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Decision Augmentation Theory: Toward a Model of Anomalous Mental Phenomena

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May, Edwin C, Utts, Jessica M, Spottiswoode, S. James P 1995 STAR GATE Era methodology

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Plain English Summary

Here is a genuinely radical idea: what if psychokinesis — the supposed ability to move or influence objects with your mind — does not actually exist? Decision Augmentation Theory proposes that when people seem to mentally nudge random number generators (RNGs), what is really happening is precognition (glimpsing the future) steering their choices toward moments when the machine would have produced favorable results anyway. No mental force needed — just remarkably well-timed decisions. The exciting part is this is not just philosophy; the authors derived a concrete, testable prediction. Under their theory, the statistical evidence should stay constant regardless of how many random bits per trial you use, while a genuine force model predicts it should grow. They calculated you would need roughly 1,368 experimental runs to tell the two apart. If correct, every anomalous mental phenomenon collapses into one elegant mechanism: information flowing backward in time.

Actual Paper Abstract

Decision augmentation theory (DAT) holds that humans integrate information obtained by anomalous cognition into the usual decision process. The result is that, to a statistical degree, such decisions are biased toward volitional outcomes. We introduce our model and show that the domain over which it is applicable is within a few standard deviations from chance. We contrast the theory's experimental consequences with those of models that treat anomalous effects as due to a force. We derive mathematical expressions for DAT and force-like models using two distributions, normal and binomial. DAT is testable both retrospectively and prospectively, and we provide statistical power curves to assist in the experimental design of such tests. We show that the experimental consequences of our theory are different from those of force-like models except for one special case.

Research Notes

Landmark theoretical paper from the Cognitive Sciences Laboratory (SRI/SAIC program). If correct, DAT eliminates the need for any force model of PK, collapsing all micro-PK into precognition. Directly critiqued by Hyman (1996) and foundational to interpreting the PEAR RNG database and GCP results.

Introduces Decision Augmentation Theory (DAT), proposing that statistical anomalies in micro-psychokinesis experiments arise not from a mental force perturbing physical systems but from anomalous cognition (precognition) biasing human decisions toward favorable outcomes within an unperturbed world. Mathematical expressions are derived for normal and binomial distributions, yielding a key testable prediction: under DAT, the expected z² is independent of n (items per trial), whereas force-like models predict z² increases linearly with n. Statistical power curves show that ~1,368 runs at n=10⁴ suffice to separate models at 95% confidence for typical RNG effect sizes. The theory implies all anomalous mental phenomena may reduce to a single mechanism—information transfer from future to past.

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📋 Cite this paper
APA
May, Edwin C, Utts, Jessica M, Spottiswoode, S. James P (1995). Decision Augmentation Theory: Toward a Model of Anomalous Mental Phenomena. The Journal of Parapsychology.
BibTeX
@article{may_1995_decision,
  title = {Decision Augmentation Theory: Toward a Model of Anomalous Mental Phenomena},
  author = {May, Edwin C and Utts, Jessica M and Spottiswoode, S. James P},
  year = {1995},
  journal = {The Journal of Parapsychology},
}