Proceedings of the 2003 Winter Simulation Conference S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, eds. EXPERIENCING NATURAL PHENOMENA WITH VIRTUAL, CONSTRUCTEDAND MATHEMATICAL MODELS Stephan Diehl Carsten Görg FR 6.2 Informatik Saarland University 66041 Saarbrücken, GERMANY ABSTRACT In this paper we discuss how different kinds of models can be combined in an educational setting to enable stu- dents to experience natural phenomena, here earthquakes. Different tools enable students to manipulate virtual mod- els, construct physical models and formulate mathematical models. Ideally, the modelling processes and the resulting models complement each other to some degree. Virtual and physical models can then be driven by real data as well as mathematical models of the phenomena. 1 INTRODUCTION The ideas presented in this paper are under development in a current research project (COLDEX, EU-FP5 Project IST 2001-32327, http://www.coldex.info) with the goal to develop and use new computational tools to fos- ter scientific experimentation, modelling and simulation in distributed collaborative settings. It focuses on visual and other perceptual phenomena, including astronomical and seismic measurements. In this paper we consider earthquakes as an example of natural phenomena. Earthquakes are caused at a spot deep below the surface, which we call its hypocenter. The point above the hypocenter on the earth surface is called the epicenter. With the help of seismographs the waves of an earthquake are recorded at different measuring sites. In particular, seismographs record two kinds of waves. Both kinds of waves propagate straight from the hypocenter to the measuring site. Primary waves create a back and forth movement of the rocks through which they travel, while secondary waves produce a side to side motion. 2 MODELS AND SIMULATION The term model is used in various ways in computer science and science in general. Etymologically, model means small measure (from the Latin modus). This gives rise to a first characterization of models. Models are not small scale copies of reality, but reduced in the sense that they allow to measure less properties. As Noam Chomsky said in an interview (Chomsky and Cockburn 1994): “If you study the planets, for example, it helps to think of them as points which have mass and move in elliptical trajectories around other points. Of course, the planets are not points – a point has no dimensions – but if you treat them as such, you can predict and understand the solar system more clearly. That is a model.” So by using models we abstract from or simplify reality to be able to predict and understand natural phenomena. In a more general way Cellier defines models and their role for simulation (Cellier 1991). “A system is a potential source of data. An experiment is the process of extracting data from a system by exerting it through its inputs. A model for a system and an experiment is anything to which an experiment can be applied in order to answer questions about a system. A simulation is an experiment performed on a model.” The descriptions above are all about the purpose of models, but not about their representation or the medium they are encoded in. We will look at three different represen- tations in the rest of this paper: virtual (here: 3D graphics), physical and mathematical (in the form of equations or formulae). Note, that this is different from sets of models as for example in UML or a multimodel (Ören 1991) which is a network or hierarchy of models. There models represent systems at different levels of abstraction or granularity. This is not necessarily the case for the models we are looking at, although, typically, the different media lend themselves to different aspects and different levels of abstraction for the same system to be modelled. In our educational settings modelling processes and not ready made models are crucial for active learning. Thus we will also focus on the modelling processes here. 778