Academic journal article The Journal of Parapsychology

Applications of Decision Augmentation Theory

Academic journal article The Journal of Parapsychology

Applications of Decision Augmentation Theory

Article excerpt

INTRODUCTION

May, Utts, and Spottiswoode (1995) proposed decision augmentation theory (DAT) as a general model of anomalous mental phenomena.(1) DAT holds that anomalous cognition information is included along with the usual inputs that result in a final human decision that favors a "desired" outcome. In statistical parlance, DAT says that a slight, systematic bias is introduced into the decision process by anomalous cognition.

This concept has the advantage of being quite general. We know of no experiment that is devoid of at least one human decision; thus, DAT might be the underlying basis for anomalous mental phenomena. May et al. (1995) mathematically developed this concept and constructed a retrospective test algorithm that can be applied to existing databases. In this paper, we summarize the theoretical predictions of DAT, review the criteria for valid retrospective tests, and analyze the historical random number generator (RNG) database. In addition, we summarize the findings from one prospective test of DAT and comment on the published criticisms of an earlier formulation, which was then called intuitive data sorting (IDS). We conclude with a discussion of the RNG results that provide a strong circumstantial argument against a force-like explanation. As part of this review, we show that one biological-AP experiment is better described by an influence model.

REVIEW OF DECISION AUGMENTATION THEORY

Since the formal discussion of DAT is statistical, we will describe the overall context for the development of the model from that perspective. Consider a random variable, X, that can take on continuous values (e.g., the normal distribution) or discrete values (e.g., the binomial distribution). Examples of X might be the hit rate in an RNG experiment, the swimming velocity of single cells, or the mutation rate of bacteria. Let Y be the average of X computed over n values, where n is the number of items that are collected as the result of a single decision - one trial. Often this may be equivalent to a single effort period, but it also may include repeated efforts. The key point is that, regardless of the effort style, the average value of the dependent variable is computed over the n values resulting from one decision point. In the examples above, n is the sequence length of a single run in an RNG experiment, the number of swimming cells measured during the trial, or the number of bacteria-containing test tubes present during the trial. As we will show, force-like effects require that the z score, which is computed from the Ys, increase as the square root of n. In contrast, informational effects will be shown to be independent of n.

Under DAT, we assume that the underlying parent distribution of a physical system remains unperturbed; however, the measurements of the physical system are systematically biased by an AC-mediated informational process. Since the deviations seen in actual experiments tend to be small in magnitude, it is safe to assume that the measurement biases are small and that the sampling distribution will remain normal. Therefore, we assume the bias appears as small shifts of the mean and variance of the sampling distribution as:

[Mathematical Expression Omitted],

where [[Mu].sub.z] and [[Sigma].sub.z] are the mean and standard deviation of the sampling distribution. Under the null hypothesis, [[Mu].sub.z] = 0.0 and [[Sigma].sub.z] = 1.0.

REVIEW OF AN INFLUENCE MODEL

For comparison's sake, we summarize a class of influence models. We begin with the assumption that a putative anomalous force would give rise to a perturbational interaction, by which we mean that given an ensemble of entities (e.g., random binary bits), an anomalous force would act equally on each member of the ensemble, on the average. We call this type of interaction micro-AP.

In the simplest micro-AP model, the perturbation induces a change in the mean of the parent distribution but does not affect its variance. …

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