On the Possible Experimental Detection of Non-Causal Synordination Patterns of Physical Events

TL;DR

This paper explores the potential for experimentally detecting non-causal patterns linking two series of physical events through statistical analysis, focusing on microevents and their macroscopic amplification.

Contribution

It introduces a method to identify non-causal synordination patterns between microevents and macroevents using statistical deviations from expected probability distributions.

Findings

Statistical deviations indicate possible non-causal synordination.

Reproducible patterns can be distinguished from random correlations.

Method provides a way to detect non-causal links in physical events.

Abstract

With reference to a previous work, the problem of the experimental detection of non-causal synordination patterns between two series of physical events is examined. It is necessary that the patterns in question act in a reproducible, or at least regular, manner, so that they can be identified by means of statistical analysis; action on individual occasional facts cannot be proved statistically, though it is hypothetically possible. As established in the previous work, the first series must be constituted by microevents (Penrose R processes), that can be amplified at the macroscopic level so that they are translated at this level into a choice between alternatives. The obtained result defines (by means of a structural constraint which is not, however, a causal link) the choice between execution or non-execution of certain actions on a macroscopic system S, without any feedback on the microevents source. A succession of macroevents is thus generated, which makes up the second series. Statistics of the events belonging to the second series is thus completely defined by the probability distribution of the first series of events. In the absence of a synordination, this is the a priori probability distribution, which can be measured by removing the constraint between the two series. If there is synordination, the probability distribution of the two sets of events will deviate in a measurable manner from the expected trend once the constraint is activated.