The Dragonfly Eye methodology provides a structured approach for cross-disciplinary research using AI. It emphasizes that effective research begins with defining clear boundaries and objectives rather than immediate hypothesis generation, which often leads to chaotic results.
The process relies on subject facets, which are organized knowledge domains with specific vocabularies and mechanisms. Researchers must normalize these into taxa—distinct, verifiable units—and create passports for each taxon to define their boundaries, states, and sources.
To enable meaningful comparisons, taxa are analyzed through specific dimensions such as financial, risk, or regulatory metrics. This allows researchers to compare underlying mechanisms rather than just linguistic similarities between different fields.
A mature subject facet must include a library of taxa, passports, and defined relationships. This maturity allows the researcher to identify potential interfaces with other domains, ensuring that only relevant, verifiable constructs are transferred between fields.
The final stage involves identifying invariants—the core structural elements that must remain intact during a cross-disciplinary transfer. By abstracting these constructs and reinterpreting them within the target domain, researchers can test hypotheses without relying on synthetic AI-generated data.
