Algorithmic stablecoins (AS) are one special type of stablecoins that are not backed by any asset. They stand to revolutionize the way a sovereign flat operates. As implemented, AS are poorly stabilized in most cases; their prices easily deviating from the target or even falling into a catastrophic collapse, and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how AS work from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game. We thereby propose a rational model in the context of AS and draw its holding conditions. We apply the model to examine: whether or not the algorithmic stablecoin is a rational Ponzi game. Accordingly, we discuss two types of algorithmic stablecoins (Rebase & Seigniorage Shares) and dig into the historical market performance of a number of impactful projects to demonstrate the effectiveness of our model.

Existing NFTs confront restrictions of one-time incentive and product isolation. Creators cannot obtain benefits once having sold their NFT products due to the lack of relationships across different NFTs, which results in controversial profit sharing. This paper proposes a referable NFT solution to extend the incentive sustainability of NFTs. We construct the referable NFT (rNFT) network to increase exposure and enhance the referring relationship of inclusive items. We introduce the DAG topology to generate directed edges between each pair of NFTs with corresponding weights and labels for advanced usage. We accordingly implement and propose the scheme under Ethereum Improvement Proposal (EIP) standards, indexed in EIP-5521. Further, we provide the mathematical formation to analyze the utility for each rNFT participant. The discussion gives general guidance among multi-dimensional parameters. The solution, as a result, shape the recognition of potential values hidden in isolated NFTs and raise the interest of communities toward the discovery of NFT derivatives. To our knowledge, this is the first study to build a referable NFT network, explicitly showing the virtual connections among NFTs.