[Krimel] [Sorry about the double 'm', previously] [yeah, that was getting annoying...] > How can you possibly see a single chain of causality in a cattle stampede?
[Craig] Because I agree with you that everything in the universe affects everything else. So everything is in the same chain as everything else. We can follow the path of one cow & call it a chain of causality. Or follow the path of another cow & call it a different chain. But what the first cow does affects what the second cow does (even in a stampede). The two cows are in the same chain. [Krimel] Ok, I think it makes more sense to see two cows as two chains. But this just highlights the arbitrary nature of what we select to look at. Are we looking at wholes or parts or wholes as parts or parts as wholes? I maintain that however we look we see a fractal structure that is self similar across scale. At herd of cows can be seen as a unit that responds to various causal factors that pertain to herds. A cow is a unit or part of a herd that is influenced by other sets of causal variable that effect cows. Cows are an assemblage of biochemical units that are influenced by other sets of causal relationships. As you note everything in the universe affects everything else but in my opinion seeing it as all one chain of linear causality misses the point. [Craig, previously] > How do you distinguish between "lots of chains converging in the present" > & lots of partial descriptions of the single chain leading to the present? [Craig] What reason do we have to think "the world remains totally deterministic", when "the knowledge needed can not be determined even in principle"? [Krimel] Because indeterminacy arise from several sources. In this case indeterminacy does not mean acausal but rather unpredictable. Indeterminacies at the quantum level tend to average out probabilistically. They may impact the macro level but they tend to average out. A bigger source of uncertainty is the behavior or chaotic systems like the weather or flocks of birds or herds of cattle or traffic on a highway. Such systems may settle into statistical averages but precise prediction of future states of such systems deteriorates with the passage of time. Part of what is at work here is nonlinearity. The best example of this is the straw that breaks the camel's back. If we add straws to the camel's back one at a time, the weight on the camel's back increases in a linear fashion an the camel adjusts to the added weight in a similarly linear way. At some point the final straw alters the system in a nonlinear way. The camel's back breaks and the whole system is abruptly transformed in unpredictable ways. We are accustomed to thinking of linear systems that are relatively easy to study and describe. Nonlinear systems are actually more common in the real world but much more difficult to describe and model. Moq_Discuss mailing list Listinfo, Unsubscribing etc. http://lists.moqtalk.org/listinfo.cgi/moq_discuss-moqtalk.org Archives: http://lists.moqtalk.org/pipermail/moq_discuss-moqtalk.org/ http://moq.org.uk/pipermail/moq_discuss_archive/
