Efficient, Formal, Material, and Final Causes in Biology and Technology

Abstract

This paper considers how a classification of causal effects as comprising efficient, formal, material, and final causation can provide a useful understanding of how emergence takes place in biology and technology, with formal, material, and final causation all including cases of downward causation; they each occur in both synchronic and diachronic forms. Taken together, they underlie why all emergent levels in the hierarchy of emergence have causal powers (which is Noble’s principle of biological relativity) and so why causal closure only occurs when the upwards and downwards interactions between all emergent levels are taken into account, contra to claims that some underlying physics level is by itself causality complete. A key feature is that stochasticity at the molecular level plays an important role in enabling agency to emerge, underlying the possibility of final causation occurring in these contexts.

Genetic tests for athletic ability: hopes and problems (Part 1)

“…Było to odkrycie na miarę kopernikańskiego i darwinowskiego, wskazujące, że unikatowość gatunku, który jako jedyny zsekwencjonował własny genom, nie jest "zapisana w genach", lecz wynika z jedynego w historii Ziemi splotu ewolucji biologicznej oraz ewolucji społecznej, kulturowej i technologicznej (których jesteśmy zarówno twórcą, jak produktem) (Corning 2023;Zwart 2015). Jak się wydaje wielopoziomowy, adaptacyjny system homeostatyczny, obejmujący wymiar społeczny, ekonomiczny, kulturowy, polityczny i prawny umożliwia emergentne powstawanie cech jednostek, populacji i społeczności wykraczających poza redukcjonistyczne uwarunkowania biologiczne (Ellis 2023).…”

Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies

“…Therefore, Equation ( 20) can be used on this neural network to calculate EI. In [31], the authors used the Monte Carlo integration method to estimate the expectation in Equation (20). This technique significantly reduces the computational complexity.…”

“…However, when the authors compared the results calculated using Equation (20) for various neural networks with different dimensions, they obtained unreasonable results: EI increased with the number of dimensions. Thus, causal emergence was not always observed because micro-dynamics exhibited unreasonably larger EI.…”

“…where dCE is the dimension-averaged causal emergence measure. If we incorporate Equation (20) into Equation ( 28), we obtain:…”

“…Specifically, the development of a quantitative theory of emergence is on the verge of emerging. Such a theory holds the potential to address significant challenges, including the origins of life [20], the emergence of novel capabilities in large neural network models [7], and the potential for intelligence, consciousness, and free will to arise in artificial systems [21]. Causality not only exhibits a profound connection with emergence but is also considered by many researchers as one of the most crucial perspectives for quantitatively comprehending emergence [18,19,22,23].…”