Paper: Would Friedman Burn Your Tokens?
Authors: Aggelos Kiayias, Philip Lazos, Jan Christoph Schlegel
Date: 2023-06-30
Estimated Reading Time: 38 minutes
This paper develops a theoretical framework for determining optimal token supply policies in cryptocurrencies by grounding tokenomics within established monetary economics. The authors examine how algorithmic monetary mechanisms (such as token issuance, fee burning, and taxation) align with or diverge from the Friedman rule, which states that optimal monetary policy sets nominal interest rates to zero. The model formalizes the equilibrium of a token economy with users and validators, identifying how steady-state conditions link token growth, technological progress, and the risk-free rate. The findings suggest that the optimal supply policy in blockchain systems should maintain the expected return on token holdings equal to the risk-free rate, achievable under certain constraints through smart contract–based mechanisms like block rewards and burn schemes.
Core insights
- Friedman Rule Application: The optimal token supply change satisfies Mt/Mt-1 = (1 + γ)/(1 + r), aligning cryptocurrency inflation or deflation with the economy’s risk-free rate and technological growth.
- Algorithmic Implementation: The model shows that expansions via block rewards and contractions via taxation and burning can approximate the Friedman rule under specific market conditions.
- Neutrality of Fee Burning: In deterministic demand scenarios, burning transaction fees through taxation does not improve welfare, as reduced demand offsets token appreciation.
- Positive Effects Under Heterogeneous Shocks: When users experience diverse preference shocks, moderate taxation during congestion can improve welfare by redistributing surplus and reducing carry costs.
- Policy Implications: Future tokenomics should integrate monetary theory, suggesting that optimal supply adjustments can be governed algorithmically, with oracles introducing external rate data.
The paper models a cryptocurrency economy where users transact with tokens while validators provide network capacity. Its equilibrium analysis connects token value evolution with the risk-free rate and platform growth, identifying when algorithmic control can mimic central bank behavior. The authors find that maintaining token return parity with the risk-free rate leads to an optimal steady state. This raises the question of whether decentralized systems can feasibly estimate and apply macroeconomic parameters such as technological growth γ and rate r in real time through on-chain governance or oracles.
A significant insight concerns the limits of deflationary policies. While fee burning mechanisms like Ethereum’s EIP-1559 reduce supply, they do not inherently increase welfare unless agents are heterogeneously affected and congestion exists. This suggests that the benefits of “ultra-sound money” claims depend on state-dependent usage rather than static scarcity. Another question arises: can decentralized protocols adjust taxation dynamically based on observed congestion without violating incentive compatibility?
In exploring supply expansion, the authors note that issuance via block rewards is distributionally neutral but raises questions about validator incentives and long-term inflation control. The welfare neutrality result under deterministic demand emphasizes that stable token value requires a balance between supply elasticity and user demand, rather than continuous deflation. In uncertain environments, however, controlled burning can stabilize utility across states, effectively smoothing consumption risk.
Finally, the study establishes a formal bridge between monetary economics and blockchain tokenomics, treating cryptocurrencies as monetary economies governed by algorithmic policy. The implication is that future protocol design must consider macroeconomic optimization rather than heuristic claims of soundness. The authors propose extending this framework to speculative demand and continuous-time modeling, potentially allowing protocols to compute real-time monetary adjustments algorithmically in open blockchain systems.
