**ABSTRACT
**from the

**A****PPLICATIONS
OF ****D****ECISION
****A****UGMENTATION
****T****HEORY**

By Edwin C. May, S. James P. Spottiswoode, Jessica M. Utts, and Christine L. James

330 Cowper Street, Suite 200

Palo Alto CA 94301 (May, Spottiswoode, and James)

and: Division of Statistics

University of California, Davis

Davis CA 956168705 (Utts).

Decision augmentation theory (DAT) provides an
informational mechanism for a class of anomalous mental phenomena
that have hitherto been viewed as being caused by a forcelike
mechanism. Under specifiable conditions, DAT's predictions for
statistical anomalous perturbation databases are different from
those of all forcelike mechanisms. For large random number
generator databases, DAT predicts a zero slope for a least
squares fit to the (*z*^{2}, n) scatter diagram,
where n is the number of bits resulting from a single run and z
is the resulting *z*-score. We find a slope of
(1.73plus/minus3.19) 10^{-6} (*t* = 0.543, *df*
= 126, *p* = .295) for the historical binary RNG database,
which strongly suggests that some informational mechanism is
responsible for the anomaly. In a two sequence length analysis of
a limited set of RNG data from the Princeton Engineering
Anomalies Research laboratory, we find that a forcelike
explanation misses the observed data by 8.6 sigma; however, the
observed data are within 1.1 sigma of the DAT prediction. We also
apply DAT to one pseudoRNG study and find that its predicted
slope is not significantly different from the expected value for
an informational mechanism. We review and comment on six
published articles that discussed DAT's earlier formalism (i.e.,
intuitive data sorting). We found two studies that support a
forcelike mechanism. Our analysis of Braud's 1990 hemolysis
study confirms his finding in favor of an influence model over a
selection one (*p* = .023), and Braud and Schlitz (1989)
demonstrated a forcelike interaction in their remote staring
experiment (*p* = .020). We provide six circumstantial
arguments against an influence hypothesis. Our anomalous
cognition research suggests that the quality of the data is
proportional to the total change of Shannon entropy. We
demonstrate that the change of Shannon entropy of a binary
sequence from chance is independent of sequence length; thus, we
suggest that a fundamental argument supports DAT over influence
models. In our conclusion, we suggest that, except for one
special case, the physical RNG database cannot be explained by
any influence model, and that contradicting evidence from two
experiments on biological systems should inspire more
investigations in a way that would allow valid DAT analyses.

© Parapsychology Press

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