Modellenosial

Modellenosial is a theoretical concept used in discussions of modeling within social systems. It refers to the study of how computational or mathematical models interact with social practices, and, in turn, how social context shapes the development, validation, and deployment of those models. The term blends "model" with a root resembling "social" to emphasize the co-dependency of technical artifacts and human actors.

Origins and usage: The term is not standardized and appears mainly in interdisciplinary debates on model governance and sociotechnical systems. It is used to frame analyses that treat models as social instruments rather than purely objective representations, highlighting the ways in which trust, expertise, and organizational cultures affect modeling work.

Framework: Modellenosial emphasizes three dimensions. First, model creation and adaptation are driven by organizational norms, epistemic cultures, and practical constraints. Second, model dissemination and adoption are shaped by social factors such as trust, credibility, and power relationships among stakeholders. Third, there is feedback from model outputs into decision-making, which reshapes social dynamics and prompts subsequent revisions of the models.

Methodology and evaluation: Researchers typically combine qualitative methods (ethnography, interviews) with quantitative approaches (network analysis, experiments) to map actor networks around models and to assess adoption, fidelity, and resilience to bias. Transparency, reproducibility, and governance practices are often foregrounded as part of the evaluative process.

Applications and critique: Modellenosial is relevant to policy modeling, healthcare decision support, climate risk assessments, and engineering design processes, with implications for model risk management and transparency. Critics argue that the concept can be vague or overly deterministic unless paired with concrete case studies and clear operational definitions. Proponents advocate an explicit focus on socio-technical contexts to improve model reliability and legitimacy.