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Applications of Decision Augmentation Theory
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intentions may be calculated by repeating the individual likelihood calculation for each intention, and
the total likelihood will simply be the product of factors such as (6) above for each of the three inten-
tions.”
That statement is simply incorrect. A combined likelihood is found by multiplying the individual likeli-
hoods only if the random variables are independent of each other (DeGroot, 1986, p. 145). Clearly, the
rows of the table are not independent.
In fact, if you know any two of the rows, the third is determined
exactly! The correct likelihood ratio needs to build that dependence into the formula.
A second technical problem with the conclusion that the data support the influence model
is that the
method itself strongly supports the influence model. As noted by Dobyns, “In fact, applying the test to
data sets that, by construction, contain no effect, yields strong odds (ranging, in a modest Monte Carlo
database, from 8.5 to over 100) in favor of the influence model (page 268).” The actual data in his paper
yielded odds of 28.9 to one in favor of the influence model; however, this value
is well within the re-
ported limits from his “influence-less” Monte Carlo data.
Under DAT it is possible that/lC-mediated selection might occur at the protocol level, but the primary
way is through timing—initiating a run to capitalize upon a locally deviant subsequence. How this might
work in dynamic RNG devices is clear; wait until such a deviant sequence is in your immediate future
and initiate the run in time to capture
it.
With “static” devices, such as PEAR’s random mechanical
cascade (RMC) device, how timing enters in
is less obvious. Under closer inspection, however, even
with the RMC device there is a statistical variation among unattended control runs.
That
is, there is
never a series of control runs that give exactly the same mean. Physical effects, such as Browian motion,
temperature gradients, etc., can account for the observed variance in the absence of human operators.
Thus, when a run
is initiated to capture favorable local “environmental” factors, even for “static” de-
vices, remains the operative issue with regard to DAT Dobyns does not consider this case at all in his
analysis.
If DAT enters in at the protocol selection, as it probably does, it is likely to be a second-order
contribution because of the limited possibilities from which to select (i.e., six in the tripolar case).
Finally, a major problem with Dobyns’ conclusion, which was pointed out when he first presented this
paper at a conference (May, 1990),
is that the likelihood ratio supports the influence model even for
their PRNG data. Dobyns dismisses this finding (page 268) all too easily given the preponderance of
evidence that suggest that no influence occurs during PRNG studies (Radin and May, 1986).
Aside from the technical flaws in Dobyns’ likelihood ratio arguments, and even ignoring the problem
with the PRNG analysis, we reject his conclusions simply because they hold in favor of influence even in
Monte Carlo-constructed and unperturbed data.
Circumstantial Evidence Against an AP Model for RNG Data
Experiments with hardware RNG devices are not new.
In fact, the title of Schmidt’s very first paper on
the topic (1969) portended our conclusion, “Precognition of a Quantum Process.” Schmidt listsP/Cas a
third option after two possible sources for precognition, and remarks, “The experiments done so far do
not permit a distinction (if such a distinction is at all meaningful) between the three possibilities.” From
* Dobyns agrees on this point—private communication.
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1969 onward, the RNG research has been strongly oriented toward a PK model. The term micro-PK,
itself, embeds the force concept further into the lexicon of RNG descriptions.
In this section, we examine a number of RNG experimental results that provide circumstantial evidence
against th&AP hypothesis. Any single piece of evidence could be easily dismissed; however, taken to-
gether, they demonstrate a substantial case against/I/
.
Internal Complexity of RNG Devices and Source Independence
Schmidt (1974) conducted the first experiment to explore potential dependencies upon the internal
workings of his generators. Since by definition AP implies a force or influence, it seemed reasonable to
expect that an influence should depend upon the details of the target system.
In this study, one genera-
tor produced individual binary bits, which were derived from the (3-decay of 90Sr, while the other
“binary” output was a majority vote from 100 bits, each of which were derived from a fast electronic
diode. Schmidt reports individually significant effects with both generators, yet does not observe a sig-
nificant difference between the generators.
This particular study
is interesting, quite aside from the timing and majority vote issues; the binary
streams were derived from fundamentally different physical sources.
Radioactive P-decay is governed
by the weak nuclear force, cind electronic devices (e.g., noise diodes) are governed by the electromag-
netic force. Schematically speaking, the electromagnetic force is approximately 1,000 times as strong as
the weak nuclear force, and modern high-energy physics has shown them to be fundamentally different
after about 10“ 10 seconds after the big bang (Raby, 1985).
Thus, a putative /lP-force would have to
interact equally with these two forces; and since there is no mechanism known that will cause the elec-
tromagnetic and weak forces to interact with each other, it is unlikely that AP will turn out to be the first
coupling mechanism.
The lack of difference between p-decay and noise diode generators was con-
firmed years later by May et al. (1980).
We have already commented upon one aspect of the timing issue with regard to Radin and May’s (1986)
experiment and the papers by Walker (1987) and Vassy (1990). May (1975) introduced a scheme to
remove any first-order biases in binary generators that also is relevant to the timing issue. The output of
his generator was a match or anti-match between the random bit stream and a target bit. One mode of
the operation of the device, which May describes, included an oscillating target bit—one oscillation per
bit at approximately 1 MHz rate.* May and Honorton (1975) and Honorton and May (1975) reported
significant effects with the RNG operating in this mode. Thus, significant effects can be seen even with
devices that operate in the microsecond time domain, which
is three orders of magnitude faster than
any known physiological process.
Effects with Pseudorandom Number Generators
Pseudorandom number generators are, by definition, those that depend upon an algorithm, which
is
usually implemented on a computer. Radin (1985) analyzed all the PRNGs commonly in use and found
that they require a starting value (i.e., a seed), which
is often derived from the computer’s system clock.
As we noted above, Radin and May (1986) showed that the bit stream, which proved to be “successful”
in a PRNG study, was bit-for-bit identical with the stream, which was generated later, but with the same
* Later, this technique was adopted by Jahn (1982) for use in the RNG devices at PEAR.
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seed.
With that generator, at least, there was no change from the expected bit stream.
Perhaps it is
possible that the seed generator (i.e., system clock) was subjected to some /l/
1 interaction. We propose
two arguments against this hypothesis:
(1) Even one cycle interruption of a computers’ .system clock will usually invoke a system crash; an
event not often reported in PRNG experiments.
(2) Computers use crystal oscillators as the basis for their internal clocks. Crystal manufacturers usual-
ly quote errors in the stated oscillation frequency of the order of 0.001 percent. That translates to
500 cycles for a 50 MHz crystal, or to 10 ps in time. Assuming that the quoted error is a l-o estimate,
and that a putative AP interaction acts at within the ± 2-o domain, then shifting the clock by this
amount might account for only one seed shift in Radin and May’s experiment.
By Monte Carlo
methods, we determined that, given a random entry into seed-space, the average number of ticks to
reach a “significant” seed
is 10; therefore, even MAP could shift the oscillators by 2-o,
it cannot
account for the observed data.
Since computers in PRNG experiments are not reported as “crashing” often,
it is safe to assume that
PRNG results are only due to AC, In addition, since the results of PRNG studies are statistically insepa-
rable from those reported with true RNGs, it is also reasonable to assume that the mechanisms are simi-
larly /lC-based.
Precognitive AC
Using the tools of modern meta-analysis, Honorton reviewed the precognition card-guessing database
(Honorton and Ferarri, 1989). This analysis included 309 separate studies reported by 62 investigators.
Nearly two million individual trials were contributed by more the 50,000 subjects. The combined effect
size was £ = 0,020±0,002) which corresponds to an overall combined effect of 11.4a.
T\vo important
results emerge from Honorton’s analysis.
First,
it is often stated by critics that the best results are from
studies with the least methodological controls. To check this hypothesis, Honorton devised an eight-
point quality measure
(e.g., automated recording of data, proper randomization techniques) and
scored each study with regard to these measures. There was no significant correlation between study
quality and study score. Second, if researchers improved their experiments over time, one would expect
a significant correlation of study quality with date of publication. Honorton found r = 0,246, df = 307, p
<C 2xl0~ 7
.
In brief, Honorton concludes that a statistical anomaly exists in this data that cannot be
explained by poor study quality or a large variety of other hypotheses; therefore, a potential mechanism
underlying DAT has been verified.
SRI Internationars RNG Experiment
May, Humphrey, and Hubbard (1980) conducted an extensive RNG study at SRI International in 1979.
They applied state-of-the-art engineering and methodology to construct two true RNGs, one based on
the (3-decay of 137Pm and the other based on an MD-20 noise diode from Texas Instruments.
It is be-
yond the scope of this paper to describe, in detail, the intricacies of this experiment; however, we will
discuss those aspects, which are pertinent to this discussion.
Technical Details
Each of the two sources were battery operated and optically coupled to a Digital Equipment Corpora-
tion LSI 11/23 computer.
Fail-safe circuitry would disable the sources if critical physical parameters
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(e.g., battery voltages and currents, temperature) exceed preset ranges. Both sources were subjected to
environmental testing which included extreme temperature cycles, vibration tests, E&M and nuclear
gamma and neutron radiation tests.
Both sources behaved as expected, and the critical parameters,
such as temperature, were monitored and their data stored along with the experimental data.
A source was sampled at 1 KHz rate. After eight milliseconds, the resulting byte was sent to the comput-
er while the next byte was being obtained. In this way, a continuous stream of 1 ms data was presented to
the computer. May et
al. had specified,
in advance, that bit number 4 was the designated target bit.
Thus each byte provided 3 ms of bits prior to the target and 4 ms of bits after the target bit.
A trial was defined as a definitive outcome from a sequential analysis of bit four from each byte.
In
exchange for not specifying the number of samples in advance, sequential analysis requires that the
Type I and Type II errors, and the chance and extra-chance hitting rate be specified in advance. In May
et al.’s two-tailed analysis, a =
(3 = 0.05 and the chance and extra-chance hitting rate was 0.50 and 0.52,
respectively. The expected number of samples to reach a definitive decision was approximately 3,000.
The outcome from a single trial could be in favor of a hitting rate of 0.52, 0.48, or at chance of 0.50, with
the usual risk of error in accordance with the specified Type
I and Type II errors.
Each of seven operators participated in 100 trials of this type. For an operator’s data to reach indepen-
dently statistical significance, the operator had to produce 16 successes in 100 trials, where a success was
defined as extra-chance hitting (i.e., the exact binomial probability of 16 successes for 100 trials with an
event probability of 0.10 is 0.04 where one less success is not significant). Two operators produced 16
and 17 successful trials, respectively.
Temporal Analysis
We analyzed the 33 trials from the two independently significant operators from May et al.’s experi-
ment. Each of the 33 trials consisted of approximately 3,000 bits of data with -3 bits and +4 bits of 1
ms/bit temporal history surrounding the target bit. We argue that
if the significance observed in the
target bits was because of/IP, we would expect a large correlation with the target bit’s immediate neigh-
bors, which are only ± 1 ms away. As far as we know, there is no known physiological process that can be
cognitively, or in any other way, manipulated within a millisecond. We might even expect a 100% cor-
relation under the complete AP model.
We computed the linear correlation coefficients between bits 3 and 4, 4 and 5, and 3 and 5. For binary
data:
N<j> 2 ~X2{df =
1),
where
<j6 is the linear correlation coefficient and N is the number of samples. Since we examined three
different correlations for 33 trials, we computed 99 different values of N(p^. Four of them produced X^s
that were significant—well within chance expectation. The complete distribution is shown in Figure 7.
We see that there is excellent agreement of the 99 correlations with the X2 distribution for one degree of
freedom, which is shown as a smooth curve.
We conclude, therefore, that there was no evidence beyond chance to suggest that the target bit neigh-
bors were affected even when the target bit analysis produced significant evidence for an anomaly. So,
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