Table of Contents
1. Introduction
Technological progress has historically enabled the development of new forms of money with novel and enhanced properties. The digital era has introduced numerous non-physical currencies including demand deposits, cryptocurrencies, stablecoins, central bank digital currencies (CBDCs), in-game currencies, and quantum money. These forms of money possess properties that were not extensively studied in traditional economics literature but are crucial determinants of the monetary equilibrium in the forthcoming era of heightened currency competition.
Digital Payment Adoption
89%
of transactions in Sweden are digital
CBDC Development
130+
central banks researching digital currencies
2. Historical Framework of Money Properties
2.1 Traditional Properties of Physical Money
The classical properties of money were originally identified by Jevons (1875) and Menger (1892) for physical currencies. These include:
- Durability: Ability to withstand physical degradation
- Portability: Ease of transportation and transfer
- Divisibility: Capacity to be divided into smaller units
- Uniformity: Standardization of units
- Limited Supply: Scarcity to maintain value
- Acceptability: Wide recognition as medium of exchange
2.2 Limitations of Classical Framework
The traditional framework fails to adequately describe digital currencies, as it doesn't account for properties like:
- Programmability through smart contracts
- Censorship resistance
- Transaction finality
- Throughput and latency
- Cryptographic security guarantees
3. Digital Currency Properties Framework
3.1 Technical Properties
Digital currencies introduce novel technical properties that fundamentally change how money functions:
- Throughput: Transactions per second (TPS) capacity
- Latency: Transaction confirmation time
- Finality: Irreversibility of transactions
- Censorship Resistance: Ability to resist third-party interference
- Smart Contract Programmability: Automated execution of contractual terms
3.2 Economic Properties
Economic properties specific to digital currencies include:
- Interest-bearing capabilities
- Automated monetary policy implementation
- Micro-transaction feasibility
- Cross-border transaction efficiency
3.3 Regulatory and Societal Properties
Modern currencies must balance competing societal objectives:
- Privacy vs. Transparency
- Accessibility vs. Security
- Innovation vs. Stability
- Decentralization vs. Regulatory compliance
4. Technical Implementation and Analysis
4.1 Mathematical Foundations
The security of digital currencies relies on cryptographic primitives. For quantum money, the no-cloning theorem provides fundamental security:
$|\psi\rangle \rightarrow |\psi\rangle \otimes |\psi\rangle$ is impossible for unknown quantum states
The unforgeability of quantum money can be expressed as:
$Pr[Verify(\$_{quantum}) = 1 | \$_{quantum} \notin Valid] \leq \epsilon(\lambda)$
where $\epsilon(\lambda)$ is negligible in the security parameter $\lambda$.
4.2 Experimental Results
The paper presents comparative analysis of different currency types across multiple properties. Key findings include:
Figure 1: Property Comparison Across Currency Types
The experimental results show that no single currency type excels across all properties. CBDCs demonstrate strong regulatory compliance but limited programmability, while cryptocurrencies excel in censorship resistance but face scalability challenges. Quantum money, while theoretically superior in unforgeability, remains technically infeasible for practical implementation.
| Currency Type | Throughput (TPS) | Latency (s) | Censorship Resistance | Regulatory Compliance |
|---|---|---|---|---|
| Cash | N/A | 0 | High | Low |
| Bank Deposits | 1000-5000 | 1-3 | Low | High |
| Bitcoin | 7 | 600 | High | Low |
| Ethereum | 15-30 | 15 | Medium | Medium |
4.3 Code Implementation Examples
Below is a simplified smart contract implementation for a programmable CBDC:
// Solidity example for programmable money
pragma solidity ^0.8.0;
contract ProgrammableCBDC {
mapping(address => uint256) private balances;
address public centralBank;
constructor() {
centralBank = msg.sender;
}
function transferWithCondition(
address to,
uint256 amount,
uint256 timestamp
) external {
require(balances[msg.sender] >= amount, "Insufficient balance");
require(block.timestamp >= timestamp, "Transfer condition not met");
balances[msg.sender] -= amount;
balances[to] += amount;
emit ConditionalTransfer(msg.sender, to, amount, timestamp);
}
function automatedMonetaryPolicy(uint256 inflationRate) external {
require(msg.sender == centralBank, "Only central bank can execute");
// Adjust balances based on inflation rate
for(uint256 i = 0; i < accountCount; i++) {
address account = accounts[i];
balances[account] = balances[account] * (100 + inflationRate) / 100;
}
}
}
5. Currency Competition Analysis
The framework enables analysis of currency competition across multiple dimensions. Traditional competition centered around physical proximity and macroeconomic integration, while digital competition focuses on:
- Technical performance metrics (throughput, latency)
- Programmability and smart contract capabilities
- Privacy and security features
- Regulatory compliance and interoperability
6. Future Applications and Directions
The evolution of money properties suggests several future directions:
- Hybrid Systems: Combining benefits of multiple currency types
- Quantum-Safe Cryptography: Preparing for quantum computing threats
- Cross-Chain Interoperability: Enabling seamless value transfer between systems
- Programmable Monetary Policy: Automated response to economic conditions
- Privacy-Enhancing Technologies: Zero-knowledge proofs and other cryptographic tools
7. Original Analysis
The framework proposed by Hull and Sattath represents a significant advancement in monetary economics by systematically categorizing the properties of both traditional and digital forms of money. This comprehensive approach addresses a critical gap in the literature, as noted by the Bank for International Settlements in their 2021 annual report, which emphasized that "existing monetary frameworks fail to capture the full spectrum of properties exhibited by new digital currencies."
The authors' integration of computer science perspectives with economic theory is particularly valuable. Similar to how CycleGAN (Zhu et al., 2017) demonstrated the power of cross-domain learning in machine learning, this paper shows how insights from cryptography and distributed systems can enrich economic analysis. The technical properties identified—such as throughput, latency, and finality—are becoming increasingly important determinants of currency adoption, as evidenced by the growing user bases of high-performance blockchain networks like Solana and Avalanche.
From a technical implementation perspective, the mathematical formulation of quantum money properties aligns with recent advances in quantum cryptography. The no-cloning theorem, fundamental to quantum mechanics, provides a theoretical foundation for unforgeable digital cash that cannot be replicated—a property impossible to achieve with classical physics. This has significant implications for central banks considering future-proof digital currency designs, as noted in recent Federal Reserve discussions on quantum-resistant cryptographic standards.
The trade-off analysis between competing properties (e.g., privacy vs. regulatory compliance) echoes similar tensions in other technological domains. Just as differential privacy has emerged as a solution for balancing data utility and individual privacy in database systems, we may see similar cryptographic techniques applied to digital currencies to satisfy both individual privacy rights and regulatory requirements.
Looking forward, the framework provides a foundation for analyzing emerging monetary innovations. The rapid development of decentralized finance (DeFi) protocols demonstrates how programmability can create entirely new financial primitives. However, as the 2022 cryptocurrency market collapse showed, technical properties alone are insufficient without appropriate economic and regulatory safeguards. The comprehensive nature of this framework makes it particularly valuable for policymakers navigating these complex trade-offs.
Future research should expand this framework to include additional properties relevant to emerging use cases, such as cross-border interoperability standards and environmental sustainability metrics. As digital currencies continue to evolve, this systematic approach to property classification will be essential for understanding their potential impacts on monetary systems and financial stability.
8. References
- Jevons, W. S. (1875). Money and the Mechanism of Exchange. London: Macmillan.
- Menger, C. (1892). On the Origin of Money. Economic Journal, 2(6), 239-255.
- Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE International Conference on Computer Vision (ICCV).
- Bank for International Settlements. (2021). Annual Economic Report. Basel: BIS.
- Agur, I., Ari, A., & Dell'Ariccia, G. (2022). Designing Central Bank Digital Currencies. Journal of Monetary Economics, 125, 62-79.
- Ferrari, M. M., Mehl, A., & Stracca, L. (2020). Central Bank Digital Currency in an Open Economy. ECB Working Paper No. 2488.
- Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies. Princeton University Press.
- Aaronson, S., & Christiano, P. (2012). Quantum Money from Hidden Subspaces. Proceedings of the 44th Annual ACM Symposium on Theory of Computing.
- Federal Reserve Board. (2022). Money and Payments: The U.S. Dollar in the Age of Digital Transformation. Discussion Paper.
- World Economic Forum. (2021). Central Bank Digital Currency Policy-Maker Toolkit. White Paper.