An early model of explanatory reduction was put forward by William Wimsatt (1976b).
Sometimes, however, the Nagel model has been characterized as an epistemological model of reduction, because it is a model of theory-reduction (Sarkar 1992; Hoyningen-Huene 1989; Silberstein 2002), because it is a model of explanatory reduction (Sarkar 1992), or because bridge-laws are to be interpreted epistemologically (Fazekas 2009, following Klein 2009).
This requires models of explanatory scope. 5.
Models of explanatory reduction avoid the problems facing theory reduction mentioned at the end of Section 3.1.
Models of explanatory reduction typically assume that reductive explanation is causal explanation, where a higher level feature is explained by the interaction of constituent parts.
Context-dependence is primarily a problem for models of theory reduction; models of explanatory reduction often take the organismal context for granted without being committed to reducing it molecularly (as seen in reductive explanations offered in experimental biology).
This parallels the trend in models of explanatory reduction of moving away from theories as the only epistemic units of interest (Section 3.2), but the emphasis is on relata such as coordination, integration, synthesis, or reciprocal interaction.
This decline has been accompanied by an increasing attention to models of explanatory reduction (e.g., Sarkar 1998, Weber 2005) across a wider variety of domains in biology (e.g., development, ecology, evolution, cell biology, and neuroscience).
Other models of explanatory reduction have been put forward by philosophers, many with an explicit eye to capturing how reduction occurs in scientific practice (e.g., Bickle 2003, 2006, 2008).
Two basic categories can be distinguished: (a) models of theory reduction maintain that one theory can be logically deduced from another theory (Section 3.1); and, (b) models of explanatory reduction focus on whether higher level features can be explained by representations of lower level features (Section 3.2).
Fig. 1 Model inclusion of explanatory variables based on best 2000 models: Red colour indicates a negative coefficient, blue colour indicates a positive coefficient and white colour indicates non-inclusion.
