![]() ![]() But this is also true if the sound of the crash causes me to throw a rock. If throwing a rock causes the sound of a crash, then the throw and the crash will tend to occur in each other's presence. But is this because R2 causes M1 to be +, or because M1 causes R2 to be +? For example, we could find that when M1 is in the + state, that R2 is often also in the + state. We can try to look at various slices M, and try to find correlations between the values of M, and the values of L and R. Second, that causality could be flowing from R to M to L:Īs good Bayesians, we realize that to distinguish these two hypotheses, we must find some kind of observation that is more likely in one case than in the other. First, that causality could be flowing from L to M to R: When we look at an arbitrary value-pair and its neighborhood, let's call the three slices L, M, and R for Left, Middle, and Right. But we don't know the global direction of time, yet, so we don't know if our statistic relates the effect to the cause, or the cause to the effect. We can try to infer the laws by gathering statistics about which values of 1 and 2 are adjacent to which other values of 1 and 2. We know this, but we don't know the actual laws. On each round, the past values of 1 and 2 probabilistically generate the future value of 1, and then separately probabilistically generate the future value of 2. We have a large amount of data from the series, laid out on a track, but we don't know the direction of time on the track. Let's say we have a data series, generated by taking snapshots over time of two variables 1 and 2. Fisher testified that it was impossible to prove that smoking cigarettes actually caused cancer. Causality was declared dead, and the famous statistician R. They could measure correlation, and that was enough. And in statistics, nobody thought there was any way to define causality. Time-symmetrical laws of physics didn't seem to leave room for asymmetrical causality. Many thought it was unsolvable even with time. Once, sophisticated statisticians believed this problem was unsolvable. Perhaps correlation, plus time, can suggest a direction of causality? If, on the other hand, someone observed me on the cliff, and saw a flash of light, and then immediately afterward saw me throw a rock off the cliff, they would suspect that flashes of light caused me to throw rocks. It seems more likely that throwing the rock off the cliff is causing the crash. Perhaps the sound of the crash is causing me to throw a rock off the cliff? But no, this seems unlikely, for then an effect would have to precede its cause. I do this again and again, and it seems that the rock-throw, and the crash, tend to correlate to occur in the presence of each other. I throw a rock over the side, and a few seconds later, I hear a crash. ![]() Suppose I'm at the top of a canyon, near a pile of heavy rocks. There is an old saying: "Correlation does not imply causation." I don't know if this is my own thought, or something I remember hearing, but on seeing this saying, a phrase ran through my mind: If correlation does not imply causation, what does? We could throw out time, and keep the concept of causal computation. If so, we would not be forced to conclude that a single configuration, encoding a brain frozen in time, can be the bearer of an instantaneous experience. Such causal links could be required for "computation" and "consciousness"-whatever those are. Barbour may not have studied this it is not widely studied. There is a timeless formulation of causality, known to Bayesians, which may glue configurations together even in a timeless universe. On this point, I take it upon myself to disagree with Barbour. Julian Barbour believes that each configuration, each individual point in configuration space, corresponds individually to an experienced Now-that each instantaneous time-slice of a brain is the carrier of a subjective experience. ![]()
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