Using natural language processing, a system of using artificial intelligence to understand text, the author constructs a predictor based on a simple, constrained, description of the solar system on the start dates of various world historical events (n=7819). This predictor is then statistically shown to be effective at characterizing similar events of the past. An online offering of this predictor for use of characterizing the future accompanies this paper.
Understandably, the future is a subject of shared fascination.
Future history is a term of ripe heritage. Typically, it is used to describe works of science fiction. What if future historical events could be predicted computationally, even if hazily?
This paper shows that the simple solar system astronomy of historical event start dates can map onto similar major world events of the past as numerically measured by textual analysis of one-sentence descriptors. By looking at the astronomy that accompanies future dates, those textual characterizations can easily be employed to see astronomy mappings onto historical events.
Methods and Materials
The project makes heavy use of the Mathematica system of knowledge representation, computation, and analysis.
Characterization of Historic Events Dataset
The first step is to seek the historical events. What counts as a true historical event? What counts as the start date? How does one even characterize a world event given differing cultural perceptions? For ancient events, how confident can one be for either the start date or the description?
These are all excellent questions that the author largely side stepped by employing Mathematica’s “Historical Events” feature. There were 7819 such events available thereby with start dates and one-line descriptions included. The sources from Mathematica for this historical event dataset are numerous. They can be found through this mechanism.
The following is a set of descriptors for 400 or so example historic events out of the 7819.