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Energy, 1990
This section addresses the important issue of how business managers and government planners can gain confidence in using engineering-economic models as part of the decision making and planning process. Purposes and methods of model evaluation are discussed from the viewpoint of the modeling methodologies employed and the nature of the model validation procedures applied to them.
Sherwin sorted out how to select models over a cup of coffee in the Modelling and Simulation Lab at the University of Otago School of Pharmacy in Dunedin, NZ (12 Nov 2008) Model selection typically involves an initial Discrimination step using goodness of fit (e.g. objective function value) to find a candidate model for evaluation. Evaluation may use a diagnostic process (e.g. VPC) for learning about the model weaknesses ("all models are wrong") which may suggest how to improve the model then an acceptance process (e.g. NPDE) for confirming a model ("some models are useful"). Slide 3 Model Selection Mentre F, Escolano S. Prediction discrepancies for the evaluation of nonlinear mixed-effects models. J Pharmacokinet Pharmacodyn. 2006 Jun;33(3):345-67.
1998
Mathematical modeling is a technique used to gain control of complexity in real life. In science, mathematical models are often descriptive (so-called “laws of nature”, such as Newton’s gravitational law, are examples), but in management, models are just as often prescriptive, aiding a decision maker by pointing toward the “best” course of action. In this course we will concentrate on prescriptive models. The term optimization means selecting the best course of action from among many alternatives. A mathematical model is a description of a real-world situation or problem using the language of mathematics. Often, the grubby details of the real-life situation are abstracted away, so many mathematical models appear to be simple, elegant, and unrealistic. It turns out that even these models can be complex and difficult to solve, but they can also be rewarding in that their solutions can be applied back to the real-world situation from which they arose. It is the modeler’s responsibility...
Model-model evaluasi pendidikan
INSANIA : Jurnal Pemikiran Alternatif Kependidikan, 1970
On learning literature, there many kind of learning model design, for example model developed by Winarno Surakhmad, Winkel, Hisyam Zaini et al., Briggs and Wager, Gerlach and Ely, and Kemp. Those models design have component and pattern that different each other, from model that have dominant quantitative measure like measurement model and model that using qualitative approach as illuminative model. By studying many models and broaden view not only to one model approach, and even combine (merger) between two or more models, or even developing our specific model. As one basic rule, good evaluation have to comply with several principle, namely validity, reliability, objectivity, continuity, and comprehensive so the resulted information can became source to make right and wise decision. On learning literature, there many kind of learning model design, for example model developed by Winarno Surakhmad, Winkel, Hisyam Zaini et al., Briggs and Wager, Gerlach and Ely, and Kemp. Those mo...
Theoretical Framework for Defining Validity and Quality in Modeling
2003
The increasing number of Object Oriented databases and knowledge bases raises the need for some criteria of model validity, modeling guidelines, and quality criteria to be used in modeling. Although some researchers have developed some specialized sets of modeling guidelines, we found no general-purpose framework to evaluate these proposals and customize them. In this paper we develop a theoretical framework for characterizing modeling. Modeling is seen as a transformation process. These transformations must be valid, and must increase the quality of the model. The framework is centered around these two issues of validity and quality. 1. KNOWLEDGE MODELING Modeling is a central activity in databases. It consists of defining a set of constructs (relations, classes, constraints) that, together, capture the contents of interest and exhibit model-specific structural quality (normal forms, schema invariants). Software design is also a modeling activity. According to Jocabson et al. [14],...
Assessment of the adequacy of mathematical models
Agricultural Systems, 2006
Models are mathematical representations of mechanisms that govern natural phenomena that are not fully recognized, controlled, or understood. They have become indispensable tools via decision support systems for policy makers and researchers to provide ways to express the scientific knowledge. Model usefulness has to be assessed through its sustainability for a particular purpose. Adequate statistical analysis is an indispensable step during development, evaluation, and revision phases of a model. Therefore, in this paper we discussed and compared several techniques to evaluate mathematical models designed for predictive purposes. The identification and acceptance of wrongness of a model is an important step towards the development of more reliable and accurate models. The assessment of the adequacy of models is only possible through the combination of several statistical analyses and proper investigation regarding the purposes for which the mathematical model was initially conceptualized and developed for. The use of only a few techniques may be misleading in selecting the appropriate model in a given scenario.
Measurement of modeling abilities
This paper discusses the difficulties of measuring modeling abilities within examina- tions. Modeling abilities are inherently difficult to measure since they imply cognitive processes that may not become evident in the result of a written examination. In ad- dition, for a given problem there exists a wide variety of valid models that may just differ in the employed modeling language, technique, or paradigm. The models may just differ with respect to the aspects of the problem that are covered. Or the models may differ in the level of abstraction that has been chosen, e.g. UML level or code level. Even for a given modeling language and for clearly identified aspects that are to be covered and for a given level of abstraction there are still many possible solutions for a given problem that are difficult to compare and where it is difficult to judge their relative quality. This paper will mainly raise questions related to these problems. Ho- wever, in addition we will describe a speci...
Evaluation framework for the design of an engineering model
Ai Edam Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2010
According to both cybernetics and general system theory, a subject develops and uses an adequate model of a system to widen his/her knowledge about the system. Models are then the interface between a subject and a real-world system to solve problem and to construct knowledge. Hence, evaluating these models is crucial to ensure the quality of the constructed knowledge. We propose here an evaluation framework to assess complex models based on the intrinsic properties of these models as well as the properties of the derived knowledge. A series of 40 evaluation criteria are proposed under the four systemic axes: ontology, functioning, evolution and teleology. Through a case study, we show how our evaluation model allows both presenting a given model and assessing it.