# Theoretical Underpinnings of the System's Decision-Making Process * **Decision Theory:** Utilize principles from decision theory, where the system's decision-making can be modeled as a function that chooses the action with the highest expected utility, considering the probabilities of various outcomes and their respective utilities. * **Bayesian Inference:** In the context of threat detection, Bayesian inference can be applied to update the probabilities of a contract being malicious based on new evidence (opcode analysis). The posterior probability is updated as new data is observed, refining the model's predictions. * **Information Theory:** Concepts like entropy and information gain can be employed to measure the amount of information each opcode or feature contributes to the classification decision, guiding feature selection and model optimization. These mathematical formulations and theoretical concepts provide a solid foundation for the system's opcode analysis, threat assessment, and decision-making processes, ensuring that the system's actions are grounded in rigorous mathematical principles. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://neurablock.gitbook.io/whitepaper/mathematical-formulations/theoretical-underpinnings-of-the-systems-decision-making-process.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.