Model-dependent

Model-dependent describes a conclusion, result, or interpretation that relies heavily on the specific assumptions, parameters, or structure of a particular model. It signifies that the outcome is not universally applicable or independent of the chosen framework. The validity and generalizability of model-dependent findings are limited by the accuracy and relevance of the underlying model. Consequently, alternative models may yield significantly different results or perspectives, underscoring the importance of considering the model's limitations and context when drawing conclusions.

Model-dependent meaning with examples

• In climate science, projections of future temperature increases are often model-dependent. Different climate models, with varying levels of complexity and parameterization of processes, can produce different predictions, particularly regarding regional impacts. Therefore, assessments of climate change must always explicitly state the models used and acknowledge the potential for uncertainty arising from this dependency.
• Financial forecasts, especially those related to complex derivatives, are inherently model-dependent. The Black-Scholes model, for example, relies on assumptions like constant volatility and efficient markets. Consequently, the accuracy of its predictions hinges on the validity of these assumptions, and model-dependent inaccuracies can lead to substantial financial losses in volatile markets.
• Neuroscientific studies that use computational models to simulate brain activity are model-dependent. The outputs of these models, which provide insights into cognitive processes, heavily rely on the assumptions about the neural architecture and connectivity used in constructing these computational models. Therefore, the interpretations are critically tied to these assumptions.
• In particle physics, the interpretation of experimental data, such as the detection of new particles, is often model-dependent. Physicists must fit experimental results to theoretical models, such as the Standard Model or extensions of it. Any conclusion about the existence or properties of a particle is thus dependent on how well that model fits the data.
• Statistical analyses, such as those employing regression, can be model-dependent. The choice of the regression model (linear, logistic, etc.) and the inclusion of specific variables significantly impact the conclusions. For instance, a model omitting a relevant confounding variable could produce biased effect estimates that may not be applicable in broader settings.