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Dec 19, 2006 18:56

Consciousness final and sociolinguistics paper are done. That leaves only the cog sci work and the book review. Still won't get home before Friday--I just hope I'll have time to do all the book review research I need before the libraries close.

In the meantime, I keep getting distracted by other thoughts.

Here's one: we think we can model basically any causal system using causal Bayes nets. But I'm having trouble figuring out a good formal treatment of.... I'm not even sure how to say this: higher- or lower-level events.

In the normal case we assign each event to a node/random variable, use the graph structure to chart dependencies between them and then assign the local probability distributions. Cool.


But, like, each of these nodes is due to other events, right? Like, "buses slow" can be reduced to "bus A slow," "bus B slow," "bus C slow".... Does "delays" cause each of these events? Do these events collectively cause the "buses slow" event, or does the latter event supervene on the former? I suppose it does, but then how do we get this kind of reduction to the supervened-on to work out?

This is important for me, since it's looking like almost everything is causality. Mental properties are looking like causal properties, and there's evidence to suggest that most of our amodal (i.e., not based on any perceptual or motor modality) conceptual content is in fact just causal knowledge. That means that causal relations may be all that there is, ontologically (<-- huge speculative move).

graphs, supervenience, causality, bayes nets, metaphysics, reduction, ontology

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