system.model
sensorial-concept-Mcs (model)

McsHitp-creation:: {2020-08-02}

overview of model

description::
· model is a-secondary-entity which is-mapped with a-primary-one, the-archetype.
· output of methodMapping is the-secondary entity which is a-model of the-input.

name::
* Mcs.filMcsMdl.last.html!⇒model,
* Mcs.dirCor/filMcsMdl.last.html!⇒model,
* Mcs.MODEL,
* Mcs.methodMapping'02_output,
* Mcs.methodMapping'att002-output,
* Mcs.methodMapping'output-att002,
* Mcs.methodMapping'model,
* Mcs.model,
* Mcs.model'(system.model)!⇒model,
* Mcs.model-of-methodMapping,
* Mcs.rlnMapping'model!⇒model,
* Mcs.system.model!⇒model,

01_archetype of model

description::
· archetype is the-original entity that a-mapping-relation connects with another entity, the-model.
· input of methodMapping is the-primary entity that is-mapped to another one.

name::
* Mcs.archetype-of-methodMapping!⇒archo,
* Mcs.archetype-of-model!⇒archo,
* Mcs.archo, {2020-08-08},
* Mcs.entity-being-modeled!⇒archo,
* Mcs.methodMapping'01_input!⇒archo,
* Mcs.methodMapping'att001-input!⇒archo,
* Mcs.methodMapping'archetype!⇒archo,
* Mcs.methodMapping'input=att001!⇒archo,
* Mcs.model'01_archetype!⇒archo,
* Mcs.model'att001-archetype!⇒archo,
* Mcs.model'archetype!⇒archo,
* Mcs.original-entity-of-model!⇒archo,
* Mcs.rlnMapping'archetype!⇒archo,
* Mcs.subject-of-model!⇒archo,

02_mapping-relation (link) of model

03_mapping-method (link) of model

04_medium of model

description::
· The entity we use to construct a model: information, matter, both.
[hmnSgm-{2012-11-23}]

name::
* Mcs.model'04_medium,
* Mcs.model'att004-medium,
* Mcs.model'medium,

05_node of model

description::
·

name::
* Mcs.model'05_node,
* Mcs.model'att005-node,
* Mcs.model'node,

06_node-relation of model

description::
·

name::
* Mcs.model'06_node-relation,
* Mcs.model'att006-node-relation,
* Mcs.model'node-relation,

07_mapping-unit of model

description::
·

name::
* Mcs.model'07_mapping-unit,
* Mcs.model'att007-mapping-unit,
* Mcs.model'mapping-unit,

08_unit of model

description::
·

name::
* Mcs.model'08_unit,
* Mcs.model'att008_unit,
* Mcs.model'unit,

09_creator of model

description::
· the-entity that creates a-model.
· implementor is the-entity that uses the-method and does the-implementation.

name::
* Mcs.methodMapping'05_implementor,
* Mcs.methodMapping'att005-implementor,
* Mcs.methodMapping'implementor-att005,
* Mcs.model'09_creator,
* Mcs.model'att009-creator,
* Mcs.model'creator,
* Mcs.model'implementor,

10_user of model

description::
· model'user is an-entity that uses it.

name::
* Mcs.model'10_user,
* Mcs.model'att018-user,
* Mcs.model'user,

11_evaluation of model

description::
· defining what is of "value".
· degree of correctness of mapping-relation.
· usage, method, cost-time-reduction, ...
===
"In practice, as quality management terms, the definitions of verification and validation can be inconsistent. Sometimes they are even used interchangeably."
[{2020-08-09} https://en.wikipedia.org/wiki/Verification_and_validation]

name::
* Mcs.model'11_evaluation,
* Mcs.model'att011-evaluation,
* Mcs.model'evaluation,

evaluating (link) of model

resource of model

name::
* Mcs.model'resource,

addressWpg::
*

structure of model

name::
* Mcs.model'structure,

description::
*

DOING of model

description::
· modeling is any doing related to models, designing, creating, evaluating, using, ...

name::
* Mcs.model'doing,
* Mcs.modeling,

description::
* creating,
* evaluating,
* using,
* designing,
* comparing,
* revising,
* testing,

creating of model

description::
· implementation of methodMapping is the-mapping-acting of the-method.

name::
* Mcs.methodMapping'04_implementation,
* Mcs.methodMapping'att004-implementation,
* Mcs.methodMapping'implementation-att004,
* Mcs.model'att010-creating,
* Mcs.model'creating,
* Mcs.model'building,
* Mcs.model'implementation,

evaluating of model

description::
· model'evaluating is the-process of finding the-degree of correctness of archetype-model-mapping, ie the-problem-of-truth!!!
* experiment,
* testing,
* time,

name::
* Mcs.model'att016-evaluating,
* Mcs.model'evaluating,

addressWpg::
* https://en.wikipedia.org/wiki/Verification_and_validation_of_computer_simulation_models,
* https://en.wikipedia.org/wiki/Software_verification,

using of model

description::
a) the-process of operating a-model.
b) the-process we are-doing with the-model.

name::
* Mcs.model'att017-using,
* Mcs.model'using,

using.learning of model

description::
"Models are vehicles for learning about the world. Significant parts of scientific investigation are carried out on models rather than on reality itself because by studying a model we can discover features of and ascertain facts about the system the model stands for; in brief, models allow for surrogative reasoning (Swoyer 1991). For instance, we study the nature of the hydrogen atom, the dynamics of populations, or the behavior of polymers by studying their respective models. This cognitive function of models has been widely acknowledged in the literature, and some even suggest that models give rise to a new style of reasoning, so-called ‘model based reasoning’ (Magnani and Nersessian 2002, Magnani, Nersessian and Thagard 1999). This leaves us with the question of how learning with a model is possible.
Hughes (1997) provides a general framework for discussing this question. According to his so-called DDI account, learning takes place in three stages: denotation, demonstration, and interpretation. We begin by establishing a representation relation (‘denotation’) between the model and the target. Then we investigate the features of the model in order to demonstrate certain theoretical claims about its internal constitution or mechanism; i.e. we learn about the model (‘demonstration’). Finally, these findings have to be converted into claims about the target system; Hughes refers to this step as ‘interpretation’. It is the latter two notions that are at stake here."
[http://plato.stanford.edu/entries/models-science/]

name::
* Mcs.learing.with-model,
* Mcs.model'using'learing,

evoluting of model

name::
* Mcs.model'evoluting,

{time.2020-08-02}::
=== McsHitp-creation:
· creation of current concept.

WHOLE-PART-TREE of model

name::
* Mcs.model'whole-part-tree,

whole-tree-of-model::
*
* ... Sympan.

part-tree-of-model::
*

GENERIC-SPECIFIC-TREE of model

name::
* Mcs.model'generic-specific-tree,
* Mcs.model.specific,

generic-tree-of-model::
* system,
* entity,

specific-tree-of-model::
* concept,
* conceptual-model,
* conceptual-model.sensorial,

model.specs-division-on.evaluation

description::
* accurate-model,
* accurate-strong-model,
* accurate-weak-model,
* accurate-accurateNo-model,
* accurateNo-weak-model,
* accurateNo-strong-model,
* accurateNo-model,

name::
* Mcs.model'att012-specs-division-on.evaluation,
* Mcs.model.specs-division-on.evaluation,

model.specs-division-on.medium

description::
* same-medium-model,
* sameNo-medium-model,
===
* mental-model,
* mentalNo-model,
===
* computer-model,
* info-model,
* text-model,
* theory,
* visual-model,

name::
* Mcs.model'att013-specs-division-on.medium,
* Mcs.model.specs-division-on.medium,

model.specs-division-on.mapping-method

description::
* one-way-model,
* two-way-model,
===
* analogical-model,
* analogicalNo-model,
===
* abstract-model,
* abstractNo-model,
===
* math-model,
* linear-model,
* scientific-model,
* statistical-model,

name::
* Mcs.model'att014-specs-division-on.mapping-method,
* Mcs.model.specs-division-on.mapping-method,

model.specs-division-on.archetype

description::
* body-model,
* process-model,
===
* economy-model,
* social-model,
* society-model,
* organism-model,
* organization-model,
* worldview-model,

name::
* Mcs.model'att015-specs-division-no.archetype,

model.specs-division-on.creator

description::
· human-model,
· humanNo-model,

name::
* Mcs.model'att019-specs-division-on.creator,
* Mcs.model'specs-division-on.creator,

model.codomain-026

description::
· codomain-of-model is the-set of all models.

name::
* Mcs.codomain-of-model,
* Mcs.model.026-codomain,
* Mcs.model.aggregate,
* Mcs.model.codomain,

domain of model

description::
· domain-of-model is the-set of all its archetypes.

name::
* Mcs.domain-of-model,
* Mcs.model'domain,

model.method.scientific-005

description::
"A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way. All models are in simulacra, that is, simplified reflections of reality that, despite being approximations, can be extremely useful.[6] Building and disputing models is fundamental to the scientific enterprise. Complete and true representation may be impossible, but scientific debate often concerns which is the better model for a given task, e.g., which is the more accurate climate model for seasonal forecasting.[7]
Attempts to formalize the principles of the empirical sciences use an interpretation to model reality, in the same way logicians axiomatize the principles of logic. The aim of these attempts is to construct a formal system that will not produce theoretical consequences that are contrary to what is found in reality. Predictions or other statements drawn from such a formal system mirror or map the real world only insofar as these scientific models are true.[8][9]
For the scientist, a model is also a way in which the human thought processes can be amplified.[10] For instance, models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon, or process being represented. Such computer models are in silico. Other types of scientific models are in vivo (living models, such as laboratory rats) and in vitro (in glassware, such as tissue culture).[11]"
[{2020-08-04} https://en.wikipedia.org/wiki/Scientific_modelling]

name::
* Mcs.modelSci,
* Mcs.model.005-scientific!⇒modelSci,
* Mcs.model.method.scientific!⇒modelSci,
* Mcs.model.scientific!⇒modelSci,

evaluation of modelSci

description::
"Models are of central importance in many scientific contexts.
The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point.
Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools.
In short, models are one of the principal instruments of modern science."
[http://plato.stanford.edu/entries/models-science/]

name::
* Mcs.modelSci'evaluation,

model.method.math-006

description::
· modelMath quantify the-archetype.

name::
* Mcs.mathematical-model!⇒modelMath,
* Mcs.modelMath,
* Mcs.model.006-math!⇒modelMath,
* Mcs.model.math!⇒modelMath,
* Mcs.model.method.math!⇒modelMath,

model.method.statistical-007

description::
"A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process.[1]
A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables. As such, a statistical model is "a formal representation of a theory" (Herman Adèr quoting Kenneth Bollen).[2]
All statistical hypothesis tests and all statistical estimators are derived via statistical models. More generally, statistical models are part of the foundation of statistical inference."
[{2020-08-05} https://en.wikipedia.org/wiki/Statistical_model]

name::
* Mcs.modelStats,
* Mcs.model.007-statistical!⇒modelStats,
* Mcs.model.statistical!⇒modelStats,
* Mcs.model.method.statistical!⇒modelStats,
* Mcs.probabilistic-model!⇒modelStats,
* Mcs.statistical-model!⇒modelStats,

all-models-are-wrong

description::
""All models are wrong" is a common aphorism in statistics; it is often expanded as "All models are wrong, but some are useful". It is usually considered to be applicable to not only statistical models, but to scientific models generally. The aphorism recognizes that statistical/scientific models always fall short of the complexities of reality but can still be of use.
The aphorism is generally attributed to the statistician George Box, although the underlying concept predates Box's writings."
[{2020-08-05} https://en.wikipedia.org/wiki/All_models_are_wrong]

name::
* Mcs.all-models-are-wrong,
* Mcs.modelStats'all-models-are-wrong,

model.method.abstract-009

description::
· Abstract-model is a model if it misses some attributes of the archetype.
Every generic-concept is an abstract-model of its referents.
[hmnSgm-{2014-02-27}]

name::
* Mcs.abstract-model!⇒modelAbstract,
* Mcs.modelAbstract,
* Mcs.model.009-simulation!⇒modelAbstract,
* Mcs.model.abstract!⇒modelAbstract,
* Mcs.model.method.abstract!⇒modelAbstract,
* Mcs.model.simulation!⇒modelAbstract,
* Mcs.simulation!⇒modelAbstract,

descriptionLong::
"A simulation is an approximate imitation of the operation of a process or system;[1] that represents its operation over time.
Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games. Often, computer experiments are used to study simulation models. Simulation is also used with scientific modelling of natural systems or human systems to gain insight into their functioning,[2] as in economics. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Simulation is also used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.[3]
Key issues in simulation include the acquisition of valid sources of information about the relevant selection of key characteristics and behaviors, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes. Procedures and protocols for model verification and validation are an ongoing field of academic study, refinement, research and development in simulations technology or practice, particularly in the work of computer simulation."
[{2020-08-05} https://en.wikipedia.org/wiki/Simulation]

resource of modelAbstract

description::
"Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model", and then either "run the model" or equivalently "run a simulation"."
[{2020-08-07} https://en.wikipedia.org/wiki/Computer_simulation]

name::
* Mcs.modelAbstract'resouce,

modelAbstract.SPECIFIC

description::
* concept,

name::
* Mcs.modelAbstact.specific,

modelAbstract.idealized

description::
"An idealization is a deliberate simplification of something complicated with the objective of making it more tractable."
[http://plato.stanford.edu/entries/models-science/]

name::
* Mcs.idealization!⇒modelIdealized,
* Mcs.modelIdealized,
* Mcs.modelAbstract.idealized!⇒modelIdealized,
* Mcs.model.011-idealized!⇒modelIdealized,
* Mcs.model.idealized!⇒modelIdealized,

descriptionLong::
"Idealized models. An idealization is a deliberate simplification of something complicated with the objective of making it more tractable. Frictionless planes, point masses, infinite velocities, isolated systems, omniscient agents, or markets in perfect equilibrium are but some well-know examples. Philosophical debates over idealization have focused on two general kinds of idealizations: so-called Aristotelian and Galilean idealizations.
Aristotelian idealization amounts to ‘stripping away’, in our imagination, all properties from a concrete object that we believe are not relevant to the problem at hand. This allows us to focus on a limited set of properties in isolation. An example is a classical mechanics model of the planetary system, describing the planets as objects only having shape and mass, disregarding all other properties. Other labels for this kind of idealization include ‘abstraction’ (Cartwright 1989, Ch. 5), ‘negligibility assumptions’ (Musgrave 1981) and ‘method of isolation’ (Mäki 1994).
Galilean idealizations are ones that involve deliberate distortions. Physicists build models consisting of point masses moving on frictionless planes, economists assume that agents are omniscient, biologists study isolated populations, and so on. It was characteristic of Galileo's approach to science to use simplifications of this sort whenever a situation was too complicated to tackle. For this reason it is common to refer to this sort of idealizations as ‘Galilean idealizations’ (McMullin 1985); another common label is ‘distorted models’.
Galilean idealizations are beset with riddles. What does a model involving distortions of this kind tell us about reality? How can we test its accuracy? In reply to these questions Laymon (1991) has put forward a theory which understands idealizations as ideal limits: imagine a series of experimental refinements of the actual situation which approach the postulated limit and then require that the closer the properties of a system come to the ideal limit, the closer its behavior has to come to the behavior of the ideal limit (monotonicity). But these conditions need not always hold and it is not clear how to understand situations in which no ideal limit exists. We can, at least in principle, produce a series of table tops that are ever more slippery but we cannot possibly produce a series of systems in which Planck's constant approaches zero. This raises the question of whether one can always make an idealized model more realistic by de-idealizing it. We will come back to this issue in section 5.1.
Galilean and Aristotelian idealizations are not mutually exclusive. On the contrary, they often come together. Consider again the mechanical model of the planetary system: the model only takes into account a narrow set of properties and distorts these, for instance by describing planets as ideal spheres with a rotation-symmetric mass distribution.
Models that involve substantial Galilean as well as Aristotelian idealizations are sometimes referred to as ‘caricatures’ (Gibbard and Varian 1978). Caricature models isolate a small number of salient characteristics of a system and distort them into an extreme case. A classical example is Ackerlof's (1970) model of the car market, which explains the difference in price between new and used cars solely in terms of asymmetric information, thereby disregarding all other factors that may influence prices of cars. However, it is controversial whether such highly idealised models can still be regarded as informative representations of their target systems (for a discussion of caricature models, in particular in economics, see Reiss 2006).
At this point we would like to mention a notion that seems to be closely related to idealization, namely approximation. Although the terms are sometimes used interchangeably, there seems to be a substantial difference between the two. Approximations are introduced in a mathematical context. One mathematical item is an approximation of another one if it is close to it in some relevant sense. What this item is may vary. Sometimes we want to approximate one curve with another one. This happens when we expand a function into a power series and only keep the first two or three terms. In other situations we approximate an equation by another one by letting a control parameter tend towards zero (Redhead 1980). The salient point is that the issue of physical interpretation need not arise. Unlike Galilean idealization, which involves a distortion of a real system, approximation is a purely formal matter. This, of course, does not imply that there cannot be interesting relations between approximations and idealization. For instance, an approximation can be justified by pointing out that it is the ‘mathematical pendant’ to an acceptable idealization (e.g. when we neglect a dissipative term in an equation because we make the idealizing assumption that the system is frictionless)."
[http://plato.stanford.edu/entries/models-science/]

model.method.abstractNo-010

description::
· abstractNo-model is a-model identical with its archetype.

name::
* Mcs.abstractNo-model!⇒modelAbstractNo,
* Mcs.modelAbstractNo,
* Mcs.model.010-abstractNo!⇒modelAbstractNo,
* Mcs.model.abstractNo!⇒modelAbstractNo,
* Mcs.model.method.abstractNo!⇒modelAbstractNo,

model.creator.human-014

description::
· modelHuman is a-model with creator a-human.

name::
* Mcs.modelHuman,
* Mcs.model.014-human!⇒modelHuman,
* Mcs.model.creator.human!⇒modelHuman,
* Mcs.model.human!⇒modelHuman,

model.creator.humanNo-015

description::
· modelHumanNo is a-model without creator a-human.

name::
* Mcs.modelHumanNo,
* Mcs.model.015-humanNo!⇒modelHumanNo,
* Mcs.model.creator.humanNo!⇒modelHumanNo,
* Mcs.model.humanNo!⇒modelHumanNo,

model.medium.computer-021

description::
· modelCmr is a-model with medium a-computer.

name::
* Mcs.computational-model!⇒modelCmr,
* Mcs.computer-model!⇒modelCmr,
* Mcs.modelCmr,
* Mcs.model.021-computer!⇒modelCmr,
* Mcs.model.medium.computer!⇒modelCmr,
* Mcs.model.computer!⇒modelCmr,

archetype of modelCmr

description::
· anything.

name::
* Mcs.modelCmr'archetype,

mapping-method of modelCmr

description::
·

name::
* Mcs.modelCmr'mapping-method,

simulation-language of modelCmr

description::
· simulation-language is a-programing-language used to create computer-models.
===
"A computer simulation language is used to describe the operation of a simulation on a computer.[1][2] There are two major types of simulation: continuous and discrete event though more modern languages can handle more complex combinations. Most languages also have a graphical interface and at least a simple statistic gathering capability for the analysis of the results. An important part of discrete-event languages is the ability to generate pseudo-random numbers and variants from different probability distributions."
[{2020-08-09} https://en.wikipedia.org/wiki/Simulation_language]

name::
* Mcs.computer-simulation-language!⇒lagSim,
* Mcs.lagSim,
* Mcs.modelCmr'simulation-language!⇒lagSim,
* Mcs.simulation-language!⇒lagSim,

simulation-program of modelCmr

description::
· simulation-program is a-computer-program we use to create computer-models.

name::
* Mcs.CpgmSim,
* Mcs.computer-simulation-program!⇒CpgmSim,
* Mcs.modelCmr'simulation-program!⇒CpgmSim,
* Mcs.simulation-program!⇒CpgmSim,
* Mcs.simulation-software!⇒CpgmSim,

addressWpg::
* https://en.wikipedia.org/wiki/List_of_computer_simulation_software,

computer of modelCmr

description::
· a-computer is the-medium of a-modelCmr.

name::
* Mcs.modelCmr'computer,

evaluation of modelCmr

description::
"... avoids actual experimentation, which can be costly and time-consuming. Instead, mathematical knowledge and computational power is used to solve real-world problems cheaply and in a time efficient manner."
[{2020-08-09} https://en.wikipedia.org/wiki/Modeling_and_simulation]

name::
* Mcs.modelCmr'evaluation,

resource of modelCmr

description::
* https://en.wikipedia.org/wiki/Computer_simulation,
* https://en.wikipedia.org/wiki/Modeling_and_simulation,

name::
* Mcs.modelCmr'resouce,

DOING of modelCmr

description::
· any doing related to modelCmr.

name::
* Mcs.modelCmr'doing,

descriptionLong::
"A computer model is the algorithms and equations used to capture the behavior of the system being modeled. By contrast, computer simulation is the actual running of the program that contains these equations or algorithms. Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model", and then either "run the model" or equivalently "run a simulation"."
[{2020-08-09} https://en.wikipedia.org/wiki/Computer_simulation#Simulation_versus_model]

modelCmr.SPECIFIC

description::
* McsHitp-modelConceptSensorial,
* computer-program,

name::
* Mcs.modelCmr.specific,

modelCmr.discrete-event-001

description::
"A discrete-event simulation (DES) models the operation of a system as a (discrete) sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system.[1] Between consecutive events, no change in the system is assumed to occur; thus the simulation time can directly jump to the occurrence time of the next event, which is called next-event time progression.
In addition to next-event time progression, there is also an alternative approach, called fixed-increment time progression, where time is broken up into small time slices and the system state is updated according to the set of events/activities happening in the time slice.[2] Because not every time slice has to be simulated, a next-event time simulation can typically run much faster than a corresponding fixed-increment time simulation.
Both forms of DES contrast with continuous simulation in which the system state is changed continuously over time on the basis of a set of differential equations defining the rates of change of state variables."
[{2020-08-09} https://en.wikipedia.org/wiki/Discrete-event_simulation]

name::
* Mcs.DES-discrete-event-simulation!⇒modelCmrDiscrete,
* Mcs.modelCmrDiscrete,
* Mcs.modelCmr.001-discrete-event!⇒modelCmrDiscrete,
* Mcs.modelCmr.discrete-event!⇒modelCmrDiscrete,

modelCmr.continuous-event-002

description::
"Continuous Simulation refers to a computer model of a physical system that continuously tracks system response according to a set of equations typically involving differential equations.[1][2]"
[{2020-08-09} https://en.wikipedia.org/wiki/Continuous_simulation]

name::
* Mcs.modelCmrContinuous,
* Mcs.modelCmr.002-continuous-event!⇒modelCmrContinuous,
* Mcs.modelCmr.continuous-event!⇒modelCmrContinuous,

modelCmr.web-003

description::
"Web-based simulation (WBS) is the invocation of computer simulation services over the World Wide Web, specifically through a web browser.[1][2][3][4] Increasingly, the web is being looked upon as an environment for providing modeling and simulation applications, and as such, is an emerging area of investigation within the simulation community.[4][5][6]"
[{2020-08-09} https://en.wikipedia.org/wiki/Web-based_simulation]

name::
* Mcs.WBS-web-based-simulation!⇒modelWeb,
* Mcs.modelCmr.003-web!⇒modelWeb,
* Mcs.modelCmr.web!⇒modelWeb,
* Mcs.modelWeb,
* Mcs.web-modelCmr!⇒modelWeb,

model.medium.visual-008

description::
· modelVisual visualizes the-archetype.

name::
* Mcs.modelVisual,
* Mcs.model.008-visual!⇒modelVisual,
* Mcs.model.medium.visual!⇒modelVisual,
* Mcs.model.visual!⇒modelVisual,
* Mcs.visual-model!⇒modelVisual,

model.medium.infoBio-012 (link)

model.medium.infoBioNo-013

description::
· modelInfoBioNo is a-model with medium NOT infoBio.

name::
* Mcs.modelInfoBioNo,
* Mcs.model.013-infoBioNo!⇒modelInfoBioNo,
* Mcs.model.infoBioNo!⇒modelInfoBioNo,
* Mcs.model.medium.infoBioNo!⇒modelInfoBioNo,

model.medium.physical-020

description::
· modelPhysical model with medium mentalNo, not info.

name::
* Mcs.modelPhysical,
* Mcs.model.020-physical!⇒modelPhysical,
* Mcs.model.medium.physical!⇒modelPhysical,
* Mcs.model.physical!⇒modelPhysical,

model.archo.process-016

description::
· modelProcess is a-model of a-process.

name::
* Mcs.modelProcess,
* Mcs.model.016-process!⇒modelProcess,
* Mcs.model.archo.process!⇒modelProcess,
* Mcs.model.process!⇒modelProcess,

model.archo.body-017

description::
· modelBody is a-model of a-body.

name::
* Mcs.modelBody,
* Mcs.model.017-body!⇒modelBody,
* Mcs.model.archo.body!⇒modelBody,
* Mcs.model.body!⇒modelBody,

model.archo.system-018

description::
· modelSys is a-model of a-system.

name::
* Mcs.modelSys,
* Mcs.model.018-system!⇒modelSys,
* Mcs.model.archo.system!⇒modelSys,
* Mcs.model.system!⇒modelSys,

model.archo.sysDynamic-019

description::
· modelSysDynamic is a-model of a-sysDynamic.

name::
* Mcs.modelSysDynamic,
* Mcs.model.019-sysDynamic!⇒modelSysDynamic,
* Mcs.model.archo.sysDynamic!⇒modelSysDynamic,
* Mcs.model.sysDynamic!⇒modelSysDynamic,

model.archo.organism-022

description::
· a-model of an-organism.

name::
* Mcs.modelOgm,
* Mcs.model.022-organism!⇒modelOgm,
* Mcs.model.archo.organism!⇒modelOgm,
* Mcs.model.organism!⇒modelOgm,
* Mcs.organism-model!⇒modelOgm,

addressWpg::
* https://www.nytimes.com/2012/07/21/science/in-a-first-an-entire-organism-is-simulated-by-software.html,
* https://en.wikipedia.org/wiki/Synthetic_Organism_Designer,

model.archo.organization-023

description::
· a-model of an-human-organization.

name::
* Mcs.modelOzn,
* Mcs.model.023-organization!⇒modelOzn,
* Mcs.model.archo.organization!⇒modelOzn,
* Mcs.model.organization!⇒modelOzn,
* Mcs.organization-model!⇒modelOzn,

model.archo.society-024

description::
· a-model of a-human-society.

name::
* Mcs.modelSoc,
* Mcs.model.024-society!⇒modelSoc,
* Mcs.model.archo.society!⇒modelSoc,
* Mcs.model.society!⇒modelSoc,
* Mcs.society-model!⇒modelSoc,

model.archo.economy-025

description::
· a-model of a-human-economy.

name::
* Mcs.economy-model!⇒modelEcon,
* Mcs.modelEcon,
* Mcs.model.025-economy!⇒modelEcon,
* Mcs.model.archo.economy!⇒modelEcon,
* Mcs.model.economy!⇒modelEcon,

meta-info

this page was-visited times since {2020-08-02}

page-wholepath: synagonism.net / Mcs-worldview / dirCor / model

SEARCH::
· this page uses 'locator-names', names that when you find them, you find the-LOCATION of the-concept they denote.
GLOBAL-SEARCH:
· clicking on the-green-BAR of a-page you have access to the-global--locator-names of my-site.
· use the-prefix 'model' for sensorial-concepts related to current concept 'entity.model'.
LOCAL-SEARCH:
· TYPE CTRL+F "Mcs.words-of-concept's-name", to go to the-LOCATION of the-concept.
· a-preview of the-description of a-global-name makes reading fast.

footer::
• author: Kaseluris.Nikos.1959
• email:
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• edit on github: https://github.com/synagonism/Mcsw/blob/master/dirCor/filMcsMdl.last.html,
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• twitter: @synagonism,
• steemit: https://steemit.com/@synagonism,

webpage-versions::
filMcsMdl.last.html: dynamic,
• filMcsMdl.0-1-0.2020-08-02.last.html: draft creation,

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