Mark Boccuzzi is a co-founder and researcher at the Windbridge Institute, LLC, and the executive director of the Windbridge Research Center, an Arizona nonprofit dedicated to performing rigorous scientific research and creating educational materials focused on dying, death, and what comes next. He is also the managing editor of the free, open-access, peer-reviewed journal Threshold: Journal of Interdisciplinary Consciousness Studies. His main research interests include applied psi, machine learning, virtual/augmented reality, transformative technologies, mindfulness, and instrumental transcommunication (ITC). A free copy of his book, Visualizing Intention: Art Informed by Science can be downloaded from www.windbridgeInstitute.com/vibook
IRVA 2018 – When Pundits Fail, Psi Prevails: Using a Symbolic Hieronymus Machine to Accurately Predict Voting Outcomes in Battleground States During the 2016 U.S. Presidential Election
In 1949, Radionics pioneer, Thomas Hieronymus, was awarded a U.S. Patent for the detection of eloptic energy (EE) from “materials and volumes thereof.” This technology became the basis for his “Hieronymus Machine.” However, the device follows no currently accepted scientific or engineering paradigm. Starting in the 1950s, writer John Campbell claimed that the device functioned using the operator’s psi and could still produce an output even if the electronics were replaced by a schematic of the internal circuit. This non-electronic device is known as a Symbolic Hieronymus Machine (SHM). In the current exploratory study, a SHM was used to predict voting results in six “Battle Ground” states (states whose voting outcomes could not be predicted using traditional means) during the 2016 U.S. Presidential Election. A randomized, blinded protocol was used to establish two baseline EE Rates: one for historically “Blue” and another for historically “Red” voting states. Next, using the same protocol, EE Rates were established for each of the six target states while the operator asked to be shown the state’s future election outcome. The resulting EE Rates were mapped to the Blue and Red baseline rates to create outcome predictions for each target. This method accurately predicted the outcomes in five of the six states. In comparison, only two states were accurately predicted using the same protocol with a truly random source substituted for the SHM. While still exploratory, this project demonstrates a potential methodology to evaluate event prediction using the SHM and similar devices.