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thank you and good morning or as Bob

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says whatever it is are these are

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wonderful long days I want to talk about

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basically the what amounts to hard edge

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scientific statistically based material

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but I would like to start by mentioning

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that this project began because we were

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interested in consciousness we were

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interested in the possibility that there

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is interconnection among people that

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there might even be something that could

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be construed as a global consciousness I

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won't prove or demonstrate that

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necessarily but we have some very

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interesting results over time I guess

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most importantly I think we're able to

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show with clarity that there really is

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as Gertrude Stein said some there there

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the odds are of this being just chances

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million to one or ten million to one we

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have independent measures and they're

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correlated they have correlated response

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to these events there's some structure

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in terms of distance in terms of time

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and also in terms of what you might

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think of as psychological qualities

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there's a lot of structure where there

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shouldn't be any this is what the

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network looks like interpret out over

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the world you'll see a lot of

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concentration in the US and Europe but

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we have tried to get a distribution that

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was big enough so we could ask questions

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about distance the data flow through the

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internet to Princeton and that's what

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the data looked like when they are

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coming in so we have to do a lot of

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processing to make sense or make find

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out whether there in indeed is in any

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kind of structure in the data the we

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look at each of the devices which we

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often call eggs there that's a node in

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the network it's a random event

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generator with custom software and if we

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look at them separately and then

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calculate an average

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they're accumulating deviation over time

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it will look something like this

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black summary trace and it may look like

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this in our formal experiments we first

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define the event we figure out we decide

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that there's an interesting event

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something it might possibly affect

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global consciousness if you will by

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because it makes an awful lot of people

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feel the same emotions think the same

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kind of thoughts so we discover the

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event in the news perhaps and then we

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define the beginning and end and extract

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the data and do the calculations so the

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experiment is done in a hypothesis

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testing since we know it every time

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without looking at the data which data

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we're interested in and we often show

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use these kind of figures to plot the

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result they're really just a historical

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record of the duration of the event but

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this point at the end is the point we're

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interested in in terms of a bottom-line

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statistic for each of the events here I

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will just give you two or three examples

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and then get on to the kind of analytic

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details this is September 11th in the

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context of a week of surrounding days so

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we if we look at at the our first

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prediction really only encompassed four

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hours that's the formal prediction and

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it was marginally significant it was at

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the point O two level or something like

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that

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had we realized the magnitude and in

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consciousness space we might have said

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let's look at two days that effect in

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the data data should look like what it

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looks like on the left a kind of random

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walk with a level trend and and of

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course you see when we examine over a

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longer period of time there's a

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tremendous persistence in the effect a

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big deviation that's apparently

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associated with the feelings and

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thoughts that people had

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and this one is a completely different

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kind of event this one was a planned and

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organized synchronized meditation which

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we as best we can tell involved about a

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half a million people around the world

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that's not a huge number in comparison

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of what 9/11 might produce nevertheless

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there's a powerful deviation from the

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expected level trend another completely

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different kind of event New Year's we've

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now had ten new years that we could look

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at and the question one of the questions

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we asked is does the variability of the

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data stay constant or does it deep

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decrease and as you can see a few

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minutes before midnight when people are

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beginning to think

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Midnight's coming I am I have to find my

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partner just so I can get a hug or I

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have to get my glass ready so I can

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toast the New Year and so forth

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fairly strong evidence that there's even

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in an unimportant event and this

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coalescence of large numbers of people

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in a similar direction or the same

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interest can produce an effect on our

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random event generator network this is a

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picture of the data over almost 10 years

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there are 250 events and the cumulative

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even though sometimes it's backwards

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sometimes we're flat sometimes there's

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no kind of effect the tendency is for

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there to be in effect it's relatively

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small but the accumulation over such a

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large number of formal trials is highly

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significant with a z-score equivalent to

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5 plus standard deviations million one

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odds or smaller the independent

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statistics are we have names for them we

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call one of them Network variance or

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net-net fire and a second one which is

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called Cove are they're really pair

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products in one case of z-scores and the

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other case of squared C scores one is

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more responsive to distance

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implications and one more responsive to

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temporal interconnections in the data if

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we plot those over time we see and

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compare that with the kind of control

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data the gray cloud is a thousand

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resampling from the database with the

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same kind and the same event definitions

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except now they're just randomly pieces

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of data randomly extracted that's a kind

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of background that we would you expect

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from truly random data all three or both

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of those measures or a combination of

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those independent measures show pretty

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strong difference here's another way to

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look at the independent measure question

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we created a random sample of pseudo

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events with a an effect size equivalent

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to what we find in a database and that

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blue curve shows what happens not

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unexpectedly because we've constructed a

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powerful large database of small effect

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sizes we get a peak z-score of seven or

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eight standard deviations now the neck

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the question is what happens if we on

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these pseudo events calculate the same

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kind of the same do the same

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calculations but now with our covariance

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measure and the red trace shows that

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there's basically no nothing there this

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is a I think a good demonstration of the

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true independence of these measures now

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going on to some of the other is the

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structure we see that if we move the

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event from its real time slide it toward

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the future toward the past we quickly

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lose the high high departure from

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expectation and and enter in a kind of

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random space this also answers the

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question that some people ask aren't

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there a lot of other spikes in the

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database and this in a sense shows that

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the spikes associated with the events

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that are predefined are themselves

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spectacular the correlation between the

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two measures is shown in the right

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and figure they both are centered on the

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time of the real event and if you move

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the event artificially from either to

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the future of the past it changes

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another version of time structure this

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by the way I should I believe was on the

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first slide but much of this work is

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that is from Peter Bensel who was here

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at the SSE meeting and gave a present

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presentation last year he in this case

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looked at the correlation between our

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two independent measures they both

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respond to the to the events but that

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response has a time course it appears I

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mean if you read this graph and

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interpret it what it means is that the

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real interesting time period is about

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one or two hours that means something

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like the moment for a global

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consciousness that an hour or two long

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and in some sense there's interesting

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questions about what's happening at the

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beginning we think this may mean that

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we're in this jog at the beginning of

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the graph may mean that the correlation

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the covariance measure lags the network

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variance measure but we've got a lot

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more work to do this is a complicated

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slide we do a weighted regression which

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you can see in the green straight line

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in both graphs it's significant and what

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this is means is that the measures which

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are driven by this correlation between

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our pairs pairs of re G's is stronger

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when the pairs are close to each other

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than it is when they're far apart so we

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actually have a distance indication this

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is just a picture of that on the left

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here we have a short relatively short

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distance compared to a long distance

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another way to look at the same data the

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blue curves show the data in each of

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those two measures for pair separations

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less than 8,000 kilometers

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and the red data for pair separations

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greater than 8,000 kilometers

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very interesting and to me surprising

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because my intuition going in was that

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we had a truly non-local phenomenon so

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we can easily relatively easily

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categorize a lot of the events into

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things like terror political events

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natural disasters and so on collapsed

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this to a smaller set which is makes it

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easier to read what's shown here is a

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group terror events and partisan events

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where the stimulus to have the same

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emotions comes from the outside in a

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sense and compared with something where

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the meditation where the stimulus is

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basically kind of internal and what we

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see is that the network variants blue

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column is much stronger than the then

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the response from the and the covariance

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covariance measure and for the these

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terror and partisan events we have a lot

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more work to do to really understand

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this but it it looks like well literally

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that the two different kinds of

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independent measures are actually

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responsive to different kinds of things

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this is just an analysis of variance

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showing the same data that there is an

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interaction between the type of

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statistic we use in the category that

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they're in by in several different

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groupings we see this that is there's a

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significant outcome so if there is

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consciousness driving what our system

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does one might ask what happens if

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people are awake versus asleep we might

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imagine there's a little more tendency

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while people are awake what this shows

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is in the center the a real 24-hour a

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day compared with days there that are

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minutes longer or minutes shorter

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there's a pretty impressive spike it's

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actually only sixteen to one odds but

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it's it suggests that there really is a

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kind of consciousness pressure on the

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data when people are awake the long

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there's the blue curve show alarm

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long-term trend

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our data which is in a way kind of

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mysterious why would this happen we look

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for some sort of external correlate and

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Peter decided to gather all kinds of

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presidential all kinds of polling data

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and looked in particular at the

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presidential approval ratings which as

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you can see in the left-hand graph even

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in the raw form have a fairly similar

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kind of trend when we do a simple model

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to fit the presidential approval data to

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the to the global consciousness network

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variance it's a very striking fit no

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proof of a causal result okay this is my

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last slide there there is in in the last

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ten years or nine years some six hundred

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earthquakes in the world or seven

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hundred with Richter magnitude seven or

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greater in other words damaging quakes

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about hundred of those have been on land

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where they matter to people and the rest

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are in the ocean so this graph shows a

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strong pattern when they're on the land

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and not much of a pattern at all when

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these quakes occur in the ocean

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what's perhaps more interesting in a

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certain sense and again a temporal

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structural kind of thing this central

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portion is magnified here and actually

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separated into two independent subsets

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both of which show the same pattern and

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that pattern begins about eight hours

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before the minimum point which is at the

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time of the quake so okay last point was

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oh i this is the button the fact that

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only where that the only the quakes

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which affect people show any pattern

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suggests i think strongly the

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consciousness definitely is involved

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lots of other things to do there's even

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a suggestion of premonition but more

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work to do to discover where there's any

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reality to that thank you very much this

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is the

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and part of the group who help Thank You

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Roger

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I just wanted to ask because I have a

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son who lives in Los Angeles could you

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please call me if that you can see that

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happening one of the suggestions that

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the data give is that we could in

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principle predict things the trouble is

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that if we see a strange change in the

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data we don't know much more than the

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data or responding to something we don't

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know where whether it's Los Angeles or

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maybe China and we don't know when

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exactly will be either but it's a good

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thought

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Roger wonderful update and beautiful

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data question about this this possible

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distance effect if you for example look

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at the earthquake data the earthquakes

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are are very physically localized and

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you literally because you've got a span

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all around the world you have you have

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eggs or re G's that are quite some

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distance from a given quake if you plot

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the data as a function of distance from

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a quake averaging over all the quakes

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for particularly obviously the ones that

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are on land is there a distance affected

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there is a small distance effect but the

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one that we know most about has to do

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with the distance between pairs of re

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G's our pair the average pair

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correlation is greater for the re G's

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that are close to each other we do have

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already a suggestion some of an answer

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to your question and it is positive

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there is a drop-off of effect with

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regard with regard to what appears to be

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the focal point of the event yes there's

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one

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is it possible to have a dedicated rag

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for a specific area and somehow in the

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intentionality says however you level or

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think of intentionality you reg will

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only ever respond to any saying from

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that specific spot or event style or you

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like an earthquake and nothing else from

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the intentions you want to put on this

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you think that's conceptually possible

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it given the nature of the meeting and

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that the content of the talks we've been

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listening to I'm inclined to say

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anything is possible but more seriously

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I think the in the nature of the

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question that we ask is very important

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and basically that's what you're talking

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about if we specify the task you so to

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speak or if we task and our AG it's it's

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there is some evidence that they are G

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will be responsive to that tasking you

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could but it wouldn't we wouldn't be

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able to use the same you know material

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as we have here because we're talking

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about pairwise correlations it really is

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a global response we don't know how deep

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that correlation structure goes but at

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least the major stuff is driven by inter

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re G correlations have you done any

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00:19:00,090 --> 00:18:56,950
analysis of as opposed to distance

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cultural connection like if if a culture

390
00:19:05,760 --> 00:19:01,570
feels more connected to where an event

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happens is their response bigger I think

392
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I can answer in the affirmative we

393
00:19:11,100 --> 00:19:09,700
haven't done very much of that but once

394
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in a while there'll be something well

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for example we look at political events

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which and more often than not they're

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us-based political events and we look

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this is usually exploratory not formal

399
00:19:27,480 --> 00:19:25,600
the formal is always asking about the

400
00:19:31,170 --> 00:19:27,490
whole network but if we look at the

401
00:19:34,940 --> 00:19:31,180
local re G's for it to that let's say

402
00:19:38,450 --> 00:19:34,950
the US if we limit the subset we see

403
00:19:41,330 --> 00:19:38,460
typically a little bit larger response

404
00:19:46,129 --> 00:19:41,340
and then we do for the whole network is

405
00:19:47,799 --> 00:19:46,139
that answering your question yeah yeah

406
00:19:50,950 --> 00:19:47,809
we don't have that much sophistication

407
00:19:54,200 --> 00:19:50,960
yet we're working on it

408
00:19:55,879 --> 00:19:54,210
Roger a comment about your wrist I think

409
00:19:57,549 --> 00:19:55,889
appropriate response to the question

410
00:20:00,830 --> 00:19:57,559
over here about could you specify

411
00:20:02,810 --> 00:20:00,840
behavior on the rig's I appreciate your

412
00:20:05,539 --> 00:20:02,820
saying well we're in this group we know

413
00:20:09,470 --> 00:20:05,549
anything is possible but more

414
00:20:12,350 --> 00:20:09,480
specifically the question you ask is so

415
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significant it makes me think of bill

416
00:20:16,159 --> 00:20:13,740
tillers work with intentionally

417
00:20:18,549 --> 00:20:16,169
imprinted electronic devices where he

418
00:20:21,289 --> 00:20:18,559
works with specific very very specific

419
00:20:23,389 --> 00:20:21,299
processes and questions so I would say

420
00:20:27,019 --> 00:20:23,399
the tiller work combined with this work

421
00:20:29,659 --> 00:20:27,029
says the answer is yes is I think it is

422
00:20:31,669 --> 00:20:29,669
definitely yes but it's something that

423
00:20:33,950 --> 00:20:31,679
we have only begun to explore I mean

424
00:20:36,980 --> 00:20:33,960
we've formulated the question and it's a

425
00:20:38,419 --> 00:20:36,990
it needs a little more refinement and

426
00:20:41,090 --> 00:20:38,429
then it'll be a good question which will

427
00:20:42,980 --> 00:20:41,100
have the answer embedded in it bill

428
00:20:45,080 --> 00:20:42,990
Roger do you have any results in the

429
00:20:47,860 --> 00:20:45,090
Chinese Chinese earthquakes did you ever

430
00:20:50,840 --> 00:20:47,870
okay yes the Chinese earthquake is

431
00:20:53,330 --> 00:20:50,850
positive deviation it's not itself

432
00:20:57,799 --> 00:20:53,340
highly significant we have learned over

433
00:21:00,529 --> 00:20:57,809
time slowly that major disasters the

434
00:21:02,750 --> 00:21:00,539
effects on human consciousness and the

435
00:21:05,259 --> 00:21:02,760
emotional state of the world develop

436
00:21:09,769 --> 00:21:05,269
over days not in a few hours that

437
00:21:11,690 --> 00:21:09,779
typically are our events so the answer