How do we understand and model the nature of scientific explanation and prediction?

Scientific explanation and prediction are fundamental aspects of the philosophy of science, aiming to understand and describe phenomena through various theoretical frameworks and methodologies.

Model-based approaches suggest that explaining phenomena requires constructing models using metaphors or analog methods rather than solely relying on deduction from laws.
Understanding in science is achieved not just by receiving explanations but by making causal and logical inferences to develop mental models that reflect the true nature of phenomena.
Sensitivity analysis-based methods for explaining prediction models consider interactions and redundancies between features, enhancing understanding of the model and its predictions.

In conclusion, the nature of scientific explanation and prediction is understood through the construction of models that encapsulate the causal and logical properties of phenomena. These models are informed by inferences drawn from data and are refined by methods that account for the complexities of feature interactions within predictive models.

How do we understand and model the nature of scientific explanation and prediction?

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A Model-based Approach to Scientific Explanation

The model-based theory suggests that scientific explanation involves building a model for a phenomenon using metaphors or analog methods, rather than deducting laws from it.

An Inferential Model of Scientific Understanding

Scientific understanding involves developing mental models that incorporate correct causal and logical properties, rather than just accepting explanations.

Explaining prediction models and individual predictions with feature contributions

This sensitivity analysis-based method improves understanding of prediction models by considering interactions and redundancies between features in classification and regression models.

The prediction and explanation of criminal violence.

Einhorn and Hogarth (1986) suggest that causality in criminal violence research is influenced by factors such as covariance, precedence, and similarity, as well as the ability to link variables to effects through causal chains or mechanical mechanisms.

Models of Human Scientific Discovery

A formal model of human scientific discovery should address core cognitive science problems, including mental models, complex system interactions, and active interrogation through experiments.

The priority of prediction in ecological understanding

A renewed focus on prediction in ecology can advance understanding and progress towards a more general understanding of the natural world.

How scientific models can explain

Scientific models can offer genuine scientific explanations under certain conditions, as demonstrated by Bohr's model of the atom.

How are Models and Explanations Related?

This paper provides a unified framework for analyzing the various relationships between scientific models and scientific explanations, revealing that epistemological relationships are not always dependent on metaphysical relationships.

Reintroducing Prediction to Explanation

Reintroducing prediction to explanation can enrich theories of explanation and clarify what makes an explanation scientific.

An inferential and dynamic approach to modeling and understanding in biology

Understanding plays a central role in biology modeling and scientific research, linking experimentation and explanation, offering a non-referentialist perspective on scientific models.

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