In the early part of the paper, the author clarifies that while he assumes "temporal precedence as an aspect of causality" for simplicity, actually his approach would work with any other systematic way of assigning asymmetric directions to relationships between events
I have been thinking a lot about how to infer causality from non-time-series data (e.g. categorial gene expression data), and this is a case where looking at some other sort of asymmetry than temporal precedence (but that may generally correlated with temporal precedence) seems to make sense. E.g. I've been thinking about looking at informational asymmetry: If one has P(A = a | B=b), one can look at whether the distribution for A gives more information about the distribution for B, or vice versa. This informational asymmetry can be used similarly to temporal asymmetry in defining causality. Furthermore, it on the average is going to correlate with temporal asymmetry, because the past tends to contain more information about the future than vice versa (due to entropy increase, roughly speaking... but there's more story here...) -- Ben On Tue, Nov 25, 2014 at 5:34 AM, Michael van der Gulik <[email protected]> wrote: > "Chapter 1. Quantum mechanics... " > > It's a nice article; I'll add it to my reading list. Prediction involves > working out what causes what, so it's pretty fundamental. > > I have a question. Causation in my mind seems to always involve time, and I > suspect it's impossible to have causation without including timing. So... > > Is it possible for a cause to happen at exactly the same moment as its > effect? > > Is it possible for a cause to happen after its effect? > > One instance I'm trying to get my head around is when an intelligence > anticipates a cause (which is an event in the future), which results in the > intelligence acting such that the effect occurs before the cause. Perhaps > the anticipation itself is the causal event. > > Regards, > Michael. > > > On Sun, Nov 23, 2014 at 7:17 AM, Ben Goertzel <[email protected]> wrote: >> >> I just happened across this 2011 paper on the probabilistic foundation >> of causality, >> >> http://philsci-archive.pitt.edu/9729/1/Website_Version_2.pdf >> >> which seems to carefully clarify a bunch of issues that remain >> dangling in prior discussions of the topic >> >> It seems to give a good characterization of what it means for "P to >> appear to cause Q, based on the knowledge-base of observer O" >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> "The reasonable man adapts himself to the world: the unreasonable one >> persists in trying to adapt the world to himself. Therefore all >> progress depends on the unreasonable man." -- George Bernard Shaw >> >> -- >> You received this message because you are subscribed to the Google Groups >> "Artificial General Intelligence" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> For more options, visit https://groups.google.com/d/optout. > > > > > -- > http://gulik.pbwiki.com/ > > -- > You received this message because you are subscribed to the Google Groups > "Artificial General Intelligence" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. -- Ben Goertzel, PhD http://goertzel.org "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." -- George Bernard Shaw ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
