senso-concept-Mcs (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::

* McsEngl.McsCor000012.last.html//dirCor//dirMcs!⇒model,

* McsEngl.dirMcs/dirCor/McsCor000012.last.html!⇒model,

* McsEngl.methodMapping'02_output,

* McsEngl.methodMapping'att002-output,

* McsEngl.methodMapping'output-att002,

* McsEngl.methodMapping'model,

* McsEngl.model,

* McsEngl.model!=McsCor000012,

* McsEngl.model!=system.model,

* McsEngl.model-of-methodMapping,

* McsEngl.rlnMapping'model!⇒model,

* McsEngl.rlnMapping'output!⇒model,

* McsEngl.rlnMapping'target!⇒model,

* McsEngl.system.model!⇒model,

* McsEngl.target-of-rlnMapping!⇒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::

* McsEngl.archetype-of-rlnMapping!⇒archo,

* McsEngl.archetype-of-model!⇒archo,

* McsEngl.archo, {2020-08-08},

* McsEngl.entity-being-modeled!⇒archo,

* McsEngl.rlnMapping'01_input!⇒archo,

* McsEngl.rlnMapping'att001-input!⇒archo,

* McsEngl.rlnMapping'archetype!⇒archo,

* McsEngl.rlnMapping'input=att001!⇒archo,

* McsEngl.model'01_archetype!⇒archo,

* McsEngl.model'att001-archetype!⇒archo,

* McsEngl.model'archetype!⇒archo,

* McsEngl.original-entity--of-model!⇒archo,

* McsEngl.rlnMapping'archetype!⇒archo,

* McsEngl.primary-entity-of-model!⇒archo,

* McsEngl.source-of-model!⇒archo,

* McsEngl.subject-of-model!⇒archo,

====== langoGreek:

* McsElln.αρχέτυπο!=archo,

description::

· The entity we use to construct a model: information, matter, both.

[hmnSngo-{2012-11-23}]

name::

* McsEngl.model'05_medium,

* McsEngl.model'att004-medium,

* McsEngl.model'medium,

name::

* McsEngl.model'06_node,

* McsEngl.model'att005-node,

* McsEngl.model'node,

name::

* McsEngl.model'07_node-relation,

* McsEngl.model'att006-node-relation,

* McsEngl.model'node-relation,

name::

* McsEngl.model'08_mapping-unit,

* McsEngl.model'att007-mapping-unit,

* McsEngl.model'mapping-unit,

name::

* McsEngl.model'09_unit,

* McsEngl.model'att008_unit,

* McsEngl.model'unit,

description::

· the-entity that creates a-model.

· implementor is the-entity that uses the-method and does the-implementation.

name::

* McsEngl.methodMapping'10_implementor!⇒modeler,

* McsEngl.methodMapping'att005-implementor!⇒modeler,

* McsEngl.methodMapping'implementor!⇒modeler,

* McsEngl.model'09_creator!⇒modeler,

* McsEngl.model'att009-creator!⇒modeler,

* McsEngl.model'builder!⇒modeler,

* McsEngl.model'creator!⇒modeler,

* McsEngl.model'implementor!⇒modeler,

* McsEngl.modeler,

description::

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

name::

* McsEngl.model'11_user,

* McsEngl.model'att018-user,

* McsEngl.model'user,

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::

* McsEngl.evaluation.model,

* McsEngl.model'12_evaluation,

* McsEngl.model'att011-evaluation,

* McsEngl.model'evaluation,

generic-tree-of-model-evaluation::

* evaluation,

description::

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

name::

* McsEngl.model'doing,

* McsEngl.modeling,

description::

* creating,

* evaluating,

* using,

* designing,

* comparing,

* revising,

* testing,

description::

· implementation of methodMapping is the-mapping-doing of the-method.

· mapping is the-doing of association of a-primary-entity (archetype) with another one (the-model) on a-system of rules.

name::

* McsEngl.doing.mapping!⇒dngMapping,

* McsEngl.dngMapping,

* McsEngl.mapping-action!⇒dngMapping,

* McsEngl.methodMapping'04_implementation!⇒dngMapping,

* McsEngl.methodMapping'att004-implementation!⇒dngMapping,

* McsEngl.methodMapping'implementation!⇒dngMapping,

* McsEngl.model'att010-creating!⇒dngMapping,

* McsEngl.model'building!⇒dngMapping,

* McsEngl.model'construction!⇒dngMapping,

* McsEngl.model'creating!⇒dngMapping,

* McsEngl.model'development!⇒dngMapping,

* McsEngl.model'implementation!⇒dngMapping,

* McsEngl.model'mapping-doing!⇒dngMapping,

* McsEngl.to-map!~verbEnglA2:map-s-ped-ping-ped!=dngMapping,

====== langoGreek:

* McsElln.αντιστοίχιση!=dngMapping,

* McsEngl.verbElln.αντιστοιχώ!=dngMapping,

* McsElln.αντιστοιχώ!~verbElln!=dngMapping,

description::

* archetype,

* model,

* creator|doer,

* medium,

* method,

* rlnMapping,

* evaluating,

name::

* McsEngl.argMapping,

* McsEngl.dngMapping'argument!⇒argMapping,

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::

* McsEngl.model'att016-evaluating,

* McsEngl.model'evaluating,

* McsEngl.model'validation,

addressWpg::

* model evaluation: https://www.nap.edu/read/11972/chapter/6,

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

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

specific-tree-of-model'evaluating::

* referenting,

description::

a) the-process of operating a-model.

b) the-process we are-doing with the-model.

name::

* McsEngl.model'att017-using,

* McsEngl.model'application,

* McsEngl.model'using,

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::

* McsEngl.learing.with-model,

* McsEngl.model'using'learing,

name::

* McsEngl.evoluting-of-model,

* McsEngl.model'evoluting,

{2020-08-02}::

=== McsHitp-creation:

· creation of current concept.

description::

* proble-identification,

* conceptual-model,

* constructed-model,

* model-use,

name::

* McsEngl.model'whole-part-tree,

whole-tree-of-model::

*

* ... Sympan.

name::

* McsEngl.model'generic-specific-tree,

* McsEngl.model.specific,

generic-tree-of-model::

* system,

* entity,

specific-tree-of-model::

* concept,

* conceptual-model,

* conceptual-model.sensorial,

description::

* accurate-model,

* accurate-strong-model,

* accurate-weak-model,

* accurate-accurateNo-model,

* accurateNo-weak-model,

* accurateNo-strong-model,

* accurateNo-model,

name::

* McsEngl.model'att012-specs-division-on.evaluation,

* McsEngl.model.spec-div.evaluation,

description::

* same-medium-model,

* sameNo-medium-model,

===

* mental-model,

* mentalNo-model,

===

* computer-model,

* info-model,

* text-model,

* theory,

* visual-model,

name::

* McsEngl.model'att013-specs-division-on.medium,

* McsEngl.model.spec-div.medium,

description::

* one-way-model,

* two-way-model,

===

* analogical-model,

* analogicalNo-model,

===

* abstract-model,

* abstractNo-model,

===

* math-model,

* linear-model,

* scientific-model,

* statistical-model,

name::

* McsEngl.model'att014-specs-division-on.mapping-method,

* McsEngl.model.spec-div.mapping-method,

description::

* body-model,

* process-model,

===

* economy-model,

* social-model,

* society-model,

* organism-model,

* organization-model,

* worldview-model,

description::

· human-model,

· humanNo-model,

name::

* McsEngl.model'att019-specs-division-on.creator,

* McsEngl.model'specs-division-on.creator,

description::

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

name::

* McsEngl.codomain-of-model,

* McsEngl.model.026-codomain,

* McsEngl.model.aggregate,

* McsEngl.model.codomain,

description::

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

description::

· accurate-model is a-model that maps correctly its archetype.

name::

* McsEngl.accurate-model!⇒modelAccurate,

* McsEngl.model.027-accurate!⇒modelAccurate,

* McsEngl.model.accurate!⇒modelAccurate,

* McsEngl.model.evaluating.accurate!⇒modelAccurate,

* McsEngl.modelAccurate,

* McsEngl.true-model!⇒modelAccurate,

* McsEngl.valid-model!⇒modelAccurate,

name::

* McsEngl.model.028-accurateStrong!⇒modelAccurateStrong,

* McsEngl.model.accurateStrong!⇒modelAccurateStrong,

* McsEngl.model.evaluating.accurateStrong!⇒modelAccurateStrong,

* McsEngl.modelAccurateStrong,

* McsEngl.trueStrong-model!⇒modelAccurateStrong,

* McsEngl.validStrong-model!⇒modelAccurateStrong,

name::

* McsEngl.model.029-accurateWeak!⇒modelAccurateWeak,

* McsEngl.model.accurateWeak!⇒modelAccurateWeak,

* McsEngl.model.evaluating.accurateWeak!⇒modelAccurateWeak,

* McsEngl.modelAccurateWeak,

* McsEngl.trueWeak-model!⇒modelAccurateWeak,

* McsEngl.validWeak-model!⇒modelAccurateWeak,

name::

* McsEngl.model.030-accurateAccurateNo!⇒modelAccurateAccurateNo,

* McsEngl.model.accurateAccurateNo!⇒modelAccurateAccurateNo,

* McsEngl.model.evaluating.accurateAccurateNo!⇒modelAccurateAccurateNo,

* McsEngl.modelAccurateAccurateNo,

name::

* McsEngl.falseWeak-model!⇒modelAccurateNoWeak,

* McsEngl.invalidWeak-model!⇒modelAccurateNoWeak,

* McsEngl.model.031-accurateNoWeak!⇒modelAccurateNoWeak,

* McsEngl.model.accurateNoWeak!⇒modelAccurateNoWeak,

* McsEngl.model.evaluating.accurateNoWeak!⇒modelAccurateNoWeak,

* McsEngl.modelAccurateNoWeak,

name::

* McsEngl.falseStrong-model!⇒modelAccurateNoStrong,

* McsEngl.invalidStrong-model!⇒modelAccurateNoStrong,

* McsEngl.model.032-accurateNoStrong!⇒modelAccurateNoStrong,

* McsEngl.model.accurateNoStrong!⇒modelAccurateNoStrong,

* McsEngl.model.evaluating.accurateNoStrong!⇒modelAccurateNoStrong,

* McsEngl.modelAccurateNoStrong,

description::

· inaccurate-model is a-model that does-not-map correctly its
archetype.

name::

* McsEngl.false-model!⇒modelAccurateNo,

* McsEngl.inaccurate-model!⇒modelAccurateNo,

* McsEngl.invalid-model!⇒modelAccurateNo,

* McsEngl.model.033-accurateNo!⇒modelAccurateNo,

* McsEngl.model.accurateNo!⇒modelAccurateNo,

* McsEngl.model.evaluating.accurateNo!⇒modelAccurateNo,

* McsEngl.modelAccurateNo,

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::

* McsEngl.modelSci,

* McsEngl.model.005-scientific!⇒modelSci,

* McsEngl.model.method.scientific!⇒modelSci,

* McsEngl.model.scientific!⇒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/]

description::

· modelMath quantify the-archetype.

">overview of mathematical-models:

A mathematical model is a description of a system using mathematical concepts and language. Mathematical models are used in many fields, including physics, chemistry, biology, engineering, economics, and finance. They can be used to make predictions about the behavior of a system, to design new systems, or to understand how a system works.

There are many different types of mathematical models, but they all share some common features. A mathematical model will typically include the following:

* A set of variables that represent the state of the system.

* A set of equations that describe the relationships between the variables.

* A set of parameters that control the behavior of the model.

The equations in a mathematical model can be either algebraic or differential. Algebraic equations represent relationships between variables that are constant over time. Differential equations represent relationships between variables that change over time.

The parameters in a mathematical model are values that control the behavior of the model. For example, in a model of population growth, the parameters might include the birth rate, the death rate, and the immigration rate.

Mathematical models are developed through a process of observation, experimentation, and mathematical analysis. The first step in developing a mathematical model is to identify the variables that are relevant to the system being modeled. The next step is to develop a set of equations that describe the relationships between the variables. Finally, the model is tested to see if it can accurately predict the behavior of the system.

Mathematical models are a powerful tool that can be used to understand the world around us. They can be used to make predictions about the future, to design new systems, and to understand how systems work.

A mathematical model is a description of a system using mathematical concepts and language. Mathematical models are used in many fields, including physics, chemistry, biology, engineering, economics, and finance. They can be used to make predictions about the behavior of a system, to design new systems, or to understand how a system works.

There are many different types of mathematical models, but they all share some common features. A mathematical model will typically include the following:

A set of variables that represent the state of the system.

A set of equations that describe the relationships between the variables.

A set of parameters that control the behavior of the model.

The equations in a mathematical model can be either algebraic or differential. Algebraic equations represent relationships between variables that are constant over time. Differential equations represent relationships between variables that change over time.

The parameters in a mathematical model are values that control the behavior of the model. For example, in a model of population growth, the parameters might include the birth rate, the death rate, and the immigration rate.

Mathematical models are developed through a process of observation, experimentation, and mathematical analysis. The first step in developing a mathematical model is to identify the variables that are relevant to the system being modeled. The next step is to develop a set of equations that describe the relationships between the variables. Finally, the model is tested to see if it can accurately predict the behavior of the system.

Mathematical models are a powerful tool that can be used to understand the world around us. They can be used to make predictions about the future, to design new systems, and to understand how systems work.

Sources

br.forum-family.ru/"

[{2023-11-08 retrieved} https://bard.google.com/chat/cb814abf6debb748?hl=en&pli=1]

name::

* McsEngl.math-model!⇒modelMath,

* McsEngl.mathematical-model!⇒modelMath,

* McsEngl.modelMath,

* McsEngl.model.006-math!⇒modelMath,

* McsEngl.model.math!⇒modelMath,

* McsEngl.model.method.math!⇒modelMath,

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::

* McsEngl.modelStats,

* McsEngl.model.007-statistical!⇒modelStats,

* McsEngl.model.statistical!⇒modelStats,

* McsEngl.model.method.statistical!⇒modelStats,

* McsEngl.probabilistic-model!⇒modelStats,

* McsEngl.statistical-model!⇒modelStats,

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::

* McsEngl.all-models-are-wrong,

* McsEngl.modelStats'all-models-are-wrong,

description::

· Abstract-model is a model if it misses some attributes of the archetype.

Every generic-concept is an abstract-model of its referents.

[hmnSngo-{2014-02-27}]

name::

* McsEngl.abstract-model!⇒modelAbstract,

* McsEngl.modelAbstract,

* McsEngl.model.009-simulation!⇒modelAbstract,

* McsEngl.model.abstract!⇒modelAbstract,

* McsEngl.model.method.abstract!⇒modelAbstract,

* McsEngl.model.simulation!⇒modelAbstract,

* McsEngl.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]

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]

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::

* McsEngl.idealization!⇒modelIdealized,

* McsEngl.modelIdealized,

* McsEngl.modelAbstract.idealized!⇒modelIdealized,

* McsEngl.model.011-idealized!⇒modelIdealized,

* McsEngl.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/]

description::

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

name::

* McsEngl.abstractNo-model!⇒modelAbstractNo,

* McsEngl.modelAbstractNo,

* McsEngl.model.010-abstractNo!⇒modelAbstractNo,

* McsEngl.model.abstractNo!⇒modelAbstractNo,

* McsEngl.model.method.abstractNo!⇒modelAbstractNo,

description::

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

name::

* McsEngl.modelHuman,

* McsEngl.model.014-human!⇒modelHuman,

* McsEngl.model.creator.human!⇒modelHuman,

* McsEngl.model.human!⇒modelHuman,

description::

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

name::

* McsEngl.modelHumanNo,

* McsEngl.model.015-humanNo!⇒modelHumanNo,

* McsEngl.model.creator.humanNo!⇒modelHumanNo,

* McsEngl.model.humanNo!⇒modelHumanNo,

description::

· modelCmpr is a-model with medium a-computer.

name::

* McsEngl.computational-model!⇒modelCmpr,

* McsEngl.computer-model!⇒modelCmpr,

* McsEngl.modelCmpr,

* McsEngl.model.021-computer!⇒modelCmpr,

* McsEngl.model.medium.computer!⇒modelCmpr,

* McsEngl.model.computer!⇒modelCmpr,

description::

· anything the-model maps.

Applications of computational models

* Weather forecasting: Computational models are used to predict weather patterns.

* Climate change: Computational models are used to study the effects of climate change.

* Gene expression: Computational models are used to study how genes are expressed in cells.

* Drug discovery: Computational models are used to design new drugs.

* Financial markets: Computational models are used to study financial markets."

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

description::

· Represent the initial state of the system being studied.

description::

· Represent the relationships between the model inputs, outputs, and parameters.

name::

* McsEngl.modelCmpr'mapping-method,

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::

* McsEngl.computer-simulation-language!⇒lagSim,

* McsEngl.lagSim,

* McsEngl.modelCmpr'simulation-language!⇒lagSim,

* McsEngl.simulation-language!⇒lagSim,

description::

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

name::

* McsEngl.appSim,

* McsEngl.computer-simulation-program!⇒appSim,

* McsEngl.modelCmpr'simulation-program!⇒appSim,

* McsEngl.simulation-program!⇒appSim,

* McsEngl.simulation-software!⇒appSim,

addressWpg::

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

description::

* computer,

* computer-program,

* computer-network,

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]

"Benefits of using computational models

* Reduced cost: Computational models can be used to study systems that are too expensive or dangerous to study in the real world.

* Increased speed: Computational models can be used to study systems that are too slow to study in the real world.

* Increased control: Computational models can be used to study systems under controlled conditions.

* Increased insight: Computational models can be used to gain insights into systems that are not fully understood.

Challenges of using computational models

* Complexity: Computational models can be complex and difficult to develop.

* Validation: Computational models can be difficult to validate.

* Interpretation: The outputs of computational models can be difficult to interpret."

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

"There are also some limitations to using computational modeling, including:

* Computational models are always simplifications of the real world and may not capture all of the important features of the system being studied.

* Computational models can be sensitive to the input data and the assumptions made by the modeler.

* Computational models can be computationally expensive to run, especially for complex systems."

[{2023-11-22 retrieved} https://bard.google.com/chat/29839a7fc72e6d80?hl=en&pli=1]

description::

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

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

description::

· any doing related to modelCmpr.

name::

* McsEngl.modelCmpr'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]

description::

· The process of running the model to generate outputs.

description::

· The process of ensuring that the model's outputs are consistent with real-world observations.

description::

* McsHitp-modelConceptSensorial,

"Types of computational models

* Deterministic models: Models in which the same inputs always produce the same outputs.

* Stochastic models: Models in which the same inputs can produce different outputs.

* Agent-based models: Models in which individual agents interact with each other and with the environment.

* Continuous-time models: Models in which time is continuous.

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

description::

Applications of computational models

* Weather forecasting: Computational models are used to predict weather patterns.

* Climate change: Computational models are used to study the effects of climate change.

* Gene expression: Computational models are used to study how genes are expressed in cells.

* Drug discovery: Computational models are used to design new drugs.

* Financial markets: Computational models are used to study financial markets."

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

Here are some examples of computational models:

* Weather forecasting models: These models use mathematical equations and data from weather stations to predict future weather conditions.

* Climate change models: These models simulate the effects of human activities on the Earth's climate.

* Flight simulators: These models simulate the behavior of aircraft in different flight conditions.

* Molecular protein folding models: These models simulate the process of protein folding, which is essential for the function of cells.

* Computational engineering models: These models are used to design and test new products, such as cars and airplanes.

* Neural network models: These models are inspired by the structure of the human brain and are used to solve a variety of problems, such as image recognition and natural language processing.

description::

· a-computer-model of a-society in order to build a-better society.

name::

* McsEngl.modelCmpr.004-society,

* McsEngl.modelCmpr.society,

* McsEngl.societal-modelCmpr,

* McsEngl.society's-modelCmpr,

descriptionLong::

">create a model of a society:

Creating a model of a society involves considering various elements such as its structure, governance, economy, culture, and more. Here's a simplified framework to help you design a societal model:

1. **Basic Information:**

- **Name:** Give your society a name.

- **Location:** Specify the geographical location (e.g., on a planet, in a specific region on Earth).

- **Population:** Determine the approximate population size.

2. **Governance:**

- **Type of Government:** Choose a form of government (e.g., democracy, dictatorship, monarchy, socialism).

- **Leadership Structure:** Define how leaders are selected and the hierarchy of leadership.

3. **Economy:**

- **Economic System:** Decide on the economic system (e.g., capitalism, socialism, mixed economy).

- **Major Industries:** Identify key industries that drive the economy.

- **Currency:** Create a currency and its valuation.

4. **Social Structure:**

- **Social Classes:** Determine if there are distinct social classes and how they interact.

- **Education System:** Outline the education structure and access.

- **Healthcare System:** Design the healthcare system and its accessibility.

5. **Cultural Aspects:**

- **Language:** Specify the primary language(s) spoken.

- **Religion:** Decide if there is a dominant religion or if the society is secular.

- **Arts and Entertainment:** Identify cultural practices, art forms, and entertainment.

6. **Technology:**

- **Technological Level:** Define the overall technological advancement of the society.

- **Innovation:** Consider the society's approach to technological innovation.

7. **Legal System:**

- **Laws and Justice:** Establish the legal system, including laws and the judicial process.

- **Punishment:** Determine the consequences for breaking laws.

8. **International Relations:**

- **Diplomacy:** Describe how the society interacts with neighboring societies or countries.

- **Alliances:** Determine if the society is part of any international alliances.

9. **Environment:**

- **Natural Resources:** Identify key natural resources and their management.

- **Environmental Policies:** Consider the society's approach to environmental sustainability.

10. **Conflict and Resolution:**

- **Conflict Resolution:** Describe how internal and external conflicts are addressed.

- **Military:** Decide on the role and strength of the military.

11. **Daily Life:**

- **Housing:** Outline the types of housing available.

- **Transportation:** Describe the transportation infrastructure.

- **Communication:** Specify how information is communicated within the society.

12. **Future Development:**

- **Long-Term Goals:** Identify the society's aspirations and long-term development plans.

Remember that the above points are just a starting point, and you can expand on each aspect based on the level of detail you want to achieve. Additionally, consider the historical background and events that have shaped your society."

[{2023-11-22 retrieved} https://chat.openai.com/c/46b6d68b-2d1f-4d16-9478-7425b4dea4f6]

">create a model of a society:

Sure. Here is a model of a society:

**A Simple Model of Society**

**The System**

A society is a complex system of interacting individuals. These individuals have different needs, wants, and goals. They also have different skills and abilities. In order to meet their needs and achieve their goals, individuals must interact with each other. These interactions can be cooperative or competitive.

**The Components**

The following are some of the key components of a society:

* **Individuals:** Individuals are the basic building blocks of society. They are the ones who make decisions, take actions, and interact with each other.

* **Groups:** Groups are collections of individuals who share common interests or goals. Groups can be formal, such as families or businesses, or informal, such as friendships or clubs.

* **Organizations:** Organizations are groups of individuals who come together to achieve a common goal. Organizations can be for-profit, non-profit, or governmental.

* **Institutions:** Institutions are sets of rules or norms that govern behavior within a society. Institutions can be formal, such as laws, or informal, such as customs or traditions.

**The Dynamics**

Societies are constantly changing and evolving. This is due to a number of factors, including:

* **Individual actions:** The actions of individuals can have a ripple effect throughout society. For example, an individual's decision to get an education can lead to a better job and a higher standard of living.

* **Group interactions:** The way that groups interact with each other can also shape society. For example, if a group of people is discriminated against, it can make it difficult for them to achieve their full potential.

* **Technological change:** Technological change can also have a profound impact on society. For example, the invention of the printing press led to a revolution in education and communication.

* **Environmental factors:** Environmental factors, such as climate change and natural disasters, can also have a significant impact on society.

**The Outputs**

Societies produce a number of outputs, including:

* **Goods and services:** Societies produce goods and services that meet the needs and wants of individuals.

* **Culture:** Societies develop cultures that reflect their values and beliefs.

* **Knowledge:** Societies accumulate knowledge through research and experimentation.

* **Social capital:** Societies develop social capital, which is the trust and cooperation that exists between individuals and groups.

**Feedback Loops**

Societies are not closed systems. They are constantly interacting with their environment. This interaction can create feedback loops, which are situations in which the output of a system affects the input of the system.

For example, a society that produces a lot of pollution may find that its environment becomes less habitable. This can then lead to a decrease in the quality of life for the people in the society.

**Emergent Properties**

Societies have emergent properties, which are properties that cannot be predicted from the properties of the individual components of the system. For example, the human brain is made up of billions of neurons. However, the brain is capable of far more than simply processing information. It is also capable of consciousness, creativity, and empathy.

Emergent properties are what make societies so complex and fascinating. They are also what make it so difficult to study and understand societies.

**Conclusion**

This is just a simple model of a society. There are many other factors that could be included in a more complex model. However, this model provides a basic understanding of the key components and dynamics of societies.

I hope this helps!"

[{2023-11-22 retrieved} https://bard.google.com/chat/29839a7fc72e6d80?hl=en&pli=1]

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::

* McsEngl.DES-discrete-event-simulation!⇒modelCmprDiscrete,

* McsEngl.modelCmprDiscrete,

* McsEngl.modelCmpr.001-discrete-event!⇒modelCmprDiscrete,

* McsEngl.modelCmpr.discrete-event!⇒modelCmprDiscrete,

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::

* McsEngl.modelCmprContinuous,

* McsEngl.modelCmpr.002-continuous-event!⇒modelCmprContinuous,

* McsEngl.modelCmpr.continuous-event!⇒modelCmprContinuous,

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::

* McsEngl.WBS-web-based-simulation!⇒modelWeb,

* McsEngl.modelCmpr.003-web!⇒modelWeb,

* McsEngl.modelCmpr.web!⇒modelWeb,

* McsEngl.modelWeb,

* McsEngl.web-modelCmpr!⇒modelWeb,

description::

· Deterministic models: Models in which the same inputs always produce the same outputs.

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

description::

· Stochastic models: Models in which the same inputs can produce different outputs.

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

name::

* McsEngl.deterministicNo-modelCmpr,

* McsEngl.modelCmpr.deterministicNo,

* McsEngl.stohastic-modelCmpr,

description::

· Agent-based models: Models in which individual agents interact with each other and with the environment.

[{2023-11-22 retrieved} https://bard.google.com/chat/45c54d9a3e957ef4?hl=en&pli=1]

name::

* McsEngl.agent-based--modelCmpr,

* McsEngl.modelCmpr.agent-based,

description::

· Equation-based models: These models use mathematical equations to represent the relationships between different components of a system.

[{2023-11-22 retrieved} https://bard.google.com/chat/c0806b5e974236e2?hl=en&pli=1]

description::

· modelVisual visualizes the-archetype.

name::

* McsEngl.modelVisual,

* McsEngl.model.008-visual!⇒modelVisual,

* McsEngl.model.medium.visual!⇒modelVisual,

* McsEngl.model.visual!⇒modelVisual,

* McsEngl.visual-model!⇒modelVisual,

description::

· modelInfoBioNo is a-model with medium NOT infoBio.

name::

* McsEngl.modelInfoBioNo,

* McsEngl.model.013-infoBioNo!⇒modelInfoBioNo,

* McsEngl.model.infoBioNo!⇒modelInfoBioNo,

* McsEngl.model.medium.infoBioNo!⇒modelInfoBioNo,

description::

· modelPhysical model with medium mentalNo, not info.

name::

* McsEngl.modelPhysical,

* McsEngl.model.020-physical!⇒modelPhysical,

* McsEngl.model.medium.physical!⇒modelPhysical,

* McsEngl.model.physical!⇒modelPhysical,

description::

· modelProcess is a-model of a-process.

name::

* McsEngl.modelProcess,

* McsEngl.model.016-process!⇒modelProcess,

* McsEngl.model.archo.process!⇒modelProcess,

* McsEngl.model.process!⇒modelProcess,

description::

· modelBody is a-model of a-body.

name::

* McsEngl.modelBody,

* McsEngl.model.017-body!⇒modelBody,

* McsEngl.model.archo.body!⇒modelBody,

* McsEngl.model.body!⇒modelBody,

description::

· modelSys is a-model of a-system.

name::

* McsEngl.modelSys,

* McsEngl.model.018-system!⇒modelSys,

* McsEngl.model.archo.system!⇒modelSys,

* McsEngl.model.system!⇒modelSys,

description::

· modelSysDynamic is a-model of a-sysDynamic.

name::

* McsEngl.modelSysDynamic,

* McsEngl.model.019-sysDynamic!⇒modelSysDynamic,

* McsEngl.model.archo.sysDynamic!⇒modelSysDynamic,

* McsEngl.model.sysDynamic!⇒modelSysDynamic,

description::

· a-model of an-organism.

name::

* McsEngl.modelOgm,

* McsEngl.model.022-organism!⇒modelOgm,

* McsEngl.model.archo.organism!⇒modelOgm,

* McsEngl.model.organism!⇒modelOgm,

* McsEngl.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,

description::

· a-model of an-human-organization.

name::

* McsEngl.modelOzn,

* McsEngl.model.023-organization!⇒modelOzn,

* McsEngl.model.archo.organization!⇒modelOzn,

* McsEngl.model.organization!⇒modelOzn,

* McsEngl.organization-model!⇒modelOzn,

description::

· a-model of a-human-society.

name::

* McsEngl.modelSoc,

* McsEngl.model.024-society!⇒modelSoc,

* McsEngl.model.archo.society!⇒modelSoc,

* McsEngl.model.society!⇒modelSoc,

* McsEngl.society-model!⇒modelSoc,

description::

· a-model of a-human-economy.

name::

* McsEngl.economy-model!⇒modelEcon,

* McsEngl.modelEcon,

* McsEngl.model.025-economy!⇒modelEcon,

* McsEngl.model.archo.economy!⇒modelEcon,

* McsEngl.model.economy!⇒modelEcon,

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

page-wholepath: synagonism.net / worldviewSngo / 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 "McsLag4.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.

webpage-versions::

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