Modeling modeling (original) (raw)
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.
Models at Work—Models in Decision Making
Science in Context, 2014
In recent years, research on modeling in both the philosophy of science and the social studies of science and technology has undergone an acute transformation. Philosophers and social scientists have begun to realize that science, in the words of Carrier and Nordmann, has increasingly shifted its focus from “epistemic or truth-oriented” research to “application-dominated” research. “Science is viewed today as an essentially practical endeavor” (Carrier and Nordmann 2011, 1) and should be considered in the context of its application. In accordance with this re-orienting of science, research on modeling has also changed. Still considering models as genuinely scientific tools, philosophers and social scientists promoted the “practice turn” that suggests a sharper focus on pragmatic issues and the performative and productive role of modeling. Application of models for the resolution of practice-related problems is viewed as an extension of science.
Procedure for model evaluation
There is a great scope of using crop simulation models in improving research understanding, improving decisions in crop management, and in improving policy and strategic planning. However, all crop models need to be calibrated, validated, and evaluated before using them for intended applications. Evaluations of models and datasets used for their calibrations and validations should be considered as an important step in any modelling activities. To perform such an important task, there is a need to develop procedures or guidelines for evaluating crop models and datasets used for model calibrations and evaluations. The paper endeavours to develop procedures or guidelines for such purpose. The procedures involve a few steps for checking model input and output files and datasets and for doing calibrations. These steps include: (1) proper description of input files such as experimental management details, weather, soil, crop performance, and files for crop species, ecotypes and genotypes, (2) entering data into appropriate files, and (3) description of output files. Once checking of input and output files is done, then the next step is running the model for calibration and validation. The procedure for evaluation of datasets and the model is illustrated with an example of wheat dataset from New Delhi, India. It is concluded that such procedures would help identify both weaknesses and strengths in datasets intended for use in calibrations and evaluations of models as well as would identify gaps in model processes which could be useful in refining or modifying the models intended for specific or general applications.
Models and Modeling Working Group
The Models and Modeling Working Group has provided participants with a setting to reflect on models and modeling perspectives to understand how students and teachers learn and reason about real life situations encountered in a mathematics and science classroom. From these perspectives, a model is considered as a conceptual system that is expressed by using external representational media, and that is used to construct, describe, or explain the behaviors of other systems. There are different types of models that students and teachers develop (explicitly) to construct, describe, or explain mathematically significant systems that they encounter in their everyday experiences, as these models are elicited through the use of model-eliciting activities (Lesh, Hoover, Hole, Kelly, & Post, 2000). During the workshop we will continue to explore these aspects of learning, teaching, and research. New directions for Models and Modeling Perspectives will be the topic of discussion and disseminati...
This chapter distinguishes two different modeling relations between vehicles and targets: design relation and representation relation. The relations are characterized by their different directions of fit. Three examples of modeling enterprises are discussed: a bioengineering model, called the "lung chip", an architectural model, called the "weekend cottage", and an engineering design model, called the "jet engine." The two modeling relations with different directions of fit are analyzed in the three examples. The lung chip is standing in a representation relation to its corresponding target and the modeling of the cottage involves a design relation to its target. The modeling of the jet engine involves both modeling relations. The first two examples also involve the other, complementary modeling relation: the protocol of the chip is standing in a design relation to the chip and the cottage model is standing in a representation relation to the design plan. With the help of these examples a basic assumption of philosophy of engineering and technology is challenged. These examples show that it is not strictly speaking true that engineering modeling is exclusively about how things should be rather than about how things are. It is shown that a representation relation is prominently involved in the first model of the lung chip. This modeling involves reasoning about how things are rather than about how things should be. So, one modeling enterprise seems to be rather about what is than about what should be. The other two examples may be seen as confirming evidence for the basic assumption. What is common to all three models is that they involve design and representation relations. 2 Keywords Models, science, engineering, direction of fit, design, representation Introduction Models are abundantly used in engineering research, in engineering design, as well as in science. In this chapter, the urge to pose a generic question about models or engineering models will be contained. It is reasonable to assume that the generic question of what models are does not allow for a simple and uniform answer (cf. Frigg and Hartmann 2012; Poznic 2017) and neither does the less generic but still ambitious question of what engineering models are. Yet, attempts to answer specific questions of how particular engineering models are used seem to fare better. For example, one can ask for which purposes particular models are deployed. A further twist to the question about models may be to focus on the practice of modeling instead of regarding models as objects, in the first instance. 1 In the following this will be the strategy of this chapter, namely to focus on the practice of modeling and to study particular instances of the uses of models as tools in different contexts of engineering. Different purposes may direct the practice of modeling. In the literature on models one finds many answers to the question about what their function is. Modeling may be practiced in order to explain (Bokulich 2017); models can be used as exploratory means (Gelfert 2016); modeling can be pursued to predict (Levins 1966); models can be used to conduct simulation studies (Winsberg 2010); they can be used as epistemic tools (Boon & Knuuttila 2009); or 4 language to the relation between vehicles of modeling and targets of modeling. 3 Pictured simply, this is about the relation between model and the thing that is to be modeled. According to this distinction, there are two relations with different direction of fit: One relation is about how the target should be according to the vehicle of modeling and the other relation is about how the target is according to the vehicle. Examples for vehicles are computational models of buildings, scale models of a bridge, mathematical models specified by equations, simulation models, etc. But also, model descriptions such as sentences in a technical language, descriptions in ordinary language, strings in programming codes and mathematical equations can be seen as vehicles among other examples. Examples for targets are the things that are modeled with the help of the vehicles, things such as buildings, bridges, human organs, jet engines, etc. The result of the application of the distinction of directions of fit is that there are two different modeling relations. With the help of this result I will show, on the one hand, what is correct about the basic assumption that engineering modeling is rather about how things should be in contrast to how things are. On the other hand, I will also show that this assumption is too simplistic, because engineering modeling is also about how things are and not only about how things should be. Beyond that, some enterprises encompass modeling relations of both sorts.
Proceedings of the Fifth Workshop on Beyond Time and Errors Novel Evaluation Methods for Visualization - BELIV '14, 2014
In this paper, we present three case studies of utility evaluations of underlying models in software systems: a user-model, technical and social models both singly and in combination, and a researchbased model for user identification. Each of the three cases used a different approach to evaluating the model and each had challenges to overcome in designing and implementing the evaluation. We describe the methods we used and challenges faced in designing the evaluation procedures, summarize the lessons learned, enumerate considerations for those undertaking such evaluations, and present directions for future work.