Real-time Strangeness Detector




Strangeness Detector

Observing Strangeness

This Strangeness Detector allows real-time display and analysis of non-random activity in a pseudo random number generator (PRNG). It can be used for:

  • Detecting and monitoring strange (non-random) spontaneous PRNG output at particular times.
  • Detecting whether the randomness of PRNG output can be affected by groups of people who focus their minds on a common activity.
  • Learning and testing psychokinetic (mind over matter) abilities, by deliberately trying to induce non-random behavior in the PRNG.

Research carried out as part of the Global Consciousness Project (also known as the EGG Project) has suggested that the behavior of random event generators (REGs) can be affected by important world events.

For example it was found that REGs behaved non-randomly shortly before, during, and for some days after the September 11 terrorist attacks. Non-random activity in the REGs has also been reported during other high interest news events, such as award ceremonies, sporting competitions, jury verdicts in major criminal cases, natural disasters, and New Year celebrations.

One explanation that has been put forward to explain this phenomenon is that the shared consciousness of large groups of people who are focussing their minds on the same event somehow affects the random activity in the REGs. Evidence has also been reported that synchronised meditation by groups of people can affect the output of REGs.

REGs have been extensively used to assess psychokinetic abilities. In such cases, individuals or groups usually focus their intention on producing a particular output that is controlled by the REG. This may include an attempt to affect the overall randomness of the REG output.




Real-Time Strangeness Detector


Sampling Period: 


Real-time Output



 
 
 


Prob (p)
0.000001
0.000002
0.000005
0.00001
0.00002
0.00005
0.0001
0.0002
0.0005
0.001
0.002
0.005
0.01
0.02
0.05
0.10
0.20
0.50
1.00
*

 

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  Frequency Distribution
 
Freq 
 
5
5




1
4
5
6
0




1
4
0
6
5




1
3
5
7
0


B

1
3
0
7
5


I

1
2
5
8
0


N

1
2
0
8
5


A

1
1
5
9
0


R

1
1
0
9
5


Y

1
0
5
1
0
0



1
0
0
1
0
5

S


9
5
1
1
0

P


9
0
1
1
5

L


8
5
1
2
0

I


8
0
1
2
5

T


7
5
1
3
0




7
0
1
3
5




6
5
1
4
0




6
0
1
4
5




5
5








How Strangeness Detector Works

Once you click Start, the program generates a series of 200 pseudo-random binary digits every second. The frequencies of these two digits are compared statistically using a z test to see whether they differ significantly from the equal numbers expected by chance (i.e., 100 of each on average).

If the probability (p) associated with the outcome is less than 0.05 (1 in 20), this is said to indicate a moderate degree of strangeness (shown by an amber bar).

If p is less than 0.001 (1 in 1000), this is said to indicate a high degree of strangeness (shown by a red bar).

Allow Strangeness Detetector to run for a period of time and watch for any non-random activity (amber or red bars). Real-time output is displayed every second and the cumulative data are shown in the thick bar on the right (with continually updated statistical analysis presented at the bottom).

Highly strange events (HSE), i.e., those with a probability less than 0.001, are shown in the text box. You can cut and paste the data for HSEs if you wish to keep a permanent record.

Also displayed is the continually updated frequency distribution for the various binary splits (i.e., a graph of the frequencies for each combination of the 200 binary digits). Again the combinations are color-coded to indicate degree of strangeness for particular outcomes.

If Strangeness Detector is allowed to run for a long period of time then, without any evidence of overall strangeness, the graph should approximate a normal distribution (bell-shaped curve). When Strangeness Detector is stopped, the data for the frequency distribution are displayed in the text box below below the graph. You may cut and paste these data if further analysis is required.

Technical Notes

The REGs used in the EGG Project are true random event generators, where output is the result of a theoretically indeterminate process such as electronic noise. In contrast, the outcomes generated by Strangeness Detector are pseudo random (they are produced by the JavaScript Math.random algorithm). Also the EGG project pools data from several REGs, whereas Strangeness Detector uses only one PRNG.

Because of the different methods of random number generation (PRNG vs REG), one important research possibility is to use Strangeness Detector as a control in Global Consciousness (EGG) research. In this way the PRNG output of Strangeness Detector can be compared with the true REG output from the Global Consciousness Project. In theory, this might help to determine whether the global consciousness phenomena are due to synchronicity (which may also affect PRNG output) or to the genuinely causal influence of consciousness on REGs.

When using Strangeness Detector, data from consecutive seconds are pooled to calculate cumulative results. To do this, the z values for each second are squared (to produce Chi-Square values), and the Chi-Squares are then summed across all seconds. The resulting sum of Chi-Squares is tested for significance with degrees of freedom (df) = number of seconds. If the resulting probability (p) is less than 0.05 or 0.001, this is said to indicate a moderate or high degree of overall strangeness (shown by a thick amber or red bar on the right).



Strangeness Detector

Use psychokinesis to create strange events










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