 Sensitivity to the sciences: Several philosophers have made an effort to draw attention to the fact that an adequate discussion of laws has to be sensitive to the differences between distinct scientific disciplines. Their motivation is to provide a theory of laws that fits generalizations (and models) in the life sciences, the social sciences and, more generally, the sciences of complex systems (for instance, see Mitchell 1997, 2002a,b; 2008, 2009; Sober 1997; Steel 2007; and Reiss 2008). In particular, Mitchell (2000), Strevens (2003, 2008), Craver (2007), Hüttemann (2007), Wimsatt (2007), Tobin (2005), Woodward (2010) and Reutlinger (2011, 2013) distinguish various dimensions of characterizing how generalizations in these scientific disciplines may be non-universal or allow for exceptions. These dimensions include stability, robustness, specificity, and historical contingency (see Reutlinger and Unterhuber 2014b for an overview). In a similar vein, Schurz (2014) introduces a novel distinction between ceteris paribus and ceteris rectis laws. Fenton-Glynn (2016) holds that some higher-level laws about the macro-behavior of complex systems (his central example is the second law of thermodynamics) are “minutis rectis” laws and should be distinguished from cp-laws, because their non-universal character stems from a probability distribution over initial micro-conditions (see Strevens 2003 for further examples). Finally, motivated by examples from population ecology, earth science, and statistical mechanics, Strevens (2008) and Weisberg (2013) argue for a new interpretation of laws and models involving idealized assumptions, the minimalist account of idealizations. Strevens (2012, 2014) proposes treating cp-laws and idealized laws separately. What seems to emerge from this literature is that the ceteris paribus character of generalizations is multi-faceted. There is a number of ways in which a generalization can be non-universal: due to idealizations, by expressing statistical regularities and probability distributions over initial conditions, by drawing on the notion of normality, by being sensitive to changes in initial and background conditions, and so on (see Reutlinger and Unterhuber 2014b for a more detailed analysis).