1. Global Consciousness Project http://noosphere.princeton.edu http://noosphere.princeton.edu Anomalous Correlations In Networked Random Data Evidence for Consciousness Fields? Anomalous Correlations In Networked Random Data Evidence for Consciousness Fields? PPPL Colloquium, Oct 11 2006 Roger Nelson Princeton, New Jersey

2. Tools for Anomalies Research PEAR Laboratory, Princeton University The Benchmark REG Experiment Aircraft engineering

3. Random Event Generator – REG Reverse Current in Diode: White Noise Electron Tunneling – A Quantum Process Sample Resulting Voltage, Record 200-Bit Sums Binomial Distribution of Data Compared to Theoretical Normal Trial Scores: 100 ± 7.071 Plotted as a sequence, 1 trial per sec 100 is expected mean It is like flipping 200 coins and counting the heads

4. Laboratory Experiments, PEAR: Intention to Change the REG Behavior High and Low Both Depart From Expectation HI LO BL

5. Portable Random Event Generator (REG or RNG) Mindsong REG Orion RNG

6. Field REG Experiments: Take Portable REG With Palmtop Computer Into the Field Resonant Vs Mundane Situations

7. Extend to Global Dimensions 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?

8. A World Spanning Network Yellow dots are host sites for Eggs http://noosphere.princeton.edu

9. 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

10. We begin to see what’s happening If we plot the Cumulative Deviations

11. 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

12. Three Independent statistics The netvar is Mean(zz). It measures the average pair correlation of the regs: = 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: { z[i]*z[k] - }^2

13. First, how good are the data ?  Equipment: Research quality Design, Materials, Shielding, XOR, Calibration standards  Errors and Corrections: Electrical supply failures, component failures. Rare but identifiable  Normalization: All data standardized; empirical parameters facilitate comparison and interpretation  Empirical vs Theoretical: Mean is theoretical, but tiny differences in Variance (expected)

14. Identify and exclude “Bad Trials” 145 and Device failures, “Rotten Eggs” >> Empirical Normalization Identify Individual “Rotten Egg” Effect of “Rotten Eggs” on the Full NetworkFully vetted, normalized data Calculate Empirical Variance for Individual Eggs REG device failure

15. Theoretical vs Empirical Distribution (We also assess pseudorandom clone data, and use resampling and permutation analyses) Negative difference Means that formal Tests are conservative Note: These are (0,1) Normal Z-scores The Diffs are TINY

16. 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 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

17. Current Result: Formal Database, 8 Years 212 Rigorously Defined Global Events Odds: About 1 part in 500,000 9/11

18. Examples: Tragedies and Manmade Disasters

19. Examples: Tragedies and Manmade Disasters (Sometimes we see no apparent effect when we think we should) Signal to Noise ratio Is small, so Effects may Be buried; Noise may Masquerade As signal

20. Examples: Natural disasters too: Indonesian Earthquake on May 27 2006 (Note that the response seems to begin early)

21. Examples: New Year’s Celebration Device Variance Decreases Near Midnight One especially clear case Average over 8 years

22. Examples: Effects of Large Scale Organized Meditations? Correlation Replication Application

23. Examples: September 11 2001 Extreme deviations from expectation Largest spike in 3 years

24. A Deeper Examination: Suggestions of Precursor Effects In Data for Sept 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 Moderately persuasive suggestion that trend may begin before event Strong and precise indication that change begins 4 hours before event Attacks Attacks Attacks Attacks

25. Rigorous look at Possible Anticipatory Response  Suggestive single cases but low S/N ratio  Need replication in multiple samples  “Impulse” events are sharply defined  E.g. crashes, bombs, earthquakes

26. 51 Impulse events, Covar epoch average Deviation may begin ~ 2 hours before T=0 Approx Slope

27. Impulse events vary – need more consistency Earthquakes are a precisely defined, Prolific subset of impulse events They show similar responses Impulse events shown as Red, Earthquakes as Blue trace NetvarCovar

28. All Earthquakes, Richter 6 or More Select those on Land with People and Eggs Eggs shown as orange spots Selected regions outlined in orange Included quakes shown as grey dots Controls shown as blue dots

29. Strong covar response in populated Land areas where we have eggs North America and Eurasia Significant Z-scores Pre & post But not when the quakes Are in the oceans

30. Major earthquakes in populated areas Compared with quakes in the oceans Covar measure, epoch average Cum Dev T=0 ± 30 hours Ocean Quakes No structure around T=0 Scale of departure ~ 40 units North America and Eurasia Significant structure around T=0 Scale of departure ~ 80 units

31. Data split: T=0 ± 8 Hrs North American vs Eurasian Quakes Similar structure, independent subsets

32. T=0 ± 50 hr Raw data Magnified central portion Same data as a cumulative deviation Estimating significance: Estimating significance: The drop between T-8 Hrs and T=0 Corresponds to a Z score of 4.6  After Bonferroni correction Compare slope with 3  envelope The case for an anticipatory response T=0 3-Hour Gaussian smooth

33. 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. We need the balance of independent perspectives and replication. We invite efforts to confirm or deny these indications. The data are all available online.

34. New Work: Sliding the Event Time Two independent measures track In subset of events engaging large numbers Netvar blue Covar red Analysis Peter Bancel, Oct 2006

35. Sliding the Event Time: Independent measures do not track In simulated events created by resampling Netvar: Z=0.3 covar Analysis: Peter Bancel, Oct 2006

36. A Surprising, Long-term Trend Independent Correlation With a Sociological Measure? 9/11

37. GCP Homepage Status Day Sum Results Extract Special Links Complementary Perspectives http://noosphere.princeton.edu Web Design Rick Berger

38. An example of new perspectives: Is there evidence of periodicity? The generalized short answer is no. But formal events may show FFT spikes

39. Fourier Spectra and Event Echoes Dec 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).

40. Epoch or Signal Averaging A tool for revealing structure In repeated low S/N ratio events

41. Graphical presentation: Cumulative Deviation Used in Statistical Process Control Engineering Begin Cum Dev from Expectation Example, Raw data Dev from Expectation

42. Subset of formal series: 51 impulse events Epoch average for covar and devvar may Depart from expectation prior to T=0 The suggestion of early shift is clearest in covar Netvar Devvar Covar

43. In the Earthquake database, the covar measure appears to be the most useful of our three independent statistics

44. Closer look: T=0 +/- 10 hours North America Europe and Asia Significant structure around T=0 Scale of departure > 50 units No structure around T=0 Scale of departure ~ 20 units Unpopulated Ocean regions

45. For quakes R>6 (grey dots) the covar measure Responds before and after the primary temblor 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 -8 hrs +8 hrs Before Mostly Negative After Mostly Positive

46. Many questions remain, e.g., Fatal quakes should be test case. Subset with N > 5 fatalities and R > 5 The picture is less clear.

47. POSSIBILITIESPOSSIBILITIES 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.