ABSTRACT
from the
Journal of Parapsychology
APPLICATIONS OF DECISION AUGMENTATION THEORY
By Edwin C. May, S. James P. Spottiswoode, Jessica M. Utts, and Christine L. James
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 (z2, 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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