Wisdoma

Wisdoma is a theoretical framework that describes a structured approach to capturing, organizing, and applying practical wisdom in complex decision making. It is used in philosophy, information science, and speculative discussions of artificial intelligence to distinguish data gathering from guidance that reflects values, context, and long-term consequences. The name combines wisdom with a domain concept, signaling a domain of application where prudent judgment is required. In this view, wisdom is what remains after data and information have been processed and contextualized within normative principles and user goals.

Architecturally, wisdoma envisions a multi-layer model built on data, information, and wisdom, augmented by governance and context awareness. Typical components include a knowledge base, a normative rule layer, a user model, a decision pipeline, and an auditing or explainability module. The framework emphasizes value alignment, accountability, and transparency, with mechanisms to reflect user context and cultural differences.

Applications span governance planning, healthcare decision support, education, and strategic business decisions, especially where ethical considerations, cultural differences, or long-term impacts are important. A wisdoma-based approach aims to produce recommendations that are transparent, aligned with stated values, and adaptable to user context. History and reception of the concept appear in theoretical writings and thought experiments since the 2020s, discussed as a framework for how artificial and human judgment can collaborate while honoring moral constraints.

Criticism notes that the concept can be vague and difficult to operationalize, and warns against over-reliance on algorithmic guidance, potential biases, and the risk of redefining wisdom as merely efficient outcome optimization. Related topics include wisdom, decision support systems, knowledge management, and ethics in AI.