9. Addendum E

An alternative approach to interpreting the “Otherliness” at the foundation of the usefulness of Spinors. (for paper got to page 49) 

Foreword: 

Using Spinors for the reliable predictive computation of real-world health and wellbeing events. 

By Stefaan Vossen  

Apology : 

The problem with a paper like the one I’m writing (of which the paper on Spinors shared with you here is part) is that it essentially espouses a theory of everything. Some strange mythical and somewhat objectionable science-story that brings together all manner of scientific capability and incredulity, and of course I can see how that could happen.  

But this paper doesn’t try to be that. It tries to present evidential support for a use-case that requires a novel computational model using existing techniques and technologies.  

It presents computational modelling for a new way of looking at data, and a scientific model for a slightly altered perspective on the real world to achieve a very specific goal: making unwell people better. It achieves and demonstrates many other things in doing this, but this is its goal. 

This paper only emerged by default from a healthcare project I am planning and became a requirement for its successful delivery. 

These kinds of projects, however well-intended (but as you can imagine, also expensive), have investors that want a sensible level of agreement for their use cases. 

This paper contains the various ideas and innovations brought together in one format, which is what the evidence required is for such projects.  

One of these ideas is a revision of the “Spinor” as a computational, mathematical object. Those who know Spinors know it unites all the understandings across the various computational fields under evaluation in this computational model and understand all its implications. It is also the tool that is used to describe “local reality” in theoretical physics but is rarely known outside of these fields. 

If the people reviewing this paper agree, then these suggested changes to Spinors can be integrated into logic and computer sciences which is where it lands with coders etc. and becomes useful in real-world computation (function and algorithm building).  

Where it can start to make an immediate real-world impact on patients and future patients.  

The healthcare project is a software one that needs the way we understand the data behind the math represented in Spinors to be given a small tweak to its meaning and be given “flavour” basically.  

This paper is not trying to be a theory of everything, it is explaining the logic in support of the use-case, and I do it because making people better is a good way to spend time and money.  

This paper’s objective is to unite several computational perspectives from different fields (ranges of motion to ranges of emotion), each with different languages and boundaries created by lack of integration, posing communication challenges. However, data is data and anything that can be put into numbers can be computed. 

Saying that this specific use case is the only way to test a theory of everything, may not be true, but the principle of observing humans to do it, I believe, may stand.  

I think that to be able to predict the human real-world experience-future, we need to understand the human experience of the world first. 

As a society, we have become aware of the potential value of such a “theory of everything”, both in economic and cultural terms. Also, in their mythical status and ability to influence human behaviour as well as its association to complex geometries in mathematical relationships. This paper must discuss all that and I hope it does because it is the only way I know how to do my bit, but so far that label has only been a hindrance.  

Giving Spinors a flavour 

Introduction 

Spinors are useful mathematical objects currently in use for the computation of predictive or “Quantum” events such as theoretical physics, weather or financial forecasting. They are elements of vector space that carry the linear representation of the Clifford Algebra. 

They represent a real-world event reliably described as two-data-layers held in agreeable coalescence through a reliably predictive equation. 

The limitation imposed on their use in this field is the by-product of our understanding of the computational perspective taken through the position of Pauli’s exclusion principle. In this approach, we are adding a computational method of looking at “exclusion” and flavouring it with “by inversion”. 

The alteration suggested within this paper has computational implications on a possible healthcare project and on core scientific and philosophical perspectives. 

The suggestion stands as an additional flavouring to Spinors, not a correction of its core principles. 

Pauli’s principle is not immutable, but has proven very accurate on repeated evaluation, yet it is fair to say that its computational perspective on the source of the data used for calculation, singularly dictates the understanding and meaning we currently give to Spinors.  

This paper suggests that a meaningful additional data-layer becomes available when a secondary meaning (flavour) is attributed to Spinors, and the juxtaposition of these, now two, available layers, improves computational insight dramatically. Mathematically, it has no further significance, but conceptually its derivatives have implications in philosophy, science and healthcare. Whilst hardly a mathematical idea in its healthcare-expression, it requires agreement from this section of the sciences, to be validated logically and then implemented in computational terms for software applications. 

Application in healthcare  

With the rapid developments in data-evaluation capability of AI and Neural Network Learning, this paper positions a mathematically permissible alteration to the potential meanings of Spinors, for the specific purpose of advancing the theoretical implications of this shift in meaning on the concept of health-issue-prevention and healthcare-pathway design.  

This juxtaposition of complex ideas results in a computational concept where analytical systems currently in use in financial, weather and statistical forecasting, can be used to predict what would be the “best-next-action" for a patient to undertake to make them feel better.  

This is achieved by creating an enhanced data-avatar produced of, for and with permission of the clinical user involved. The system suggested results in a predictive computational capacity that can, mathematically and logically speaking, only be used for the improvement of their wellbeing. 

This application then can confidently be invested in and used for active, real-time clinical guidance and lifestyle-modification systems in conjunction with wearable apps and home-devices. The suggestion is to develop data-avatars based on movement and behavioural pattern-recognition in association with controlled environmental stimulus-management.  

Applications in Physics 

This application would constitute real-world predictive data entanglement and is considered significant in physics and science but is beyond this paper's scope. Whilst discussed in it, this paper chiefly aims to make the use-case for the application of creating digital avatars by the method suggested, to logically and reliably achieve the predictive entanglement event for the benefit of healthcare outcomes.  

This would logically emerge upon agreement with this theory in principle.  

Further applications of course exist by extension of this logic, but the theoretical agreement on the modification of the principled meaning of Spinors, naturally produces those applications for those working in the various relevant fields.  

This paper merely wishes to describe a method to make the unfolding of that theoretical agreement emerge in real-world computation, and provide mathematical method to achieve it through pure logic and computational modelling to the benefit of patients.  

Creating a flavour of Spinor in set theory (introduction) 

By acknowledging the agreed juxtaposition of angular position to angular momentum (as represented by the single 720° rotation cycle around the X-axis and seen as describing the “square rooting” of the SU3/SO2 data layers of “branes”), we can add a “flavour” to the existing construct of Spinors, with the addition of a concurrent, and simultaneous rotation in the Y axis.  

This concurrent rotation then provides space for a change in meaning to the origin of Pauli’s “Otherliness /Strangeness” with the implementation of a novel to angular momentum: angular “inversion” as suggested in the paper.  

Essentially “turning inside-out" the meaning of the data by using mathematical vortices to describe “something that is” (observed) as “being in relation to a computational set” that describes “everything that could be” (observed) within the parameters of the observational tool.  

In the computational perspective of healthcare “that which is observed”, essentially includes symptoms and personal experiences of human individuals and offers mathematical access to the datapoints that describe the real-world lived experiences of billions of human beings with similarities and differences. This in turn enables predictive computing. 

Reasoning and motivation 

Enabling this logical computational adaptation by agreement through peer-review, even if only in theory, would provide high confidence in the logical conclusions that can be drawn from it in both predictive healthcare and general human wellbeing. 

In this interpretation for Spinors, we suggest that of computing the set of “all possible things other than that what the described point-particle currently is”.  

What this logically provides is computational perspective that can truly be said to be predictive for objects representing the subjective human lived experience. This must be understood and agreed with confidence by mathematicians before being communicated further for the idea to have any linguistic meaning. If it can be communicated with confidence by this test, it offers significant logical support to the implementation of its systematic consequences across computer science, data-management and computational data-warehousing.   

Thank you for your consideration.