Is an Algorithmic Language for constructing Complex System
Results into a Inferential Statistical mechanism suitable for a highly complex system – “Hyperbolic Dirac Net”
Involves an approach that is based on the premise that a Highly Complex System driven by the human social structures continuously strives to achieve a higher order in the entropic journey by continuos discerning the knowledge hidden in the system that is in continuum.
A System in Continuum seeking Higher and Higher Order is a Generative System.
A Generative System; Brings System itself as a Method to achieve Transformation. Similar is the case for National Learning Health System.
A Generative System; as such is based on Distributed Autonomous Agents / Organization; achieving Syndication driven by Self Regulation or Swarming behavior.
It has capabilities to facilitate medical workflow, continuity of care, medical knowledge extraction and representation from vast large sets of structured and unstructured data, automating bio-statistical reasoning leading into large data driven evidence based medicine, that further leads into clinical decision support system including knowledge management and Artificial Intelligence; and public health and epidemiological analysis.
A Large Chaotic System driven by Human Social Structures has two contending ways.
a. Natural Selection – Adaptive – Darwinian – Natural Selection – Survival Of Fittest – Dominance
b. Self Regulation – Generative – Innovation – Diversity – Cambrian Explosion – Unique Peculiarities – Co Existence – Emergent
Accountable Care Organization (ACO) driven by Affordability Care Act transforms the present Healthcare System that is adaptive (competitive) into generative (collaborative / co-ordinated) to achieve inclusive success and partake in the savings achieved. This is a generative systemic response contrasting the functional and competitive response of an adaptive system.
Natural selection seems to have resulted in functional transformation, where adaptive is the mode; does not account for diversity.
Self Regulation – seems like is a systemic outcome due to integrative influence (ecosystem), responding to the system constraints. Accounts for rich diversity.
From the above observation, should the theory in self regulation seem more correct and that adheres to laws of nature, in which generative learning occurs. Then, the assertion is “method” is offered by the system itself. System’s ontology has an implicate knowledge of the processes required for transformation (David Bohm – Implicate Order)
For very large complex system,
System itself is the method – impetus is the “constraint”.
In the video below, the ability for the cells to creatively create the script is discussed which makes the case for self regulated and generative complex system in addition to complex adaptive system.
Further Notes on Q-UEL / HDN :-
That brings Quantum Mechanics (QM) machinery to Medical Science.
Is derived from Dirac Notation that helped in defining the framework for describing the QM. The resulting framework or language is Q-UEL and it delivers a mechanism for inferential statistics – “Hyperbolic Dirac Net”
Created from System Dynamics and Systems Thinking Perspective.
It is Systemic in approach; where System is itself the Method.
Engages probabilistic ontology and semantics.
Creates a mathematical framework to advance Inferential Statistics to study highly chaotic complex system.
Is an algorithmic approach that creates Semantic Architecture of the problem or phenomena under study.
The algorithmic approach is a blend of linguistics semantics, artificial intelligence and systems theory.
The algorithm creates the Semantic Architecture defined by Probabilistic Ontology :- representing the Ecosystem Knowledge distribution based on Graph Theory
To make a decision in any domain, first of all the knowledge compendium of the domain or the system knowledge is imperative.
System Riddled with Complexity is generally a Multivariate System, as such creating much uncertainty
A highly complex system being non-deterministic, requires probabilistic approaches to discern, study and model the system.
General Characteristics of Complex System Methods
Descriptive statistics are employed to study “WHAT” aspects of the System
Inferential Statistics are applied to study “HOW”, “WHEN”, “WHY” and “WHERE” probing both spatial and temporal aspects.
In a highly complex system; the causality becomes indeterminable; meaning the correlation or relationships between the independent and dependent variables are not obviously established. Also, they seem to interchange the position. This creates dilemma between :- subject vs object, causes vs outcomes.
Approaching a highly complex system, since the priori and posterior are not definable; inferential techniques where hypothesis are fixed before the beginning the study of the system become enviable technique.
Review of Inferential Techniques as the Complexity is Scaled
Step 1:- Simple System (turbulence level:-1)
Frequentist :- simplest classical or traditional statistics; employed treating data random with a steady state hypothesis – system is considered not uncertain (simple system). In Frequentist notions of statistics, probability is dealt as classical measures based only on the idea of counting and proportion. This technique is applied to probability to data, where the data sets are rather small.
Increase complexity: Larger data sets, multivariate, hypothesis model is not established, large variety of variables; each can combine (conditional and joint) in many different ways to produce the effect.
Step 2:- Complex System (turbulence level:-2)
Bayesian :- hypothesis is considered probabilistic, while data is held at steady state. In Bayesian notions of statistics, probability is of the hypothesis for a given sets of data that is fixed. That is, hypothesis is random and data is fixed. The knowledge extracted contains the more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems.
Additionally the hypothesis can be explored only in an acyclic fashion creating Directed Acyclic Graphs (DAG)
Increase the throttle on the complexity: Very large data sets, both structured and unstructured, Hypothesis random, multiple Hypothesis possible, Anomalies can exist, There are hidden conditions, need arises to discover the “probabilistic ontology” as they represent the system and the behavior within.
Step 3: Highly Chaotic Complex System (turbulence level:-3)
Certainly DAG is now inadequate, since we need to check probabilities as correlations and also causations of the variables, and if they conform to a hypothesis producing pattern, meaning some ontology is discovered which describes the peculiar intrinsic behavior among a specific combinations of the variables to represent a hypothesis condition. And, there are many such possibilities within the system, hence very chaotic and complex system.
Now the System itself seems probabilistic; regardless of the hypothesis and the data. This demands Multi-Lateral Cognitive approach
Telandic…. “Point – equilibrium – steady state – periodic (oscillatory) – quasiperiodic – Chaotic – and telandic (goal seeking behavior) are examples of behavior here placed in order of increasing complexity”
A Highly Complex System, demands a Dragon Slayer – Hyperbolic Dirac Net (HDN) driven Statistics (BI-directionalBayesian) for extracting the Knowledge from a Chaotic Uncertain System.
“””””Alexander has been inexorably led to the revolutionary necessity of revising our basic picture of the universe to include a conception of the personal nature of order and our belonging to the world in which the wholeness of space and the extent to which it is alive is perceived as rooted in the plenum behind the visible universe, “the luminous ground” that holds us all. This form of extended objective truth will ultimately resolve our Cartesian dilemma by teaching us a new view of order and a new cosmology in which objective reality “out there” and a personal reality “in here” are thoroughly connected and the bifurcation of nature healed.””””””
“”To Rene Descartes the “Method” (1638) was a convenient mental trick but its success has left us with a mindset that conceives of the universe as a machine without any intrinsic value: the realms of human experience and of feeling are simply absent from the Cartesian world. Whilst inspiring generations of architects and many others from all walks of life concerned with the fate of the earth, Alexander’s ultimately life changing work has understandably provoked powerful opposition from those invested within the establishment of the old paradigm. Social disorder, mental illness, ecological degradation, these and many other problems are due to a misunderstanding of the structure of matter and the nature of the universe and, until quite recently, there has been no coherent way of explaining the order that we respond to and love in nature.””
Affordability Care Act and HITECH Act lead into EHR Incentive Program. Based on the EHR Incentive Program CMS has already payed out 24+ Billions of dollars to Eligible Participants. Has it or will it drive the envisioned Healthcare Interoperability still remains a big question. Specifically will it be possible to mine the millions of records and discover opportunity for improvement? Without emphasis on clinical decision support will it be possible to achieve efficacy in the healthcare delivery, while also advancing the opportunities for “pay for performance” outcomes?
To advance EHR adoption in the Healthcare Ecosystem CMS proposed formation of Accountable Care Organization
“”The Pioneer ACO Model is designed for health care organizations and providers that are already experienced in coordinating care for patients across care settings. It will allow these provider groups to move more rapidly from a shared savings payment model to a population-based payment model on a track consistent with, but separate from, the Medicare Shared Services Program. And it is designed to work in coordination with private payers by aligning provider incentives, which will improve quality and health outcomes for patients across the ACO, and achieve cost savings for Medicare, employers and patients.””
Importantly CMS proposed roadmap for EHR Adoption based on Meaningful Use (MU) 3 Stages, in the hope of advancing interoperability in the healthcare ecosystem ultimately achieving performance driven model, where the payment models shifts from “pay for service” towards “pay for performance”. Looking at the Healthcare ecosystem, one must take note that achieving efficiency is in the healthcare management; while achieving efficacy is in the healthcare delivery.
You will see in the end of the discussion that somehow efforts of the EHR Incentive Program lays more emphasis on the helathcare efficiency without paying required attention to clinical efficacy. This leads to the systemic entropic discontinuity that can be described by the Boltzmann constant.
This results into missed Line of Sight, where the established “objective”s at the IT / EHR level do not deliver all the required the “business capabilities” or the output and hence the desired “transformative outcomes” are not realized.
Following are the EHR Adoption Meaningful Use Stages:-
MU Stage 1 :- Achieves electronic capture of the patient data (Data Capture and Sharing)
MU Stage 2 :- Achieves Health Information Exchanges (Advances co-ordinated clinical processes)
MU Stage 3:- Target Improved Outcomes ( achieved by moving the payment model from pay for service to pay for performance)
The eligible participants, physicians, hospitals and the ACOs have to demonstrate that they have met the MU criteria in stages. To demonstrate that they have met the requirements, first of all it is required to demonstrate that the data being captured adhere to a prescribed format. This is ascertained by MU attestation.
Additionally, the eligible participants are required to submit quality measures reports to CMS
Quality data reporting and collection support quality measurement, an important part of the Shared Savings Program. Before an ACO can share in any savings generated, it must demonstrate that it met the quality performance standard for that year. There are also interactions between ACO quality reporting and other CMS initiatives, particularly the Physician Quality Reporting System (PQRS) and meaningful use. The sections below provide resources related to the program’s 33 quality measures, which span four quality domains: Patient / Caregiver Experience, Care Coordination / Patient Safety, Preventive Health, and At-Risk Population. Of the 33 measures, 7 measures of patient / caregiver experience are collected via the CAHPS survey, 3 are calculated via claims, 1 is calculated from Medicare and Medicaid Electronic Health Record (EHR) Incentive Program data, and 22 are collected via the ACO Group Practice Reporting Option (GPRO) Web Interface.””””
“””The Health Level Seven International (HL7) QRDA is a standard document format for the exchange of electronic clinical quality measure (eCQM) data. QRDA reports contain data extracted from electronic health records (EHRs) and other information technology systems. QRDA reports are used for the exchange of eCQM data between systems for a variety of quality measurement and reporting initiatives, such as the Centers for Medicare & Medicaid Services (CMS) EHR Incentive Program: Meaningful Use Stage 2 (MU2).1
The Office of the National Coordinator for Health Information Technology (ONC) adopted QRDA as the standard to support both QRDA Category I (individual patient) and QRDA Category III (aggregate) data submission approaches for MU2 through final rulemaking in September 2012.2 CMS and ONC subsequently released an interim final rule in December 2012 that replaced the QRDA Category III standard adopted in the September 2012 final rule with an updated version of the standard.3 QRDA Category I and III implementation guides (IGs) are Draft Standards for Trial Use (DSTUs). DSTUs are issued at a point in the standards development life cycle when many, but not all, of the guiding requirements have been clarified. A DSTU is tested and then taken back through the HL7 ballot process to be formalized into an American National Standards Institute (ANSI)-accredited normative standard.
QRDA is a subset of CDA HL7 Standard; QRDA is a constraint on the HL7 Clinical Document Architecture (CDA), a document markup standard that specifies the structure and semantics of clinical documents for the purpose of exchange.4 To streamline implementations, QRDA makes use of CDA templates, which are business rules for representing clinical data consistently. Many QRDA templates are reused from the HL7 Consolidated CDA (C-CDA) standard5, which contains a library of commonly used templates that have been harmonized for MU2. Templates defined in the QRDA Category I and III IGs enable consistent representations of quality reporting data to streamline implementations and promote interoperability.”””
ONC under its purview has Office of Standards and Technology, which drives the Interoperability Standards; and it acknowledges that there are numerous challenges in realizing the ONC roadmap; as discussed in the below link
CDA helps in representing a comprehensive view of the patient; complete birth-to-death view – Longitudinal Record.
Also ONC Interoperability Specification Address the Following three levels (Not adequate to achieve EBM driven CDSS):-
There are three levels of health information technology interoperability: 1) Foundational; 2) Structural; and 3) Semantic.
1 – “Foundational” interoperability allows data exchange from one information technology system to be received by another and does not require the ability for the receiving information technology system to interpret the data.
2 – “Structural” interoperability is an intermediate level that defines the structure or format of data exchange (i.e., the message format standards) where there is uniform movement of healthcare data from one system to another such that the clinical or operational purpose and meaning of the data is preserved and unaltered. Structural interoperability defines the syntax of the data exchange. It ensures that data exchanges between information technology systems can be interpreted at the data field level.
3 – “Semantic” interoperability provides interoperability at the highest level, which is the ability of two or more systems or elements to exchange information and to use the information that has been exchanged. Semantic interoperability takes advantage of both the structuring of the data exchange and the codification of the data including vocabulary so that the receiving information technology systems can interpret the data. This level of interoperability supports the electronic exchange of patient summary information among caregivers and other authorized parties via potentially disparate electronic health record (EHR) systems and other systems to improve quality, safety, efficiency, and efficacy of healthcare delivery.
Desired or Recommended 2nd Order Semantic Interoperability
Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved.
From the above points it must be noted that QRDA and C-CDA achieves different things. Unfortunately, against MU attestation and quality reports that are filed by the eligible participants (physicians, hospitals and ACOs) based on QRDA (especially PQRA), CMS runs the EHR incentives program. Whereas, in the MU2 stage ( as per ONC), it is also required by the participants to demonstrate that they have achieved interoperability within ACO, while implementing HIE, this requires C-CDA. This stage must demonstrate that coordinated clinical processes have been achieved.
Also, clinical decision support system (CDSS) has been established addressing at least 5 critical or clinical priority areas. Unfortunately this particular capability does not seems to be addressed adequately by the ACOs; who only pursue to demonstrate quality measures have been achieved which necessarily does not mean clinical efficacy have been addressed.
It seems an important architectural problem has been glossed over by the policy designers, who proposed quality measures model with the motivation for capturing the metrics that eventually demonstrate “pay for performance”; and somehow assumed that the proposed metrics based on QRDA also demonstrate that the clinical efficacies have been achieved. This leads into systemic entropic discontinuity, where the efforts at macro states that represents healthcare management leading into healthcare efficiency is not necessarily a cumulative realization for the efforts at the micro states which represents gaining clinical efficacy. This entropic discountuinity between the macro state and the micro states is measured by Boltzmann Constant.
Link to more discussion on micro states and macro states within a complex system. Basically discusses for a given complex system, and for all the efforts towards the input; the entropy arrested and created loss, so the output is a actually created incurring loss. This means the systemic efficiency incurred losses and did not realize all the benefits arising out of the clinical efficacy. This is a model problem which inaccurately represents the “phenomenon of interest”.
To achieve Clinical Decision Support System capability which rather plays a very important role in enhancing clinical efficacy, developing data mining driven Evidence Based Medicine capability is imperative. This capability does not seem as being achieved because most HIE / ACO is being developed around QRDA; although discussed in the ONC Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap version 1.0 (Roadmap) [PDF – 3.7 MB]; unless data mining related algorithmic challenges are addressed which means standards beyond mere capture of the required data fields, interoperability efforts will be in vain.
Role of EBM in achieving CDSS discussed on following sites
“””It’s a chilling reality – one often overlooked in annual mortality statistics: Preventable medical errors persist as the No. 3 killer in the U.S. – third only to heart disease and cancer – claiming the lives of some 400,000 people each year. At a Senate hearing Thursday, patient safety officials put their best ideas forward on how to solve the crisis, with IT often at the center of discussions. “””
Bioingine (www.bioingine.com); a Cognitive Computing Platform transforms the patient information (millions of records) created by the HIE into Ecosystem Knowledge Landscape that is inherently evidence based, allowing for study of the Tacit Knowledge, as discovered from the millions of patient records (large data sets) by mining and knowledge inference in an automated way. This is achieved employing AI, Machine Learning and such techniques. Thereby, creating Clinical Decision Support System.
Enterprise (System) in reality is a complex interweaving mass. Architecture is a mere attempt to represent the complex entity via a systematic process while relying on some sense of an ‘Ontology’, which has classically relied on the Cartesian System. The dynamics emerging from the complex interplay among the various resources, with People being the most prominent – contributes to energy that is responsible for any work done. All-together in some sense the enterprise can only be best visualized as a spaghetti. Each strand being an instance of any ‘thing’ that the mind is capable of envisioning at that time. For instance a ‘business process’, or a ‘procedure’, or a technology enabler. More larger the enterprise the spaghetti depiction becomes that much more complex and so the dynamic interplay experiencing Cartesian Dilemma, eventually contributes to the break-down of the classical ontological model. And as always, an enterprise has a complex co-existence with other enterprises. This means a simple mathematical models can no longer be applied. Especially when the complexity compounds, the degree of freedom reduces (Theory of Constraints). This is the real reason for the present mess that wall-street and consequently the main-street is in.
One cannot argue that mortgage is the main contributor to the current financial systemic mess. It is is but one iota. Overall it is a complex systemic issue. Probably some theory that works in explaining the microcosms and its interdependency on the behavior of the other systems can best explain the ‘logic’ if any where the mechanistic ’cause and effect’ has a diminishing role to play. This is where the notion of ‘implicate order’ becomes important to understand the ontology behind the ‘Enterprise Architecture’.
Note: Cartesian System inherently promotes extreme linearization. These results into gaussian distribution and hence disparities in the system. Such a system is considered to be containing systemic fault owing to Cartesian Dilemma.
Alice Teaches White Elephant to Dance (WhiEle) Children are hollering. Fall is here. Myriad colors. Drivers on road are honking crazy. Beautiful fall. But rip-roaring are all. Ooh! this fizzling world has gone amok. Dizzy!! Convoluting colors are blinding. Yeah! what is this! Everything has gone white. Bizarre!! AM I feeling dizzy? Aaaaaalice! Its me. I have been waiting for you. For the one not caught-up by the convolution. Did you not see, the rest of the world has collapsed into a complex mass. It’s me the white elephant. I am WhiEle. I am the splinter of that mangled world.
(alice) Ho no! not rabbit hole again! Hey! Mr. WhiEle. Did you say, in you is concealed a mangled world. And, that you have sought me. And, why was that?
(WhiEle) Yes, I did seek you within myself, to remedy the maladies which afflict me.
(alice) Ooh!! sooo confusing. I dread to think that I was within you, a meaningless mass, that was once in a hurry. Jeeves! my dear mind, tell me WHO AM I?
(Jeeves) Aaaha!! very simple Alice, you are the spirit of that white elephant, you are the sentient being, the wakefulness of this world.
(alice) And, what are we all together to do?
(WhiEle) Dance, I mean, help me find a dance most befitting to me. I have always danced in the mall and enthralled shoppers. Now, I have become obscure. No one notices when I dance. Will you please help me find my theater?
(alice) OOh! WhiEle what a rambling rubble you have become. Don’t you worry. In your lamentation lies your answer. Ok!! where do we begin.
Separation of Concerns
(alice) You have become such a behemoth. It is practically impossible to treat you with all you together. So let me dissect you.
(WhiEle) Eeee! you mean you will tear me down.
(alice) No! no! not rip you physically. But yes, in a way I will. I will model you by breaking you down into pieces. I will hear all your lamentation and bemoaning. And each of that I will reduce until it will reduce no more. Into a hierarchy of the problem that you are I will model. Do you read me? And, what did you say is your goal. TO DANCE. To make people happy. To make society benefit from your existence. To that goal, I will model your problem and discover the hindering lumps within your rigid self. Do you know Mr. WhiEle, that unexercised cells within you either atrophies or become rigid. And, it is they who hinder you from doing what the world desires. After becoming a behemoth, you have lost the ability to see your self, both within and from outside. Rigidity has decreased your movements and increased blind spots. So you have become stiff like a stuffed cadaver. By disintegrating the problem, it can be separated in context of concerns. From all your lamentations, concerns can be discovered. It is required that the problems be separated in context of concerns and built into a hierarchy of structure. ‘Structure’ did I say. Pay close attention Mr.WhiEle. In structure lies your misery and also your happiness. In Christopher Alexander’s words, a logically structured problem, promotes loss of innocence. Also, a logically structured problem acquires a distinct ‘form’ which explicitly expresses a problem. Aaah! once your aspiration and the hindering forces are modeled into a structure, your mangled mass becomes more visible. And, now you can be ‘analyzed’.
(WhiEle) Can ever a problem be meaningful?
(alice) Most times yes. Problem instructs. But WhiEle, be aware not get paralyzed by over analysis. Hey wait! not done yet. Structure gains meaning when the problems are classified, categorized and characterized. Such a structure, ordered by nature, assumes a form which is layered into a framework.
Solution to Problem Fitment – Analysis and Synthesis
(alice) ‘Form’, the structure of a problem acquires, lends ‘form’ to solutions. Solution and problem together through their forms exists as an ensemble. Stress arises when misfits exists in the ensemble. It is to be desired in design to keep the stress low. Recurring problem have a common solution. The ensemble of forms of a ‘recurring problem – common solution’ is a ‘pattern’. There exists a contrast the way problem and solutions are dealt with. Problems are broken down until it is no more reducible and then analyzed. In contract, solution is designed in parts being discovered from broken down problem and then ‘synthesized’ into a bigger ‘whole’, before it is brought into beneficial use. Solution as a ‘whole’ is bigger than sum of its ‘parts’. It defeats the Cartesian principle. When problems are wrongly identified, then forms get spuriously introduced. The resulting forms prove to masquerade as solutions. No masquerading forms can stand test of time. The stress in such an ensemble ruptures sooner or later. Framework, a system of structure, helps in weeding out the masquerading forms. And, remember structures are paradoxical, they are also the inhibiting forces.
TathAstu! I know!! Abstraction and Aspect
(alice) In the process of defining a problem and seeking a congenial solution, abstraction is applied. It is an intuitive way to discover a form that the structure of the problem should acquire. This lends structure to the ensuing solution. Form represent the underlying content through abstraction. This helps in unraveling the unseen complexity. Hey! WhiEle. Who am I? And, what distinguishes me in the anima mundi? You huh! Talking to you, I nearly forgot, that I am talking to myself.
(Whiele) Alice! You are ruthless in subjecting me to apoptosis. Aha! you are my conscious part. And, How do you know that I am different from the lifeless object? Because, I have seen the unseen in you. Oops what the heck! Alice you are making me coil and recoil. Convoluted that already I am, please spare me from further misery. Hey! wait ! I know! I can abstract that unseen part of me and give it an aspect. I am a sentient being and have learnt to abstract the aspects of the unseen. This way I bring myself to comprehend the unseen and increase my conscious realization. Yes, also I realize that lacking in capacity to abstract I am unable to see the unseen. Aah! I can now model the unseen complex reality existing within my-self. OOh! I see this is the distinguishing factor in the anima mundi. Problems, most times lurking around the bends prove fatal. Rigidity means lacking in spontaneity to tackle problem. Rigidity leads to comatose state. Being conscious helps one improve agility. tathAstu! I know, I know, who really I am and what really I can.
(alice) Seee! You are finding answers from your within. Calypso it will be, WhiEle’s great Calypso. Bravo man those who dare they soar.
(alice) Dear WhiEle, ponder, ponder! what is it really about being agile? Isn’t it about flexible adaptive or generative growth, the resulting order of the structure of which is in unfoldment. The study of the ensemble of form that the problem (AS-IS) structure has assumed, helps in inferring the implied information. This inference facilitates in discovering a close fitment of the solution (TO-BE) under design (the enfolded reality) to the problem ensemble leading to its unfoldment. All architectures in nature are in unfoldment, in growth and decay, in a ceaseless process of evolving into higher order. By unfolding, architectures intends to gain wholeness. David Bohm, famous theoretical physicist, in his theory of the wholeness and the implicate order places more focus on wholeness and process than analysis of separate parts. In converging architectures multiplexing of processes is but evident.
Modeling is a Means Not an End
(alice) Okay! WhiEle now you know how to deal with a problem, how to design solutions, what role structure plays with both problem and solutions and finally the order that structures are unfolding into with convergence as the key aspect.
(WhiEle) Okay Alice! I have learnt the mantra. It is in model and in modeling where all the answers lies. It is the differentiator. With models I ‘become’.
(alice) Gosh!! goodness me. What a tragedy. I thought you learnt well enough about the masquerading forms. Mr. WhiEle when will your learn to discern. Learn that means is not an end into itself. Models are abstractions. They are the vaporizing perceptions of the intended reality, not reality themselves. Modeling helps the process of ‘becoming’. Gosh! WhiEle you were so close. Ok. Try again. OOh! who is this? A Kangaroo? Is it you WhiEle? Well!! looks like WhiEle learnt the ‘polymorph’ dance. You are truly an object of amusement. But hey! don’t be devoid of an objective.