8. Addenda A-D
Addenda
A. The question of whether this system’s demonstration of treatment efficiency offers proof of effectiveness of individual therapies is irrelevant to this paper but merits discussion. Our position is that efficacy as defined by the clinician’s standard is irrelevant to this system. It is not irrelevant, but it is irrelevant to this system. This system assists decision making, it does not replace it. Neither does it evaluate risk. It just looks at the data as it is. It evaluates the outcomes associated with currently approved and ongoing treatments and therefore logically optimises their usage. The feedback point is the patient’s perspective only, whether objectively justifiable (quantifiable) or not (qualia). The introduction of new treatments based on sound clinical reasoning can then introduce new variables for evaluation, thereby clearly demonstrating their relative value. This is, ultimately, the only truth that can be said about any modalities’ efficacy. What this system WILL be able to do is evaluate the efficacy of something compared to another modality. Far more effectively so than when compared to classical evaluatory scientific RCT method which requires the analysis that would automatically emerge from the computation as proposed here, to be borne from ever-more expansive research questions. In one sense one could say that this aspect of science has a limited application field after all, and in another that its limited applicability is very much the defining nature of its existence and revelatory nature.
B. The asymmetrical nature of the super-asymmetry in question resides in the Qualitative (Qualia) offset between the objects used within the superimpositions and the meanings of their Quantae. Qualia to Quantae. In essence errors of attribution of value as if being “of the same group” or mathematical set. In plain language it is simply the error of saying that 2 green round apples and two green round pears make 4 apples, although they will no doubt make a mean green round fruit-pie. This goes back all the way into Godel’s theorem and various paradoxes which fall away when understanding and using this computational perspective. When reassessed, all paradoxes are eventually seen to state that two apples and two (once indistinguishable, but) “almost-apple-pears" make a four-apple pie. What we are saying in this paper, is that the suggested computational method allows us to evaluate the meta-meta-data, or at least include it within the analytical data-mesh, and thereby enable the inclusion of a dataset that offers superior predictive computational perspective on the service user’s dataset. Alongside that comes vastly improved computational capability. What we are also saying is that rather unsurprisingly, healthcare just happens to be the logical place for it to emerge from. It also happens to be an area in dire need of it with great investment potential. The strange notion of super-asymmetry emerges naturally from the superimposition of the Quantae of “human motion” onto that of “wellbeing/human e-motion”, a well measured and recorded concept.
C. Platonic shapes are an interesting subject, and history aside, the idea of “Platonics” in brief is the adoption of one of its very special features. Most people will be familiar with the irreducibility of the platonic solids and how they represent the fundamental elements in symbolic representation. The five shapes with their polyhydronic congruence and regularity are in essence non-naturally occurring symbolic of arbitrary limitation. Establishing them computationally as 5 possible subcategories (by frequency as distributed across the range of the expression of that trait, divided into 5 equal categories) of any further irreducible trait (no further meta-data available) gives computational freedom. The distinguishing factor of a Platonic shape is the number of sides. The point is that they are a powerful symbol with a comprehensible number (5, good for data-transferability), of a real-world construct with one common ancestor (polyhydric congruence), yet fundamentally different in real-world terms. This renders them magnificently ethereal mathematical objects but also offers a potent computational option when used as arbitrary limitation. In reality (and as demonstrated in the below diagram), the truthfulness of something always lies in the perspective taken on its analysis. What the below diagram demonstrates is that from one, more distant and fuzzier viewpoint, all 4 sides would appear purple and therefore same. An unquantised value (Qualia). Yet, when zooming in, the values are available for 3 out of 4 of the branches offering geometric superimposition and computational options on knowledge on the Qualia.
Where:
A = Surface Behaviour Mesh
B= Clinical Data File
C= Surface Mesh changes over time
D= Clinical and personal file changes over time
The various quantised blocks are amalgamations of the two sources (subjective and objective/ blue and red). The usage of the outer blue ring for the left row describes its primarily objective nature with an acknowledgement of its subjective expressiveness. The outer ring as red in the right column of traits describes how those traits are expressive of subjective states (even if observer-interpreted) of health or lack of health-states. These are ultimately individual and therefore subjective. The quantised portions are records in the form of clinical records (top row), visual records (left row) and objective and subjective symptom improvement/changes over time (bottom row). The unquantised portion (Qualia) is the way people “feel”, as they “exist” offsite. Their Quality of Life.
States of Motion States of Being
Surface Mesh ------------------------> Clinical Data File
Surface Mesh Changes---------------------------> Clinical and personal file changes
Correlations in:
States of clinical and personal datafiles -----> States of wellbeing + Probabilities associated to those states
This is a logically constructive super-asymmetry naturally emerging between “states of movement” and “states of being”. Symmetry in Quantum but only for lack of the ability to further subdivide the meaning-variance (subtle nuance) of the sets of data being compared. Asymmetrical in every other sense in that the relationships are angular and distributed over time, subject to reality in every sense of the words. Why asymmetrical? Because the calculational perspective is taken from that of a unique individual, and there will always be a reason for that individual to not take the exact same perspective as you. Similar, probably. Identical, no, even if stated as such. The asymmetry resides in the “one unique perspective” vs “all other possible ones” within an infinite set.
In essence, this is saying that it is intrinsic to the human experience, language and notation that we note things as if they are the same when they are not actually. Our inability to detect the exact location of the error in meaning-giving, that causes us to calculate the data as if they were equal. By doing this, we create friction between the datasets as their values are being processed calculationally. From this friction emerges an error. The Observed Error. This again can be reframed as the error “created” by the observer (hence why it is the “observed” error, not observer error) when attributing equal meaning to the object under evaluation. Human observed error associated to the physical limitation of being human, measurement observed error associated to the limitations of the equipment humans could make to make the measurement. As this discussion refines further into the human usage of equipment to measure and the variance of error with technique, skill and physical aptitude we rapidly descend into the socio-cultural narrative of the human experience which is beyond the scope of this paper but within the scope of the concept.
Computationally, we position the Observed Error as the calculational object of wave collapse, and not the measurement.
Example regarding calculus:
Position equality of grouping: A = B thereby positioning equality in the quantum/ number /amount of the two sets i.e.: of the “thing” they are (letters representing a number of a certain thing defined by our strongest association/the thing we are in every way the same in). However, the truth is that whatever is in A, however much like B it may be, is not and will never actually be the exact same “thing(s)” contained within the set described as B, because two same things cannot be in the same place at the same time. So, whilst 2x2 is always equal to 4, it does not mean that doubling your order of apples will always get you double the profit on apple sales or even whether you’ll receive exactly double the amount in weight because no two apples are exactly the same, and no measurement is absolute.
This is what happens in paradoxes where the meaning of the equal sign cannot but ultimately be logically perverted. However, by using our novel “one-step-out" computational narrative, we have enabled a self-emergent, life-mimicking super-asymmetry. For work on Godel’s theorem etc. please refer to other papers by the authors. However, for the purpose of this paper; the “step-out”-computational perspective shift, one can also understand this as answering the question “which is the redder red?” The nexus betwixt qualia and quanta is a broad discussion but computationally it is a problem simply overcome by the ability to randomly allocate into arbitrary subgroups (like numbers or platonic shapes) within the same category of qualia (traits/redness) (like exact physical location relative to the computational framework) to create a frequency of occurrence (in that location). This enables it to be allocated as a data point to a location in time and space that is now irrevocably unique and unfalsifiable.
D. The retailing of archetypes. In one sense, the entirety of human history can be described as the “history of the retail of human archetypes”. Art, science, religion etc. In short, it should come as no surprise that a significant area of business growth in the future will be a simple continuation of the very same trend. With retail- and scientific data as well as (and most significantly) “experiential” data readily available and already analysed as business trends and predictions of human behaviour. These enormous piles of readily available data have already successfully been reduced to scientific papers and policy-performance reports and as such are archetype avatars. Each avatar, a snapshot. Not in time, but as a part of their archetypical meaning. Not a trend, but a quantised component that is associated with a trend. This is where the current rise and adoption of AI in trend-analysis is the obvious precursor and call-to-action for this novel approach to data-analysis. From this, analytical archetypes in the form of trained LLMs are being formed, which we then call “AI”. The exchange of archetypical data for consumer-experience optimisation, is a simple next step. Alongside this comes the creative perspective, already an immense subject source of debate in the field of AI generated art and reality. Art can be understood as the educated modification of observed archetypes as expressed by humans. The experienced and perceived “success” with which art does that, are only man-made constructs, of their own time and by the tools and resources available. The central point to its existence is that art somehow made something better for someone. To the degree they were willing to trade resources for it. This is what the retail of archetypes always has done, to someone, somehow. As incomprehensible to us individually as it may seem. By creating simulations, projections and alternative suggestions of those archetypes, we offer other humans an experience of “us”, our individuality or our skill as a human. It comes as no surprise then that human creativity has always been the primary commodity of value driving human existence as a species. The ability to project into the future and imagine an alternative outcome has always attracted great reward, be it socially or financially, be it within lived life or not. These artistic ideas and expressions are simulations of interactions of multiple archetypes. This is where the importance of education cannot be overstated in the development of good quality knowledge-maps for all human children. This is, and always has been, the key to successful stimulation of future creativity. Children are, so to speak, the LLM’s of the future, in a society where the valued commodity is likely to be human creativity. In this computational model (elsewhere termed the “dot theory”, unpublished) companies, artists and individual humans alike would be invited to commercialise their ability to take a real-world experience (“The thing they’re doing”) and retailing it into value, by either a) observing a pattern that made that thing better, and offer it as information for others or b) detect an archetype through analysis, run simulations on future need and make a suggestion of potentially beneficial effect on individuals. In both cases, the commercial success would be seen as by level of agreement that the creativity of choice in the moment or the predictive simulation met the value put on it. As such, the retail of archetypes is merely an extension of what humanity has always done. Only expressed in less-cultural terms. This, in brief, is very much the nature of the ethereal relationships between art and finance, creativity and industry, placebo and physical reality. Interestingly because archetypes have always been cherished by the human species, it must be considered that the ability to observe and express archetypes was possibly an undervalued commodity all along. The ever-increasing valuation of art in the global market would attest as evidence of this trend. Inextricably linked to art are human welfare. Art proliferation has seemed to increase in periods where human welfare is perceived as high and seemingly most intensely produced/generated at times where it was perceived as low. Again, this circles back to cycles between education and healthcare, both pivotal to human creativity and value-of-life.
