In the dynamic blockchain economy, valuing digital blockchain networks accurately is critical. One of the most promising valuation models is Metcalfe’s Law. Tórónet founder Dr. Ken Alabi, along with Joshua Eick, have conducted a study to explore refining this model to account for network effects in digital blockchain network valuation, specifically focusing on the Ethereum Network. Their study introduces an enhanced valuation model that builds upon Metcalfe’s Law, integrating supply-demand dynamics and network upgrades.
Purpose of the Study
The study aimed to refine Metcalfe’s Law to better account for the complexities of digital blockchain networks. By incorporating an exponential growth decay equation, the researchers sought to dynamically adjust the circulating supply, considering network upgrades and other variables. The goal was to improve the predictive accuracy of blockchain network valuations, particularly for the Ethereum Network.
Findings
The refined model demonstrated a significant improvement in explanatory power over traditional Metcalfe’s Law applications. By integrating quality variables and dynamic supply adjustments, the model more accurately reflects the value of digital blockchain networks. This enhanced model not only applies to Ethereum but also lays the groundwork for other Layer 1 blockchain networks like Bitcoin, Avalanche, and Near.
Model Incorporation: The study incorporates cumulative net issuance to model the supply for the Ethereum Network, acknowledging that the network does not have a fixed supply.
Demand Modeling: The demand model builds on Alabi’s previous work, utilizing an exponential function to quantify network effects. This model integrates active addresses and log-normal values to better capture network demand.
Regression Analysis: By combining the supply and demand models with non-linear regression and artificial neural network (ANN) models, the study achieved high predictive accuracy, with an R-squared value of 0.98 and low prediction errors.
Quality Variable: The study introduced the Transaction Cost Per User (TCPU) as a quality metric, further enhancing the model’s precision by considering network quality alongside size.
Implications for Tórónet and the Industry
For Tórónet and other layer 1 infrastructures, this refined model represents a substantial advancement in network valuation. Layer 1’s can leverage these insights to:
- Enhanced Valuation Accuracy: More precise network valuations will attract investors by providing a clearer understanding of network value.
- Strategic Network Growth: Understanding the impact of network size and quality on value can guide strategic decisions to enhance network utility and adoption.
- Broader Industry Impact: This study sets a precedent for applying refined valuation models across the blockchain industry, potentially becoming a standard for digital asset valuation.
Dr. Ken Alabi’s work on refining Metcalfe’s Law not only advances theoretical understanding but also offers practical tools for enhancing the valuation and strategic growth of blockchain networks. For Tórónet, these insights are invaluable as we continue to innovate and lead in the blockchain space.
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Tórónet is an open fintech platform built to make decentralized finance (DeFi) tools accessible to communities in Africa & emerging markets. Built on a layer-1 blockchain, the solution harnesses cutting-edge technologies to overcome the limitations of modern infrastructure and bridge the gap in access to essential services. Their mission is to improve the well-being of individuals and communities left behind in the fast-paced world by providing increased access to education, healthcare, financial services, and economic opportunities.
