In their 2010 book, Biology's First Law, D. McShea and R. Brandon present a principle that they call ''ZFEL,'' the zero force evolutionary law. ZFEL says (roughly) that when there are no evolutionary forces acting on a population, the population's complexity (i.e., how diverse its member organisms are) will increase.Here we develop criticisms of ZFEL and describe a different law of evolution; it says that diversity and complexity do not change when there are no evolutionary causes.
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The aim of this article is twofold. First, it is argued that while the principle of 'ought implies can' is certainly plausible in some form, it is tempting to misconstrue it, and that this has happened in the way it has been taken up in some of the current literature. Second, Kant's understanding of the principle is considered. Here it is argued that these problematic conceptions put the principle to work in a way that Kant does not, so that there is an important divergence here which can easily be overlooked.
Schupbach and Sprenger ([2011]) introduce a novel probabilistic approach to measuring the explanatory power that a given explanans exerts over a corresponding explanandum. Though we are sympathetic to their general approach, we argue that it does not (without revision) adequately capture the way in which the causal explanatory power that c exerts on e varies with background knowledge. We then amend their approach so that it does capture this variance. Though our account of explanatory power is less ambitious than Schupbach and Sprenger’s in the sense that it is limited to causal explanatory power, it is also more ambitious because we do not limit its domain to cases where c genuinely explains e. Instead, we claim that c causally explains e if and only if our account says that c explains e with some positive amount of causal explanatory power. 1Introduction2The Logic of Explanatory Power3Subjective and Nomic Distributions 3.1Actual degrees of belief3.2The causal distribution4Background Knowledge 4.1Conditionalization and colliders4.2A helpful intervention5Causal Explanatory Power 5.1The applicability of explanatory power5.2Statistical relevance ≠ causal explanatory power5.3Interventionist explanatory power5.4E illustrated6Conclusion
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