Validating the GCP Data Hypothesis and Harvesting Its Data

ABSTRACT

Introduction: I test for the validity of the hypothesis underlying the Global Consciousness Projects (GCP) i.e., the hypothesis that events which elicit widespread emotion or draw the simultaneous attention of large numbers of people, may affect the output of hardware generated random numbers. The hypothesis is tested for by calculating daily aggregates out of the second-by-second data generated by the GCP and then correlating them with Google Trends search data. More specifically, changes in global attention are proxied with variations in the popularity of global internet searches which is used to construct a monthly search word index. Since changes in the index thus represents changes in global attention in a particular topic, the validity of the GCP data hypothesis can be tested for by correlating it with changes in data aggregates derived out of the GCP data. When doing so, I find that all tested GCP data aggregates significantly covary with the search index and that the most significant correlation is found on its monthly average (P<0.000). Methods: Since the validity of the hypothesis seems to hold true, I proceed with constructing a global attention and engagement index. This by applying the one-sided Hodrick-Prescott filter on both the daily and monthly averages out of the GCP data. The filtered series are then normalized and by defining a significant date as a date on which the attention index surpasses the 95th percentile of a standard normal distribution. When doing so, several significant world events are identified and among them, the onset of the Covid-19 pandemic can be mentioned. As such it can be suspected that the index can "pick up" globally engaging world events, results that possibly could be used by policy makers.