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I want to talk about the same project

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global consciousness project I've talked

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about before here a number of times but

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very quickly go through the beginnings

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of and the description of the project

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and then worked toward something like

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modeling and theory even though I'm not

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a theorist I do think about those things

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quite a bit

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this is a picture of the gathering in

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New York I think there were about half a

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million people last September on Earth

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Day with a focus on the climate changes

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and so forth and I thought this is a

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kind of advance that we call a global

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event could we possibly capture a

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picture somehow of this the intense

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emotion and interest and shared ideas of

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this gathering in New York that's a

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picture of data which ought to run on a

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kind of level whoops halves at fingers

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like everybody else anyway

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the data should run level but they

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actually are way out of line so to speak

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so I'm thinking to what we're trying to

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do is build a science that can actually

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allow us to capture some aspect of human

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consciousness in a way that we might

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even call quantitative we began thinking

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about these things at least I did in the

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pair lab with experiment where people

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tried to change the behavior of a

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machine with intention no wires no

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buttons we found a significant effect

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when we asked people to get high numbers

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versus low numbers so they produce

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something that would wind up being

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rather far in the high direction

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relative to what was expected similarly

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for the low numbers

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pretty successful highly significant

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differences the next step in the

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progression toward something like

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gathering information about global

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consciousness was to go outside the lab

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and to do what we called field rake or

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appealed our AG experiments we by this

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time benefited from a miniaturization of

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electronics to the point where you could

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put a random number generator on a

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palmtop computer or laptop and easily

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carry it wherever we wanted to go the

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software would record the data

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continuously and then you could press a

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button to mark the beginning and the end

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of an interesting period of time and

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here are a couple of examples and on the

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Left we have a visit with a small group

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to Devil's Tower accompanied by a Native

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American shaman the Shoshone whose

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personal mission was to heal these

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sacred sites that have been kind of

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desecrated in some sense by careless

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thoughtless people he didn't understand

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what we were doing but when I showed him

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that graph he said I think I get the

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idea because he could see that data line

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wasn't going down the middle it was off

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the scale and went to Egypt with a group

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of 19 people who were pretty very much

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interested in the ancient religion and

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spiritual tradition and so forth and we

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went to all of the temples we could the

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ruins and we also went into the Great

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Pyramid and this figure shows in the

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first segment our group entering the

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pyramid and if you look at the trend

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there there isn't a trend that's just

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level not interesting yet but the next

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segment has us in the Queen's Chamber

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doing meditations and chanting and

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following that the Grand Gallery which

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is a fantastic place if you have a

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chance to be in Egypt

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and visit the interior of the Great

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Pyramid I'd say check that out it's my

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favorite place that led to the King's

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Chamber where we did a couple of long

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meditations in these two segments here

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and then this last part is everybody's

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splitting up no longer a group no longer

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working together in any case we think

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that the field re G protocol showed us

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lots of evidence that group

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consciousness it is a kind of natural

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thing normally we don't really notice it

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because when you're in it you can't

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really be observing it very well some

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examples we think about group residence

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afterwards and we call a great meeting

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or an engaging talk that but only

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afterwards during the event we're

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engaged so those are a variety of

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experiment experiences that we could

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think of as group consciousness the next

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step toward the global consciousness

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project is to consider what we learn

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from 12 years of intention experiments

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and another several years of field

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experiments leading to lots of other

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kinds of questions what if you have two

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or more random number generators what if

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they're further away what if they're

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remotely located so we build a network

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to answer a question like this could we

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possibly capture something that you

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could think of as global consciousness

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using the same technology that's what we

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tried to do first with some prototypes

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this is the data from Princess Diana's

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pseudo-random traces it's not off the

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scale hugely but it's very was very

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encouraging so we proceeded to build a

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network that was intended to take data

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every second every day over years and in

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fact we've now been running this network

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which

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as a kind of instrument for looking at

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global consciousness for 1517 years if

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you want to get more detailed by the way

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the the best fastest address is global -

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mind org this our basic hypothesis is a

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overarching hypothesis is a kind of

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operational definition of what we're

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talking about when I say global

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consciousness who knows if there really

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is such a thing but what we are trying

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to do is repeatedly ask a question that

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will allow us altima to say yea or nay

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about an idea like this eventually

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attention and emotion shared by people

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all around the world will correlate with

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changes in our data from this network of

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random number generators and what we do

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to test this is individual experiments

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that are each very specific their

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beginning is identified the end is

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identified of a period of time during

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which we're going to test the data and

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what what we know from the statistics is

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that this the result should be if we

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picture it ran the walk but it very

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often is not a random walk the random

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walk would have a horizontal trend this

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is a good example I'll show you some bad

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examples to where we don't we don't

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always win about 70% of the time however

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we win in the sense that the data go in

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the direction we predict which is upward

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and this kind of graph and about 20% at

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a time or a little bit less than that

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it may be statistically significant by

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the normal 5% criterion

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so we've now collected nearly 500 events

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looking at disasters of various kind

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natural and human-caused acts of war and

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but also celebrations pleasant kinds of

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events

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here's a example of that everybody has

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probably seen if you've seen any of my

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talk but it was definitely an example of

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the world gathering around an event and

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peeling deep strong shared emotion again

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by now you know the data should be

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running level but for about two days the

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data were definitely not running the way

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random data should we looked at this in

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a variety of different ways there's a

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different kind of analysis that look

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instead of at what you might think of as

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a mean shift this is a variance change

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and it also spikes hugely around on that

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day unfortunately there are lots and

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lots of examples of terrorists or

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human-caused disasters I'll just run

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through a bunch of them there are some

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that go completely in the opposite

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direction of what we expect that counts

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against our bottom line but it is a

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formal event so it is part of the

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database and in the long run it turns

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out that we have far more of the kind

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that that match our prediction than

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otherwise fortunately there are some

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other kinds of things that we can can

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look at in the world positive events

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this one is really to my mind very

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interesting when this is the coup Mela

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which happens in India there's a two

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versions of it

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the really large-scale one is every 12

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years or something like that but there's

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one every there there are some in

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between and we've looked at this now

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three times and if you if this

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transparency kind of works you can see

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that there's so much similarity from one

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to another that we could say maybe that

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is the result of 20 million people

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together to do something they really

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feel is important we also look at new

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years every year and we look at a couple

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different ways one of them is by looking

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at the variance of all of our data at

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which we predict will drop down while

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people are beginning to focus on

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midnight and that we use a signal

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averaging to look at all the timezone

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and so forth and this is a selected

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example that is like a kind of perfect

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demonstration of how the data should

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look when it confirms our hypothesis

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there are some years where it doesn't do

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that but overall this analysis shows a

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significant deviation one year after

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another we also have lots of organized

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things in the world most of you either

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have a tender or at least know about

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some kind of event you could go to like

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that piece

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climate change gathering in New York

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that I showed as a first slide we do

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this every year in September September

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21st this is a good example of data that

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don't confirm the hypothesis but most of

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the examples do confirm that hypothesis

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this is year after year of the

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International Day of Peace one of my

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colleagues decided to put together a

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compilation of all the events that have

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people either meditating or praying or

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marching for something like a brighter

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future and he called it a global harmony

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and this is a picture of something like

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a hundred events selected from the

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database all of which sort of matched

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this idea that we should and a lot of

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people do work toward a global harmony

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name is Brian Williams he I think he's a

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member of ss he and hope you might be

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so the bottom line of the data from this

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experiment is can be shown in a scatter

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plot this might not look very impressive

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but there is a small difference between

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the expected dotted dark line black line

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and this blue line which is the average

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of all of the events that we've looked

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at so far it's only one-third of a

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standard deviation away from the

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predicted or expected value for random

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data but because there are 491 events

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that the composite across all of those

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those individual samples has a z score

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of seven that's seven sigma effect so

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it's non-trivial this is exactly the

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same data presented and the format I've

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used for the individual event here you

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can see there are up zag zig zags but

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the trend because of the preponderance

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of data that go in the direction we're

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expecting or predicting it will produces

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a line that just goes further and

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further away from what's expected the

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horizontal trend and this is a way of

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showing how we do controls you can

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sample all of the data which are not in

277
00:14:26,170 --> 00:14:24,740
the events that's about ninety-eight

278
00:14:29,500 --> 00:14:26,180
percent of the data or you can do

279
00:14:34,800 --> 00:14:29,510
something like just a computer

280
00:14:38,740 --> 00:14:34,810
simulation of what we can think of as

281
00:14:41,350 --> 00:14:38,750
pseudo series and that produces a cloud

282
00:14:43,720 --> 00:14:41,360
of data like in this gray these gray

283
00:14:46,090 --> 00:14:43,730
lines and again you can see easily see

284
00:14:51,910 --> 00:14:46,100
that the real data are very different

285
00:14:53,800 --> 00:14:51,920
from from what's in that picture so what

286
00:14:55,630 --> 00:14:53,810
kinds of things are important I'll talk

287
00:14:58,950 --> 00:14:55,640
a little bit about that and then move on

288
00:15:03,340 --> 00:14:58,960
to how it might work mass consciousness

289
00:15:05,860 --> 00:15:03,350
seems to be part of the picture

290
00:15:07,759 --> 00:15:05,870
we need to we're looking for powerful

291
00:15:10,639 --> 00:15:07,769
emotions but shared

292
00:15:14,600 --> 00:15:10,649
and I think it's important and this will

293
00:15:16,609 --> 00:15:14,610
become obvious later when we're talking

294
00:15:19,210 --> 00:15:16,619
about how it might be working the

295
00:15:23,210 --> 00:15:19,220
experimenter has to be willing to accept

296
00:15:26,030 --> 00:15:23,220
the data as they come and we know from

297
00:15:30,139 --> 00:15:26,040
analysis that events that have really

298
00:15:35,090 --> 00:15:30,149
large numbers of people engaged produce

299
00:15:37,039 --> 00:15:35,100
bigger effects than small event yeah an

300
00:15:39,799 --> 00:15:37,049
interesting one that lots of people are

301
00:15:41,419 --> 00:15:39,809
interested to check out is the question

302
00:15:44,470 --> 00:15:41,429
whether a positive event will have a

303
00:15:47,689 --> 00:15:44,480
stronger effect than a negative event is

304
00:15:50,780 --> 00:15:47,699
new years better than a terrorist attack

305
00:15:52,369 --> 00:15:50,790
the answer is that at best you know we

306
00:15:55,160 --> 00:15:52,379
can do this kind of thing by

307
00:15:58,189 --> 00:15:55,170
categorizing they're pretty much similar

308
00:16:01,189 --> 00:15:58,199
either one as long as it gathers us all

309
00:16:03,889 --> 00:16:01,199
together will produce about the same

310
00:16:05,689 --> 00:16:03,899
kind of effect it does need to be

311
00:16:09,229 --> 00:16:05,699
generally speaking something like

312
00:16:12,739 --> 00:16:09,239
intense or unique shocking surprising

313
00:16:15,319 --> 00:16:12,749
arresting or deeply moving so what we're

314
00:16:17,869 --> 00:16:15,329
talking about is emotions but shared

315
00:16:21,280 --> 00:16:17,879
emotions and it turns out I'll show you

316
00:16:24,049 --> 00:16:21,290
a picture of this being awake and aware

317
00:16:25,249 --> 00:16:24,059
allows us to contribute to what we're

318
00:16:28,189 --> 00:16:25,259
thinking of as a kind of global

319
00:16:32,749 --> 00:16:28,199
consciousness but more than when we're

320
00:16:35,960 --> 00:16:32,759
asleep I'll show you a picture of a

321
00:16:38,119 --> 00:16:35,970
moment analysis by Peter Ben self who I

322
00:16:42,579 --> 00:16:38,129
think has talked about and definitely

323
00:16:45,230 --> 00:16:42,589
has published an article or two in JSE

324
00:16:48,769 --> 00:16:45,240
categorizing the many events that we

325
00:16:50,269 --> 00:16:48,779
have we can ask things like this

326
00:16:53,900 --> 00:16:50,279
question about the numbers of people

327
00:16:56,059 --> 00:16:53,910
involved if we do just large and small

328
00:16:58,369 --> 00:16:56,069
the difference is actually significant

329
00:17:01,100 --> 00:16:58,379
but there is a tendency for larger

330
00:17:04,990 --> 00:17:01,110
events to be better we can categorize

331
00:17:08,559 --> 00:17:05,000
events by almost any standard one that I

332
00:17:11,269 --> 00:17:08,569
have done is separate emotions like fear

333
00:17:14,480 --> 00:17:11,279
love compassion and so forth how much

334
00:17:17,659 --> 00:17:14,490
does the event show or embody compassion

335
00:17:18,930 --> 00:17:17,669
turns out that if the events that do

336
00:17:26,370 --> 00:17:18,940
that

337
00:17:30,630 --> 00:17:26,380
just about here's the figure I've been

338
00:17:35,370 --> 00:17:30,640
advertising the blue line that we waves

339
00:17:39,600 --> 00:17:35,380
up and down is data from the events data

340
00:17:45,270 --> 00:17:39,610
collected during the event over and

341
00:17:48,510 --> 00:17:45,280
there's two cycles of 24 hours so over

342
00:17:50,160 --> 00:17:48,520
here where that where the effect size is

343
00:17:53,340 --> 00:17:50,170
smallest is in the middle of the night

344
00:17:56,550 --> 00:17:53,350
about 3:00 in the morning this is 6:00

345
00:18:00,780 --> 00:17:56,560
p.m. I guess everybody is like getting

346
00:18:02,250 --> 00:18:00,790
ready to eat or something okay and down

347
00:18:05,600 --> 00:18:02,260
below is a kind of what you might think

348
00:18:09,120 --> 00:18:05,610
of as the the rest of the picture that's

349
00:18:12,780 --> 00:18:09,130
when we're there are none no events it's

350
00:18:15,600 --> 00:18:12,790
just what the data look like normally so

351
00:18:18,120 --> 00:18:15,610
we are contributing to whatever's going

352
00:18:24,420 --> 00:18:18,130
on in these data when were awake much

353
00:18:26,760 --> 00:18:24,430
more stronger than when we're asleep a

354
00:18:29,010 --> 00:18:26,770
long perspective if we look at all the

355
00:18:34,980 --> 00:18:29,020
data not just the ones in the events we

356
00:18:37,170 --> 00:18:34,990
have a figure that some some somebody

357
00:18:41,580 --> 00:18:37,180
contacted me said I was looking for

358
00:18:44,490 --> 00:18:41,590
something that was familiar had a

359
00:18:51,120 --> 00:18:44,500
familiar form to the graph that you call

360
00:18:54,810 --> 00:18:51,130
your long long term picture

361
00:19:00,720 --> 00:18:54,820
you said the dollar index seems to track

362
00:19:06,540 --> 00:19:00,730
that pretty well so we I did the graphs

363
00:19:07,710 --> 00:19:06,550
and or he did and it turns out that I

364
00:19:11,460 --> 00:19:07,720
guess you can't see it for some reason

365
00:19:14,430 --> 00:19:11,470
on this figure but it continues up to

366
00:19:16,440 --> 00:19:14,440
now I should note that presidential

367
00:19:18,690 --> 00:19:16,450
approval ratings track about the same

368
00:19:22,170 --> 00:19:18,700
way so this is just correlation not

369
00:19:24,960 --> 00:19:22,180
causation so a couple of different kinds

370
00:19:28,240 --> 00:19:24,970
of models seem to be

371
00:19:31,000 --> 00:19:28,250
most likely or at least lots of people

372
00:19:34,659 --> 00:19:31,010
propose them one of them is that this is

373
00:19:36,669 --> 00:19:34,669
an old experimenter effect and it's I

374
00:19:38,409 --> 00:19:36,679
try to be agnostic about it but it seems

375
00:19:42,159 --> 00:19:38,419
to me to be very unlikely I'm

376
00:19:44,440 --> 00:19:42,169
responsible for all the big rather large

377
00:19:48,700 --> 00:19:44,450
changes in data in a network that

378
00:19:50,580 --> 00:19:48,710
expands or covers the whole world there

379
00:19:55,890 --> 00:19:50,590
some of the arguments are though like

380
00:20:01,539 --> 00:19:55,900
Helmut Schmidt said well a more familiar

381
00:20:03,220 --> 00:20:01,549
idea is feedback from the future it may

382
00:20:06,490 --> 00:20:03,230
said my prediction Roger Nelson

383
00:20:08,409 --> 00:20:06,500
predictions are better than the other

384
00:20:10,030 --> 00:20:08,419
people make predictions and it turns

385
00:20:11,100 --> 00:20:10,040
they are but it's not a significant

386
00:20:15,100 --> 00:20:11,110
difference

387
00:20:17,320 --> 00:20:15,110
Peter been cell says there can't be any

388
00:20:20,140 --> 00:20:17,330
side without intention because we have

389
00:20:22,570 --> 00:20:20,150
an XOR I don't have time to go into the

390
00:20:23,950 --> 00:20:22,580
details but I think that there's a

391
00:20:27,100 --> 00:20:23,960
problem in that kind of reasoning

392
00:20:30,250 --> 00:20:27,110
because we there may be something going

393
00:20:34,530 --> 00:20:30,260
on there's not just bits

394
00:20:40,980 --> 00:20:34,540
moving on to and the another kind of

395
00:20:45,430 --> 00:20:40,990
possible source we have evidence that

396
00:20:49,720 --> 00:20:45,440
there can be something like PK happening

397
00:20:51,730 --> 00:20:49,730
and the result is correlation of between

398
00:20:54,610 --> 00:20:51,740
these devices which are separated by

399
00:20:57,490 --> 00:20:54,620
thousands of kilometers and we have

400
00:21:03,280 --> 00:20:57,500
about a dozen different parameters that

401
00:21:05,440 --> 00:21:03,290
won't fit the the model of the

402
00:21:08,320 --> 00:21:05,450
experimenter effect but will fit into a

403
00:21:11,799 --> 00:21:08,330
PK model I'm sorry I have run out of

404
00:21:14,409 --> 00:21:11,809
time so I won't be able to talk about

405
00:21:19,360 --> 00:21:14,419
this in any kind of detail but I would

406
00:21:21,280 --> 00:21:19,370
at it I'm thinking about and more and

407
00:21:23,890 --> 00:21:21,290
more deeply convinced that a model based

408
00:21:26,860 --> 00:21:23,900
on something like David bones implicit

409
00:21:28,360 --> 00:21:26,870
order might make some sense that what

410
00:21:33,290 --> 00:21:28,370
we're really talking about is something

411
00:21:36,020 --> 00:21:33,300
like active information

412
00:21:39,950 --> 00:21:36,030
that can be actualized if there's a need

413
00:21:42,530 --> 00:21:39,960
for it and experiments which provide a

414
00:21:44,450 --> 00:21:42,540
need for the information that could

415
00:21:48,830 --> 00:21:44,460
structure what's happening to say a

416
00:21:53,870 --> 00:21:48,840
random number generator or a system that

417
00:21:56,840 --> 00:21:53,880
produces correlations and so it could be

418
00:21:59,050 --> 00:21:56,850
that there's active information then is

419
00:22:02,030 --> 00:21:59,060
actualized because there's a need for it

420
00:22:03,620 --> 00:22:02,040
created by an experimenter so there is

421
00:22:06,950 --> 00:22:03,630
an experimenter effect but I think it's

422
00:22:19,820 --> 00:22:06,960
just in doing the experiment so thank

423
00:22:22,250 --> 00:22:19,830
you in the healing research that I do i

424
00:22:25,160 --> 00:22:22,260
I've come to the reasonably similar

425
00:22:26,810 --> 00:22:25,170
conclusion that healing is not something

426
00:22:31,030 --> 00:22:26,820
that happens to just between two people

427
00:22:33,920 --> 00:22:31,040
it's rather a response to need yeah I

428
00:22:38,060 --> 00:22:33,930
said I think healing is more a response

429
00:22:40,730 --> 00:22:38,070
to need and so I'm wondering in the

430
00:22:45,860 --> 00:22:40,740
spirit of connections that could be all

431
00:22:47,810 --> 00:22:45,870
over the place Roger with regard I want

432
00:22:50,150 --> 00:22:47,820
to ask the question about polarity you

433
00:22:52,520 --> 00:22:50,160
talked about sometimes the curves go up

434
00:22:55,550 --> 00:22:52,530
and sometimes the curves go down and my

435
00:22:56,990 --> 00:22:55,560
understanding is that and and please

436
00:23:01,130 --> 00:22:57,000
correct me but my understanding is that

437
00:23:04,340 --> 00:23:01,140
you have a bunch of microscopic

438
00:23:07,850 --> 00:23:04,350
measurements you're making and then you

439
00:23:11,480 --> 00:23:07,860
do some kind of manipulation on that to

440
00:23:12,980 --> 00:23:11,490
try to eliminate drift in your

441
00:23:17,800 --> 00:23:12,990
instrument and bias and all that kind of

442
00:23:21,080 --> 00:23:17,810
stuff so one way to say that is you're

443
00:23:23,330 --> 00:23:21,090
you're making a measurement of many many

444
00:23:24,710 --> 00:23:23,340
variables all those bits that are going

445
00:23:27,290 --> 00:23:24,720
to get X or it or whatever you talk

446
00:23:30,680 --> 00:23:27,300
about that's a measurement in some high

447
00:23:32,330 --> 00:23:30,690
dimensionality space it 56

448
00:23:33,860 --> 00:23:32,340
dimensionality space or 64

449
00:23:36,470 --> 00:23:33,870
dimensionality space or whatever it is

450
00:23:38,750 --> 00:23:36,480
and then you're choosing some direction

451
00:23:40,930 --> 00:23:38,760
in that 64 dimensional space and you're

452
00:23:42,470 --> 00:23:40,940
saying I'm going to call this direction

453
00:23:43,909 --> 00:23:42,480
positive and then the Oh

454
00:23:46,940 --> 00:23:43,919
direction is negative or something like

455
00:23:49,659 --> 00:23:46,950
that and so I want to ask you know how

456
00:23:54,370 --> 00:23:49,669
do you choose that orientation of your

457
00:23:58,280 --> 00:23:54,380
of your vector in that hilbert space and

458
00:24:02,480 --> 00:23:58,290
does that affect what the positive or

459
00:24:05,720 --> 00:24:02,490
negative means in your result I chose

460
00:24:07,430 --> 00:24:05,730
the directions for the predictions in a

461
00:24:09,530 --> 00:24:07,440
kind of three dimensional space in which

462
00:24:12,140 --> 00:24:09,540
we did these field ret experiments for a

463
00:24:14,900 --> 00:24:12,150
long time what we knew was that we

464
00:24:17,360 --> 00:24:14,910
weren't there wasn't an intention to

465
00:24:19,520 --> 00:24:17,370
push the data in one direction or the

466
00:24:21,830 --> 00:24:19,530
other direction so we're asking is is

467
00:24:25,159 --> 00:24:21,840
there an expansion the increase in the

468
00:24:29,450 --> 00:24:25,169
variant and that I plot as a as a an

469
00:24:33,130 --> 00:24:29,460
increasing deviation I trend away from

470
00:24:42,350 --> 00:24:33,140
in the positive direction so I don't

471
00:24:44,960 --> 00:24:42,360
know about 54 dimensions Thanks I was

472
00:24:50,480 --> 00:24:44,970
was hoping to clarify something I didn't

473
00:24:53,419 --> 00:24:50,490
quite understand about the about the way

474
00:24:55,640 --> 00:24:53,429
you do local time zones and things like

475
00:25:03,760 --> 00:24:55,650
the analysis of bigger effects when

476
00:25:06,950 --> 00:25:03,770
people are awake so how do you do the

477
00:25:10,130 --> 00:25:06,960
time zone analysis with New Year's Eve

478
00:25:12,049 --> 00:25:10,140
when you're apparently analyzing

479
00:25:13,730 --> 00:25:12,059
something in local time zones but you've

480
00:25:17,539 --> 00:25:13,740
got a network scattered all over the

481
00:25:20,060 --> 00:25:17,549
world you should talk with Peter pencil

482
00:25:22,760 --> 00:25:20,070
for the details about it but essentially

483
00:25:25,970 --> 00:25:22,770
what it amounts to is looking at the

484
00:25:29,210 --> 00:25:25,980
data which are correlations between

485
00:25:31,250 --> 00:25:29,220
these devices in a time zone while

486
00:25:35,810 --> 00:25:31,260
people are awake and then concatenate

487
00:25:37,549 --> 00:25:35,820
the ones concatenating the corresponding

488
00:25:41,060 --> 00:25:37,559
ones in the next time zone when people

489
00:25:46,159 --> 00:25:41,070
are awake does that answer the question

490
00:25:48,289 --> 00:25:46,169
I think so you can't possibly answer

491
00:25:51,500 --> 00:25:48,299
this here but please put all of your

492
00:25:52,460 --> 00:25:51,510
analysis details in on a JSC article

493
00:25:55,610 --> 00:25:52,470
sometime soon

494
00:25:58,490 --> 00:25:55,620
yeah okay

495
00:26:00,440 --> 00:25:58,500
currently it sounds like you're looking

496
00:26:02,029 --> 00:26:00,450
at world events and then backtracking

497
00:26:03,680 --> 00:26:02,039
that to the data to find the the

498
00:26:06,260 --> 00:26:03,690
correlation across these different re

499
00:26:08,630 --> 00:26:06,270
G's right is there a way given your

500
00:26:11,870 --> 00:26:08,640
current state with the technology to

501
00:26:14,630 --> 00:26:11,880
regionalize and network the re GS

502
00:26:15,980 --> 00:26:14,640
looking for local regional coherence and

503
00:26:17,330 --> 00:26:15,990
then have some kind of signaling

504
00:26:19,789 --> 00:26:17,340
mechanisms say hey there's a lot of

505
00:26:21,919 --> 00:26:19,799
coherence within you know you know this

506
00:26:24,919 --> 00:26:21,929
particular region and then back that

507
00:26:25,610 --> 00:26:24,929
track that to a concurrent event hmm

508
00:26:29,029 --> 00:26:25,620
does that make sense

509
00:26:31,100 --> 00:26:29,039
well the last I'm not so sure about the

510
00:26:34,430 --> 00:26:31,110
last part but the first part yes it is

511
00:26:36,560 --> 00:26:34,440
possible I'm hoping that other people

512
00:26:38,419 --> 00:26:36,570
will be interested in in a sense

513
00:26:41,810 --> 00:26:38,429
replicating building a better network

514
00:26:44,690 --> 00:26:41,820
with now today this was started so long

515
00:26:47,480 --> 00:26:44,700
ago that storing a gigabyte or 10

516
00:26:50,480 --> 00:26:47,490
gigabytes was a lot of storage right so

517
00:26:51,680 --> 00:26:50,490
we need a lot more detailed information

518
00:26:54,320 --> 00:26:51,690
to do the kind of thing that you're

519
00:26:56,570 --> 00:26:54,330
talking about there needs to be I think

520
00:26:58,390 --> 00:26:56,580
a fairly a substantial number in each of

521
00:27:02,180 --> 00:26:58,400
the regions you might be interested in

522
00:27:04,360 --> 00:27:02,190
in order to get a you know big sample

523
00:27:08,029 --> 00:27:04,370
where you have lots of correlations that

524
00:27:09,380 --> 00:27:08,039
might or might not occur and I think the

525
00:27:13,880 --> 00:27:09,390
last thing you said was something like

526
00:27:15,560 --> 00:27:13,890
look for an event and then find look for

527
00:27:18,020 --> 00:27:15,570
a deviation in the data and then look

528
00:27:18,980 --> 00:27:18,030
for the event we can't afford to do that

529
00:27:21,680 --> 00:27:18,990
we don't do that

530
00:27:23,810 --> 00:27:21,690
because the world is very complicated so

531
00:27:25,760 --> 00:27:23,820
if you find a spike in the data and

532
00:27:27,980 --> 00:27:25,770
start looking around you'll find that's

533
00:27:29,360 --> 00:27:27,990
why I meant regionally coordinating so

534
00:27:31,130 --> 00:27:29,370
looking if you had some kind of

535
00:27:33,830 --> 00:27:31,140
mechanism to to sample the different

536
00:27:35,450 --> 00:27:33,840
RTGS in real time and then narrow your

537
00:27:37,760 --> 00:27:35,460
regional space so let's say I'm looking

538
00:27:40,190 --> 00:27:37,770
at only re G's in Texas for example and

539
00:27:42,020 --> 00:27:40,200
then looking for a regional event in

540
00:27:43,370 --> 00:27:42,030
Texas that you know some having some

541
00:27:44,899 --> 00:27:43,380
kind of signaling mechanism to say

542
00:27:47,750 --> 00:27:44,909
there's a lot of coherence you know in

543
00:27:49,940 --> 00:27:47,760
this slice of time is there a concurrent

544
00:27:51,620 --> 00:27:49,950
ongoing event in that region of just

545
00:27:52,820 --> 00:27:51,630
these you know three or four re G's for

546
00:27:55,190 --> 00:27:52,830
example that make sense

547
00:27:57,440 --> 00:27:55,200
well we actually do have a very

548
00:27:58,940 --> 00:27:57,450
beginning you know like a rough kind of

549
00:28:02,270 --> 00:27:58,950
approximation to that if I'm

550
00:28:03,950 --> 00:28:02,280
understanding it correctly I mentioned

551
00:28:05,080 --> 00:28:03,960
small events large events what the

552
00:28:07,270 --> 00:28:05,090
smaller wins really

553
00:28:10,270 --> 00:28:07,280
is something that's regional that

554
00:28:12,880 --> 00:28:10,280
somebody really wants to find out if

555
00:28:14,860 --> 00:28:12,890
this event makes an effect on the global

556
00:28:17,320 --> 00:28:14,870
network and the analysis that we

557
00:28:20,410 --> 00:28:17,330
formerly do covers the whole network but

558
00:28:22,960 --> 00:28:20,420
when you start looking at the effect of

559
00:28:25,870 --> 00:28:22,970
the distance separating these re G's you

560
00:28:28,660 --> 00:28:25,880
find that it matters if the event is a

561
00:28:33,100 --> 00:28:28,670
small one what that suggests is that the

562
00:28:34,180 --> 00:28:33,110
effect is local to some degree people in

563
00:28:36,880 --> 00:28:34,190
the rest of the world don't know about

564
00:28:39,820 --> 00:28:36,890
it even so the people who do know about

565
00:28:42,400 --> 00:28:39,830
it in that region have some effect on

566
00:28:44,860 --> 00:28:42,410
the device so if you set if you now do a

567
00:28:47,350 --> 00:28:44,870
correlation with a further separated re

568
00:28:51,250 --> 00:28:47,360
G it will be weaker because there's no