Variations in Variation and Selection: The Ubiquity of the Variation-and-Selective-Retention Ratchet in Emergent Organizational Complexity, Part II: Quantum Field Theory

If the general arguments concerning theinvolvement of variation and selection inexplanations of ``fit'' are valid, then variationand selection explanations should beappropriate, or at least potentiallyappropriate, outside the paradigm historisticdomains of biology and knowledge. In thisdiscussion, I wish to indicate some potentialroles for variation and selection infoundational physics – specifically inquantum field theory. I will not be attemptingany full coherent ontology for quantum fieldtheory – none currently exists, and none islikely for at least the short term future. Instead, I wish to engage in some partiallyspeculative interpretations of some interestingresults in this area with the aim ofdemonstrating that variation and selectionnotions might play a role even here. Ifvariation and selection can survive in even asinhospitable and non-paradigmatic a terrain asfoundational physics, then it can surviveanywhere.     

Overcompensation and population cycles in an ungulate.

Although theoretical studies show that overcompensatory density-dependent mechanisms can potentially generate regular or chaotic fluctuations in animal numbers, the majority of realistic single-species models of invertebrate populations are not overcompensatory enough to cause sustained population cycles. The possibility that overcompensation may generate cycles or chaos in vertebrate populations has seldom been considered. Here we show that highly overcompensatng density-dependent mortality can generate recurrent population crashes consistent with those observed in a naturally limited population of Soay sheep. The observed interval of three or more years between crashes points to sharp 'focusing' of mortality over a narrow range of population density.     

On the limitations of comparing mean square forecast errors

Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mean square forecast errors (MSFEs) are not. Different rankings may result across models or methods from choosing alternative yet isomorphic representations of a process. One approach can dominate others for comparisons in levels, yet lose to another for differences, to a second for cointegrating vectors and to a third for combinations of variables. The potential for switches in ranking is related to criticisms of the inadequacy of MSFE against encompassing criteria, which are invariant under linear transforms and entail MSFE dominance. An invariant evaluation criterion which avoids misleading outcomes is examined in a Monte Carlo study of forecasting methods.