Saturday, November 10, 2007

Top Down Causation: A Logical Foundation for Intelligent Design

The abstract to Top-Down Causation by Information Control: From a Philosophical Problem to a Scientific Research Program, authored by G. Auletta, G. F. R. Ellis, FRS, and L. Jaeger, follows (in blue):

Abstract. It has been claimed that different types of causes must be considered in biological systems, including top-down as well as same-level and bottom-up causation, thus enabling the top levels to be causally efficacious in their own right. To clarify this issue, important distinctions between information and signs are introduced here and the concepts of information control and functional equivalence classes in those systems are rigorously defined and used to characterise when top down causation by feedback control happens, in a way that is potentially testable. The causally significant elements we consider are equivalence classes of lower level processes, realised in biological systems through different operations having the same outcome within the context of information control and networks.

The paper, to which the abstract pertains, frames the soundest theoretical approach to intelligent design that I have come across. Despite the focus on cellular mechanisms and the natural selection concept, the ultimate issue distinguishing competing paradigms is causality. We observe selection associated with resistence to antibiotics, selection utilized during the course of research and in breeding animals. The last two can be considered consequences of intelligent design. Linking intelligent design to bacterial resistance requires a distinct theoretical outlook establishing the association. That distinct outlook may be the one found in the referenced paper which identifies causal dynamics characteristic of intelligently designed systems. They contrast with the bottoms up approach utilized in mainstream evolution and abiogenesis theories. The difference could mark an experimental approach able to distinguish between conflicting theories and provide the empirical footing needed to establish a scientifically viable foundation for intelligent design.

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