Self-learning smart contracts

Creating a reliable and secure smart contract in a crypto project depends on an effective tokenomics model. Such models are built using algebraic specifications and formal methods. The model incorporates crypto platform operations in accordance with the actions of project stakeholders—users, team members, and investors. A smart contract is an implementation of tokenomics and often contains mitigation or preventive measures to maintain the equilibrium and liquidity of the project’s cryptocurrency. In this context, we propose a technology for determining and configuring the parameters of such smart contract actions using machine learning.
We consider an initial neural network trained on historical data reflecting changes in the rate of selected tokens in relation to the rates of major cryptocurrencies—Bitcoin or Ethereum. Additional factors, such as celebrity statements, news, world events, and natural disasters that indirectly affect cryptocurrency and token rates, are also taken into account. This combination of a tokenomics model and a neural network forms the basis of the smart contract. During the operation of the smart contract, these data are continually considered and used to retrain the neural network, taking into account the behaviour of the project’s token.
Several experiments have demonstrated that predicting changes in token rates and dynamically adjusting parameters is significantly more accurate and effective for the project’s success than relying on a static algorithm. The proposed approach is exemplified by a tokenomics model and a self-learning smart contract, with comparative metrics illustrating its effectiveness.