Analogami

Analogami is a term sometimes used in cognitive science and linguistics to refer to the study and application of analogy-based reasoning across domains. It is not a universally standardized term and may appear as a neologism or as a shorthand within interdisciplinary discussions of how people derive new ideas by relating disparate areas through underlying relational structures.

In the usage where analogami is discussed, it describes a process consisting of constructing representations of a source domain, identifying correspondences between relations in the source and target domains, and transferring or adapting those relations to generate new insights or solutions. Core components include the source-domain schema, the target-domain problem or situation, the mapping between relations, and the evaluation of the fit or usefulness of the analogy. This perspective emphasizes relational patterns rather than surface similarities and often highlights constraints that govern valid mappings.

Research on analogami employs various methods, including controlled cognitive experiments on problem solving, analyses of metaphor and analogy in language, and computational modeling that implements structure-mapping or relational-alignment algorithms. Applications are proposed in education, where analogical explanations can aid understanding, as well as in artificial intelligence, where analogy-based reasoning supports transfer learning, explanation generation, and creative problem solving.

Critics note that analogical reasoning can be influenced by prior knowledge, context, and cultural background, which may bias mappings or lead to inappropriate transfers. They argue that emphasis on analogy must be balanced with domain-specific constraints and critical evaluation. See also analogy, analogical reasoning, structure-mermap algorithms, and conceptual blending.