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Global Consciousness Project

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Title: Global Consciousness Project


1
Anomalous Anticipatory ResponsesIn Networked
Random Data Roger NelsonPrinceton, New
JerseyFrontiers of Time Reverse Causation --
Experiment and TheoryAAAS Symposium, University
of San Diego, June 2006
  • Global Consciousness Project
  • http//noosphere.princeton.edu

2
Global Consciousness Project (aka The EGG
Project)
  • The People An international collaboration of
  • 100 Scientists, Engineers, Researchers
  • The Tools REG technology, Field applications,
  • Internet communication, Canonical statistics
  • The Question Is there evidence for Non-random
  • Structure where there should be none?

3
Random Event Generator REGReverse Current in
Diode White Noise Electron Tunneling A
Quantum ProcessSample Resulting Voltage, Record
200-Bit Sums
It is like flipping 200 coins and counting the
heads
Binomial Distribution of DataCompared to
Theoretical Normal
Trial Scores 100 7.071 Plotted as a sequence,
1 trial per sec
100 is expected mean
4
A World Spanning Network Yellow dots are host
sites for Eggs
http//noosphere.princeton.edu
5
Internet Transfer to Data Archive in Princeton
Here are data plotted as sequences of 15-minute
block means, for a whole day, from 48 eggs
6
We begin to see whats happening If we plot the
Cumulative Deviations
7
If we average the cumulative deviations Across
REGs we may see a meaningful trend
Expected Trend is Level Random Walk
Cumulative deviation is a Graphical tool to
detect change Process control engineering
8
A Replication Series Of Formal Tests
The Hypothesis Global Events Correlate
with Structure in the Random Data Test
Procedure Pre-defined events, Standardized
Analysis Bottom Line Composite Statistical
Yield
9
Current Result Formal Database, 7.5 Years 204
Rigorously Defined Global EventsOdds About 1
part in 300,000
9/11
10
Now we proceed to new questionsFirst, how good
are the data?
  • Equipment Research quality Design, Materials,
    Shielding, XOR, Calibration standards
  • Errors and Corrections Electrical supply
    failure, component failure. Rare but identifiable
  • Empirical vs Theoretical Mean is theoretical,
    but tiny differences in Variance (expected)
  • Normalization All data standardized empirical
    parameters facilitate comparison and
    interpretation

11
Identify and exclude Bad Trials lt55 or gt145
Identify and exclude device failures, Rotten
Eggs
Identify Individual Rotten Egg
Calculate Empirical Variance for Individual Eggs
REG device failure
Effect of Rotten Eggs on the Full Network
Fully vetted, normalized data
REG device failure
12
Theoretical vs Empirical Distribution (We also
assess pseudorandom clone data, and use
resampling and permutation analyses)
Note These are (0,1) Normal Z-scores The Diffs
are TINY
Negative difference Means that formal Tests are
conservative
13
Three Independent statistics
  The netvar is Mean(zz). It measures the
average pair correlation of the regs ltzzgt
ltzizkgt where i k are different regs and z
is trials for one second. The devvar is Var(z)
the variance across regs Calculated for each
second. The covar is Var(zz). It represents the
variance of the reg pair products zizk
- ltzzgt2 
14
Suggestions of precursor effectsSept 11 2001
Terror Attacks
Stouffer Z across REGs per second Cumulative sum
of deviations from expectation
Variance across REGs per second Cumulative sum of
deviations from expectation
Attacks
Attacks
Attacks
Attacks
Moderately persuasive suggestion that trend may
begin before event
Strong and precise indication that change begins
4 hours before event
15
And very recently, the Indonesian earthquake on
May 27 this year also seems to show evidence of a
precursor response
16
To go further we need a better database
  • Suggestive single cases but low S/N ratio
  • Need replication in multiple samples
  • Impulse events are sharply defined
  • E.g. crashes, bombs, earthquakes

17
Subset of formal series 51 impulse events Epoch
average for covar and devvar mayDepart from
expectation prior to T0
Covar
Devvar
The suggestion of early shift is clearest in
covar
Netvar
18
51 Impulse events, Covar epoch averageDeviation
may begin 2 hours before T0
Approx Slope
19
Impulse events vary -- We need consistencyEarthq
uakes are a precisely defined,Prolific subset of
impulse events They show similar responses
Impulse events shown as Red, Earthquakes as Blue
trace
Netvar
Covar
20
Earthquakes Important to People, Numerous,
Accurately Located, Rigorously Scaled, Precisely
Timed
21
All Earthquakes, Richter 6 or More Select those
on Land with People and Eggs
Selected regions outlined in orange Included
quakes shown as grey dots
Eggs shown as orange spots
Controls shown as blue dots
22
In the Earthquake database, the covar measure
appears to be the most usefulof our three
independent statistics
23
For quakes Rgt6 (grey dots) the covar measure
Responds before and after the primary temblor
Before Mostly Negative
-8 hrs
After Mostly Positive
8 hrs
Average location of quakes in grid square marked
as a colored point Size is cum Z-score Red
positive Blue negative Green no calc, less
than 2 quakes
24
Strong covar response in populated Land areas
where we have eggs
North America and Eurasia
Symmetrical, Significant Z-scores Pre post
25
Null covar response in unpopulated Regions
(ocean) and areas where we have few eggs
Control Quakes in the Oceans
All Z-scores less than 0.5
26
Major earthquakes in populated areas Compared
with quakes in the oceans Covar measure, epoch
average Cum Dev T0 30 hours
Ocean Quakes No structure around T0 Scale of
departure 40 units
North America and Eurasia Significant structure
around T0 Scale of departure 80 units
27
Closer look T0 /- 10 hours
North America Europe and Asia
Unpopulated Ocean regions
Significant structure around T0 Scale of
departure gt 50 units
No structure around T0 Scale of departure 20
units
28
Data split T0 8 Hrs North American vs
Eurasian Quakes Similar structure, independent
subsets
29
The case for an anticipatory response
Magnified central portion
T0 50 hr Raw data
T0
3-Hour Gaussian smooth
Same data as a cumulative deviation
Estimating significance The drop between
T-7 Hrs and T0 Corresponds to a Z score of 4.6
? After Bonferroni correction Compare slope with
3 ? envelope
30
Many questions remain, e.g., Fatal quakes should
be test case. Subset with N gt 5 fatalities and R
gt 5 The picture is less clear.
31
CAUTIONARY NOTES
  • The effects we see are very small, buried in a
    sea
  • of noise. Is signal an appropriate term?
  • Statistical and correlational measures. Need to
  • understand inconsistencies.
  • Fundamental questions remain unanswered.
  • (e.g., effects of N of eggs, Distance, Time).
  • Selectivity of analyses needs balance of
    independent perspectives and replication.
  • We invite efforts to confirm or deny these
    indications.

32
POSSIBILITIES
  • The GCP database of networked random events is
    unique. No other resource like it exists.
  • Opportunity for useful questions and answers.
    Probably holds surprises.
  • Fundamental questions that should be asked are
    known (e. g., N of eggs, Distance, Time).
  • A couple of years of supported analytical
    research would break new ground.

33
GCP Homepage
http//noosphere.princeton.edu
Special Links
Status Day Sum Results Extract
Complementary Perspectives
Web Design Rick Berger
34
  • The following are extras. Some are explanatory,
    some provide additional info.

35
An example of new perspectivesIs there evidence
of periodicity?The generalized short answer is
no. But formal events may show FFT spikes
36
Fourier Spectra and Event EchoesDec 26 2004
Tsunami vs Pseudo Data
Analysis by William Treurniet
The pre-event frame shows a substantial peak
(black trace) Compared with the pseudorandom
data (right panel). And check out post-event
frame 3 (pale bluegreen).
37
EGG Network Response (Quakes on Land) Cumulative
Deviation of CovariancePrimary Temblor / 30
Hours
Control Data Oceans Low Population Zones
North America and Eurasia
Note This is an early figure with somewhat
different Circumscription and hence a different N
of quakes.
38
Epoch or Signal AveragingA tool for revealing
structureIn repeated low S/N ratio events
39
Graphical presentation Cumulative Deviation
Used in Statistical Process Control Engineering
Example, Raw data
Dev from Expectation
Begin Cum Dev from Expectation
40
Raw data and Gaussian smoothed data Quakes on
land T0 30 hours
Raw
3 Hour
Largest spikes are near T0
1 Hour
The crossover is exactly at T0 The minimum is -3
sigma and The maximum is 3 sigma
41
Cumulative deviation of covar for unpopulated
regions (ocean) and areas where we have fewer
eggs
South America
Nippon, East Asia
Control Quakes in the Oceans
No trends, and No structure Related to T0
Range is 1/2 to 1/3 of Land quakes
42
A very early suggestion that the REG data might
show evidence of Precursor response to major
events
-5 minutes
T 0
5
Cumulative Deviation From Expectation
95 confidence
Expectation
Assassination of Prime Minister Rabin, 1995
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