based on several factors, including user behavior, incentives, and external market conditions. Ensuring an equitable and functional distribution among users requires insight into these dynamics, potentially involving modeling around user acquisition, spending, and holding patterns.

An in-depth analysis would further delve into user categorizations, exploring token utilization patterns across different user Within the framework of the model, the target characteristic is set for the gradual equalization of token ownership with reaching a normal distribution after a specified period of time t* (10 months by default). demographics and behavioral profiles.

TDI Practical Calculation:

The alternative algorithm provided for calculating Token Distribution Inequality (TDI) bypasses the complexity inherent in traditional measures like GINI or Theil indices. By calculating a ratio between tokens held by the lower and upper percentiles of holders, a straightforward, comprehensible insight into wealth disparity within token holders is obtained.

Using a deterministic approach, the algorithm presumes a known evolution of token distribution among users, transitioning from 10% of users holding 90% of tokens initially to 40% holding 60% ultimately. While it might not encapsulate every nuance of the token distribution process, this method furnishes a simplified overview of distribution dynamics, facilitating intuitive understanding and informed decision-making.

Where:

o X is a predefined lower percentile of token holders, e.g., the bottom 10%.

o Y is a predefined upper percentile of token holders, e.g., the top 10%.

To calculate the TDI:

  1. Rank all token holders based on the number of tokens they possess.

  2. Calculate the total number of tokens held by the bottom X of the ranked list.

  3. Calculate the total number of tokens held by the top Y of the ranked list.

  4. Divide the sum from step 2 by the sum from step 3 to get the TDI.

Consortium Influence Index (CII):

The Consortium Influence Index (CII) elucidates the consortium's token possession relative to the total tokens in the network, expressed as:

This index becomes crucial in evaluating the consortium's control and influence within the network. A higher CII indicates a more significant consortium influence on the network's economic dynamics and, potentially, its governance structures. Continuous monitoring and ethical management of the CII are essential to maintain network trust and stability.

Figure 98 Token distribution over time
Figure 99 Distribution of Transactions over Nodes
Figure 100 Token rewards (SLA) over time
Figure 101 Tokens help by all Nodes and All Users per Month
Figure 102 Consortium to Users Token Flow
Figure 103 Token distribution among users in month 10
Figure 104 TDI Index over time
Figure 105 Consortium Influence Index over Time