Note added in 2009:
This page reports various exploratory analyses of the 9/11
data. Most
were contributed in the months following the Sept. 11
attacks. In
the ensuing years, we have developed a more sophisticated
understanding of the GCP data, and the analyses below do not
reflect all
that we have learned. One important understanding is that
the average effect size across all events is half a standard
deviation or less. Thus, the experimental effect is in
general too weak for reliable interpretation of individual events.
From careful reanalysis we know that
even in a strong case such as 9/11 the
data are only marginally significant. It was a powerful
event and the graphical displays in these pages seem
impressive, but
even so, the 9/11 results alone should not be taken as proof
of a
global consciousness effect  that requires the patient
accumulation of replications.
Some of the figures here are also in the briefer, primary page on the
disaster in New York, Washington, and Pennsylvania. I want to leave the
redundancy, because that is the simplest way to represent the
development of the analysis and our understanding of the EGG response
to the attacks. This context is a little bit of history, and is a
reflection on the process of learning what is most interesting in a
complex database. We are attempting to extract signal from noise, and
we know that the ratio is very small. We know that the movements
and variations in data from REG instruments used to study a possible
consciousness field are mostly noise, and that any
signal is always mixed with and masked by mere random activity.
Usually we deal with this by a simple procedure, namely by defining
the statistical question prior to the analysis. This allows calculating
a reliable probability that any apparent signal is after all, just
chance fluctuation. We did make two clear predictions of this nature,
and they are included in the formal results for the GCP. A third
prediction was made by Dean Radin, but it was less specific that is
required for full definition of the statistical analysis. It may be
included in the formal database if we can accept it as a proper reflection
of the hypothesis we are testing. Some of Dean's exploratory work is on
this page, but he has consolidated the best examples, together with
other work on time and distance
in a comprehensive report.
Beyond the issues of the formal
scientific work, we wish to explore this extraordinary database quite
freely, looking for especially powerful ways to visualize structure,
that is, signals in the data. Here are the explorations.
On September 11, 2001, beginning at about 8:45 in the morning, a series
of terrorist attacks destroyed the twin towers of the World Trade Center
and severely damaged the Pentagon. The disaster is so great that in New
York we have as yet, two days later, only guesses about how many thousands of
people perished when the WTC towers collapsed.
Commercial airliners were hijacked
and flown directly into the three buildings. The first crashed into the
North tower at 8:45, and about 18 minutes later the second airliner hit
the South tower. At about 9:40, a third airliner crashed into the
Pentagon. At about 9:58, the South tower collapsed, followed by the
North tower at 10:28. At about that same time, the fourth plane crashed
in Pennsylvania. We later learned from reports of cell phone calls that
this was the result of heroic action by the passengers.
The main formal prediction for this event is essentially the same as that made
for the terrorist bombing in Africa in August 1998. That specified a
period beginning a few minutes before the bombing, and including an
aftermath of "a few hours." The actual time was from 10 minutes before
the bombing to three hours after. We use in this case 10 minutes before
the first crash to four hours after, which makes the aftermath period
roughly the same following the last of the major cataclysmic events.
The measure we use is the Chisquare representing the magnitude of the
departure of the eggs' data from theoretical expectation, which is
accumulated over the time defined for the analysis.
The resulting graph of data from the formal prediction
shows a fluctuating deviation during the
moments of the five major events,
as everincreasing numbers of people
around the world are watching and hearing the news in stunned disbelief.
Times of the major events are marked by boxes on the line of zero
deviation. The uncertain fluctuation of the EGG data continues for
almost half an hour after the fall of the second WTC tower.
Then, at about 11:00, the cumulative deviation takes on a powerful trend
that continues through the aftermath period and ultimately
exceeds the significance criterion, with a final probability of 0.035
(Chisquare is 15314 on 15000 degrees of freedom. The number of eggs at
the time of this analysis was 36.) As we will see, this significant
departure from expectation
continues over many more hours.
It is instructive to compare the graph of the same data, but plotted as
the simple cumulative Zscore, which represents the sign of the
deviations and not only their magnitude.
In principle, this display could be completely different in its general
appearance compared with that of the formal measure, which shows the
accumulating absolute deviation. As we see in the following figure,
there is considerable similarity. Again early in the period of
disbelief and shock there is no strong trend, but at about the time of
the collapse of the first tower,
a powerful trend indicating high correlation among
the eggs begins, and persists for two hours. For that period of time
the slope of the line is extraordinary. If it were not selected by
inspection, but had been an a priori prediction, its associated chance
probability would be 0.000075; odds of less than 1 in 10000.
By the end of the day (midnight, GMT)
35 eggs had reported data, and the following
figure looks at the full day in New York, beginning at
midnight, Eastern daylight time, which corresponds to 04:00 GMT.
The scale of hours in this graph indicates the time in New York, and
the major events are marked on the zero line of expectation.
The figure displays the cumulative deviation of
the squared Zscores (the cumulative deviation of Chisquare).
It shows a continuous positive trend which culminates in a
probablity of 0.024 for the 20 hour period. Corresponding
pseudodata computed for this day are included in the
figure for comparison. The trend of the EGG dat begins
well before the first attack, as early as 5 or 6 in the morning.
It is noteworthy that the variance analysis, below, also shows a
striking inflection a few hours before the attack.
Over the next days, we looked at longer periods of data
to see the magnitude of the
response of the EGG network to this tragic, horrifying event. These
figures do not correspond to formal predictions, but are extensions of
the formal method of analysis to
look at the context and achieve a more general understanding.
The next graph shows three days surrounding the attack.
Statistically, this cumulative departure from random behavior is
associated with a probability of 0.005 (261060 on 259200 df).
Next, a longer period of time surrounding September 11, with the attack
marked and a small probability envelope for comparison to see the
sharpness of the increase in nonrandom deviation.
The slope of the graph beginning just before the attack to the end of
the 13th is extreme. An estimate for the probability can be made, and
lies between 0.003 and 0.0003.
The strong slope has a clear trend, and begins with a distinct
inflection at a point well before the attacks began. If we extrapolate
the slope itself, it passes through the inflection at about 04:00 on the
11th, suggesting that the terrorist attacks might have already begun to
register on the EGG network some four or five hours before the first
World Trade Center tower was hit.
A valuable tool for explorations is smoothing, where a average is
calculated within a window that is moved across the data sequence.
This is typically used to see whether there may be a concentration
similar values, or clusters of extremes.
In the September 11 data there is some concentration of strong deviations
around the major events, with a peak at 10:13 EDT. This first figure shows the
raw odds against chance for the squared Stouffer Zscores
for each second of the day.
The maximum Zscore is 4.81 and a Z this
large would appear by chance one time in about two weeks of seconds.
Dean Radin did an independent confirmation. This is his description of
the procedure:
create a Stouffer Z across all eggs per second
create zsquare from the Stouffer Z
consolidate 5 seconds worth of zsquares
create zequivalent and associated odds graph of these 5second chunks
no sliding window
Results essentially replicate your new odds chart. It is also the case,
BTW, that the lowest NEGATIVE z is a mere ~4 minutes after the large
positive spike.
(Note: the five short spikes of equal size indicate the
times of the attacks.)
For the next figure, the data are passed through a moving average using a
smoothing window of one hour width, applied to the Zscores before they
are squared and converted to odds ratios.
Here it appears that there is major structure beginning a short while
after the first WTC tower was hit.
A striking picture is generated when the smoothing is applied
later in the computations directly to the odds ratios. The resulting
picture is remarkable, but the details vary greatly if different window
sizes are chosen. The impressive main peak is actually driven by the
inclusion of the extreme score previously mentioned, because it
dominates each average as the onehour window moves over it.
For a broader perspective, the next set of analyses used a different
measure. Instead of looking at the shift of the mean values of the REG
devices, we ask whether the variability among the eggs changes. Is
there an increase or decrease in the range of scores that may be
correlated with the event of the attack?
The procedure used for visualization is the same as before, but we plot
the accumulated deviation of the variance across the 35 or 36 eggs from
its expected value.
The first figure shows the cumulative deviation of the
variance over the the 20 hours from midnight to 20:00.
No probability calculation has been made
for this figure, but it shows a normal fluctuation around the horizontal
line of expectation until about 05:00,
followed by a precipitous rise, indicating a great excess of variance
continuing until about 11:00. Shortly after, a long period begins
during which the data show an equally impressive deficit of
variance. Again there is an indication that the effects registered for
this horrendous event actually began to be noticeable several hours
prior to the first attack.
John Walker comments that the distinctive shape
of the graph is suggestive of a classic "head and shoulders" graph seen in
stock market analysis.
No probability calculation has been made for this figure,
but the extreme excursion reaches a level of about three sigma, which
corresponds to odds of about 1 in 1000.
For a visual indication of the likelihood that this is merely a random
fluctuation, the automatically generated pseudodata for
September 11 are plotted in the same format for comparison.
In contrast to the real data, there are no longsustained periods of
strong deviation in the algorithmically generated data,
although there is a small positive slope.
Again, a larger context reinforces the impression that the variance
measure is highly unusual around the time of the attacks. The following
graph shows three days centered on the 11th, and shows the corresponding
pseudodata for comparison to the cumulative variance of the actual EGG data.
A longer context is perhaps even more thoughtprovoking. Visually the
next graph is striking in several respects. Because we do not have
a priori expectations or permutation analysis to examine the likelihood
of the trends, any interpretations we make are speculative. With that
acknowledgement, we can note that the cumulative deviation trends suggest
that the spike on the 11th was part of a buildup that began several days
earlier, and took several days after the 11th to return to the level
trends expected for random walks. The primary spike on the 11th is the
most prominent in this context, but note that it is not unique; there are some
other trends that, while not so sharp for so long, are also
visually striking.
I have been continuing to analyze data from the Global Consciousness
Project to confirm and then extend what Roger Nelson and I have found,
associated with the events of 911.
For the technically inclined, the steps in creating the basic zscore
plot were as follows:
0) download raw data from the GCP site (http://teilhard.globalmind.org)
1) calculate an empirical mean and sd for each GCP egg (i.e., RNG), over
each day
2) calculate one zscore per egg, based on above mean and sd, per
second,
using daily empirical mean & sd
3) calculate sum of zsquares for all eggs in nonoverlapping 5minute
periods, per day
4) keep track of number of degrees of freedom (same as # eggs
reporting),
for step 3
5) calculate chisquare for sum of zsquares for 6 hour sliding window,
with right edge of sliding window at "present time"
6) calculate zscore equivalent for step 5
7) draw the plot
This graph shows results for a 6hour sliding window, in terms of z
scores, from Sept 6  13. In this graph, positive z's mean the RNGs
became "more ordered" than expected by chance. Negative z's mean the
RNGs became "more random" than expected by chance. The peak value in
this graph is 9:10 AM, Sept 11. Between the beginning of the tragedy and
7 hours later this data shows a drop of 6.5 sigma (odds against chance of
29 billion to 1). Such large changes will eventually occur by chance, of
course, but this particular change happened during an unprecedented
event, suggesting that this "spike" and "rebound" were not
coincidental.
This shows the onetailed odds against chance associated with the above
zscore plot, in log space. The peak is at 9:10 AM. The "0" in the
xaxis shows the start of each day.
This shows the twotailed odds against chance associated with the above
zscore plot, in log space. The first peak is at 9:10 AM, the second is
at 4:20 PM. I show this to emphasize that unexpected negentropic and
entropic changes both appeared during the crisis.
This shows the result for the zscore plot above when pseudorandom data are
substitued for the real data.
This shows the onetailed odds ratio plot
when pseudorandom data are substitued for the real data.
This shows the twotailed odds ratio plot when pseudorandom data are
substitued for the real data.
A draft report is now available giving
the details of a sequence of analyses by Dean Radin examining the timing
of significant spikes in the data and the effect of the
distance of the eggs from New York and Washington.
More Context
The extraordinary results around the day of the disaster can be seen
more clearly in the context of variations that are found in earlier
data. Here is a picture of a month of data, from July 15 to Sept 16
odds against chance, using empirical mean and sd. The highest peak is
at 9:10 AM 911.
In the process of exploratory analysis, various parameters such as the
width of the sliding window for the moving average. In some respects,
the process is one of seeking an optimal algorithm for extracting signal
from noise. This leads to some combinations that may be unusually
successful, and although the analytical results have to be understood in
context, we think it is worthwhile to show some of these special cases.
Dean's accompanying remarks for the next figure were simply: "odds for
9/6  9/16, wow" The figure shows the onetailed odds ratio
for the 11 day period centered on 11 September.
I was checking descriptions and technical information
and asked whether I had correctly expanded the rather brief
explanation of the figure. Dean responded, "I said wow
because it was the first graph I did where Sept 11 was in the middle of
the graph, and the spike just sits there all by itself, mocking us in our
ignorance of what it means (I know we have some speculations, but sometimes
I think we're more like a couple of clever neurons trying to figure out
what the nature of a brain is)."
Premonition and Prayer
Just an hour and a half before these terrible events transpired,
I had sent one of the occasional updates
to my mailing list of people interested in the Global Consciousness
Project. In it, I said
that it had been rather quiet for the last couple of months, and
ventured that this might be a good sign. Quoting from a followup note,
It is a terrible irony that I should have sent a GCP note with
such an optimistic impression just an hour before the first
explosive crash of the terrorist attacks in New York.
Before the end of this day, I want to say how deeply saddened I am that
this global event occurred, and I pray it will not lead to more
events born of hatred and evil intent.
Please join us all in that prayer.
Since the horrible event, innumerable calls for prayer
have been made. On the 14th of September
there was a special emphasis on such collective spiritual moments,
including major organized
periods of silence in Europe and America. Doug Mast made a specific
and formal prediction for a deviation of the Chisquare "over the time periods
1000 to 1003 GMT, corresponding to a European organized mourning
(http://www.cnn.com/2001/WORLD/europe/09/14/europe.mourning/)
and the time period 1200 to 1203 EDT (1600 to 1603 GMT) corresponding
to the beginning of the Washington service and many
organized mourning events in the Eastern US."
Here is the resulting graph.
The result is very
interesting, I think  a marginally significant *decrease* in
variance. The Chisquare is 150.68 on 180 degrees of freedom, with
probability 0.9455. The trend is steadily opposite to the
usual (and specified)
direction, but I think it somehow looks right  symbolic of
the moment's contrast to the preceding days.
It may be worth noting that this is one of only two cases in the
database of 80 formal GCP predictions where the result goes in the opposite
direction.
There were four planes taken by the terrorists. Only three made it to
their destructive destinations. The fourth was apparently brought
down to crash in western Pennsylvania by passengers who attacked and
overcame the terrorists, in a deliberate sacrifice of their own lives.
We did not make a formal prediction about this event, but it certainly
deserves analysis from the point of view of the EGG network. We do not
have precise timing information, so the times selected for analysis
here are speculative and selected with emphasis on the dramatic trends.
The first of the two following figures shows the data during
what was likely the buildup and the struggle. The spike at the end is
driven by a onesecond trial during which the eggs were so highly
correlated as to produce a Zscore of 4.8, which has odds of less than
1 in a million. Such an extreme score might happen by chance once in
15 days; the odds of ocurring during the 1.5 hour span of the terror
attacks is about 1 in 200.. The second figure shows 15
minutes immediately preceding the crash.
We will never know whether this picture reflects anything of
what was happening in reality, but
I like to imagine it represents an acceptance of the sacrifice.
What is the difference in the Chisquare and Variance graphs.
How does one change the "deviation" calculation to arrive at "variance"?
The Chisquare figures show the cumulative
deviation of the secondbysecond Zscores (squared),
compounded across the N eggs (N=36 to 38 at this time).
That is, for each second, the Z's for all the N eggs
are added and normalized by sqrt(N), then the resulting Z is
squared to yield a Chisquare with 1 df, and finally the
Chisquares1 (Chisq=1 is the expectation) are cumulatively
summed, to represent the departure from expectation.
The Variance figures show something similar, but instead of
the compounded Z across eggs, the variance (squared standard
deviation) is computed across the N eggs for each second.
The sequence of Variance50 (Var=50 is the expectation) is then
cumulatively summed as before.
The Chisquare figure displays extreme departures, in either
direction, of the trial scores of the egg from what is
expected by chance. The Variance figure displays the degree
of variability among the trial scores for the eggs. Chisquare
addresses movement of the central value of the distribution,
Variance represents changes in the range or width of the distribution.
What is the difference in the the analyses by Roger
Nelson and Dean Radin?
The most important difference is in the treatment of the data at the
finest scale. Neither way is superior, but there is a difference in what
is expected or hypothesized about the behavior of the eggs in the
presence of a possible influence. The two perspectives are
complementary, and though they are not fully independent, using both
contributes to our confidence that the apparent effects are not
accidents or mistakes.
For each second, Roger calculates what is called a
Stouffer Z across the eggs as described above.
This means that in order to produce a
large deviation, the eggs have to
have a positive correlation to be doing the same thing. This
composite Z is squared, so it does not matter whether the average value
is shifted to the high or low direction, but there must be some excess
deviation and there must be a tendency
toward interegg consistency in the direction of deviation.
The result is a single squared Zscore,
which is Chisquare distributed, for each second.
Dean calculates a Zscore for each egg separately, and squares these
individual Zscores. He then sums the squared Z's across the eggs,
producing a a single Chisquare for each second. In this
case, the eggs are not expected to show a positive correlation, and a
high score requires only that there is a tendency for excess deviation
in either direction; no interegg consistency in the direction of
deviation is predicted. Again, the result is a single squared Zscore,
which is Chisquare distributed, for each second.
