Simulation (STELLA)

1.0       Introduction

Modeling is the process of producing a model. According to Shannon, a model is a representation of an object, a system, or an idea in some form other than that of the entity itself. A model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. One purpose of a model is to enable the analyst to predict the effect of changes to the system. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. A model should not be so complex that it is impossible to understand and experiment with it. An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.

A simulation of a system is the operation of a model of the system. The model can be reconfigured and experimented with which this is impossible, too expensive or impractical to do with the system it represents. The operation of the model can be studied. Hence, properties concerning the behavior of the actual system or its subsystem can be inferred. Simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time. Simulation is used before an existing system is altered or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance.

In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study. The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently. The important point is that powerful simulation software is merely a hygiene factor. If it is absence can hurt a simulation study but if it is presented will not ensure success.

Experienced problem formulators and simulation modelers and analysts are indispensable for a successful simulation study.

            The steps involved in developing a simulation model, designing a simulation experiment, and performing simulation analysis are, identify the problem, formulate the problem, collect and process real system data, formulate and develop a model, validate the model, document model for future use, select appropriate experimental design, establish experimental conditions for runs, perform simulation runs, interpret and present results. Last but not least, is recommended further course of action. Although this is a logical ordering of steps in a simulation study, many iterations at various sub-stages may be required before the objectives of a simulation study are achieved. Not all the steps may be possible or required. On the other hand, additional steps may have to be performed.

2.0              How to design a simulation experiment?

A simulation experiment is a test or a series of tests in which meaningful changes are made to the input variables of a simulation model so that we may observe and identify the reasons for changes in the performance measures. Design of a simulation experiment involves answering the question. For example, what data need to be obtained, in what form, and how much?. Notwithstanding the facts that there are no data collection errors in the simulation, the underlying model is fully known, and replications and configurations are used

controlled, simulation results are difficult to interpret. An observation may be due to system characteristics or just a random occurrence. Normally, statistical inference can assess the significance of an observed phenomenon, but most statistical inference techniques assume independent, identically distributed data. Most types of simulation data are auto correlated, and hence, do not satisfy this assumption.

What makes a problem suitable for simulation modeling and analysis?

In general, whenever there is a need to model and analyze randomness in a system, simulation is the tool of choice. It is impossible or extremely expensive to observe certain processes in the real world. For example, next year's cancer statistics, performance of the next space shuttle, and the effect of Internet advertising on a company's sales. Besides that, problems in which mathematical model can be formulated but analytic solutions are either impossible or too complicated. It is impossible or extremely expensive to validate the mathematical model describing the system due to insufficient data.

How to select simulation software?

Although a simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms, and less expensive, most simulation studies today are implemented using a simulation package. The advantages are reduced programming requirements, natural framework for simulation modeling, conceptual guidance, the automated gathering of statistics, graphic symbolism for communication, animation, and increasingly, flexibility to change the model. Naturally, the question of how to select the best simulation software for an application arises. Metrics for evaluation include modeling flexibility, ease of use, modeling structure, code reuseability, graphic user interface, animation, dynamic business graphics, hardware and software requirements, statistical capabilities, output reports and graphical plots,customer support, and documentation.

Benefits of simulation modeling and analysis

According to practitioners, simulation modeling and analysis are one of the most frequently used operations research techniques. When used judiciously, simulation modeling and analysis makes it possible to obtain a better understanding of the system by developing a mathematical model of a system of interest, and observing the system's operation in detail over long periods of time. Test hypotheses about the system for feasibility is needed. Besides, compress time to observe certain phenomena over long periods or expand time to observe a complex phenomenon in detail. Next, study the effects of certain informational, organizational, environmental and policy changes on the operation of a system by altering the system's model. This can be done without disrupting the real system and significantly reduces the risk of experimenting with the real system. Experiment with new or unknown situations about which only weak information is available. Identify the "driving" variables which ones that performance measures are most sensitive to and the inter-relationships among them. Identify bottlenecks in the flow of or information. Use multiple performance metrics for analyzing system configurations. Employ a systems approach to problem solving. Develop a well designed and robust systems and reduce system development time.

Why teach with simulations?

Teach with simulations has involved deep learning. Instructional simulations have the potential to engage students in ‘deep learning’ that empowers understanding as opposed to ‘surface learning’ that requires only memorization. Deep learning gives the meaning that students can learn scientific methods including the importance of model building. Actually, experiments and simulations are the way scientists do their work. Science’s student should be familiar with the scientific methods which applied in science’s subjects. Using instructional simulations gives students concrete formats of what it means to think like a scientist and do scientific work. Student will learn the relationships among variables in a model or models. Simulation allows students to change parameter values and see what happens. By doing that, students develop a feel for what variables are important and the significance of magnitude changes in parameters.

The ability to match simulation results with an analytically derived conclusion is especially valuable in beginning classes, where students often struggle with sampling theory. Simulations help students understand that scientific knowledge rests on the foundation of testable hypotheses. Actively engaging in student-student or instructor-student conversations needed to conduct a simulation. Instructional simulations by their very nature cannot be passive learning. Students are active participants in selecting parameter values, anticipating outcomes, and formulating new questions to ask. Transferring knowledge to new problems and situations occur while students conducting the simulation. A well done simulation is constructed to include an extension to a new problem or new set of parameters that requires students to extend what they have learned in an earlier context.

By involve simulation in teaching and learning, it help students understanding and refining their own thought processes. A well done simulation includes a strong reflection summary that requires students to think about how and why they behaved as they did during the simulation. Seeing social processes and social interactions in action also involved while carry out simulation. This is one of the most significant outcomes of simulation in social science disciplines such as sociology and political science.

 Teaching and learning using simulation

There are some specific advantages connected with computer simulation. First, some general advantageous aspects of simulation as a form and method of learning will be indicated. Computer simulation offers the opportunity to experiment with phenomena or events, which for a number of reasons, cannot normally be experimented with in the traditional way. The simulation also provides students with experience that may be difficult or impossible to obtain in everyday life. For example, if a group students would like to do an observation or research on the interaction between prey and predator, they might have a problem due to time limitation. Execution of the real experiment is impossible because this would take a number of weeks and can therefore not be integrated as such within a lesson. So, from the computer simulation students will be able to do experiment by using the suitable computer simulation.

Computer simulation programs can be used in education to give the student more feeling for reality in some abstract fields of learning. Sometimes, simulations can be entertaining because of dramatic and game-like components. While working with a computer simulation program the student is experimenting, so he or she is playing an active rather than a passive role. This active engagement contrasts with the situation students often experience during 'face-to-face' teaching when they listen passively. Simulation creates an interactive educational setting which offers the possibility to effect changes in relation to the learning experience in a more efficient way than is normally possible with other didactic methods. It is obvious that computer simulation does not work to its intended advantage on face-to-face teaching (lecture type). Nor does it stand alone. Only when computer simulation is appropriately alternated with other didactic forms, will it render a positive result.

Working with a computer simulation program often evokes enthusiasm in the student and as such it has a positive influence on his motivation. Simulations are highly motivating student both intrinsically and extrinsically. However, no educational tool is effective for everyone. A differentiated supply of educational support tools is therefore important. A computer simulation program is one of them. Working with a computer simulation program can increase the interest of a student about a subject. This can express itself in the fact that students will often study relevant literature concerning the subject after using a simulation more than they would have done with the traditional approaches to learning. The subject is discussed more among students and special experiences are mentioned. 

The computer can be used as a didactic medium and in this form it can serve as a tool to realize a chosen educational strategy and to reach the set goals in a way that would otherwise have been impossible. But what justifies the use of computer simulation within the situation of the classroom?. Which specific didactic functions can computer simulation fulfil in education?. Often the technical possibilities and the particularly effective calculating capacity of the computer are advanced in order to justify a switch to the use of computer simulations in classrooms. The didactic functions that are possible with computer simulation are much more important. Students are then offered the possibility to experiment with the real world system, though it is simulated. Computer simulation also offers the possibility to repeat the experiment as often as necessary until the intended insight into the system has been acquired. It is also possible to do extreme things in computer simulation and to observe the results, contrary to many traditional experiments.

The use of computer simulation, however, cannot replace the practical laboratory. However, when experience with aspects of a real experiment is considered important but a practical laboratory only has a limited capacity, then working with a computer simulation program can increase the impact of practical work. As was said earlier there can be different reasons why the traditional experiment cannot be used in the educational situation, even though the experiment would be desirable because the student's insight could be positively enhanced by doing so.

Teach with simulations has involved deep learning. Instructional simulations have the potential to engage students in ‘deep learning’ that empowers understanding as opposed to ‘surface learning’ that requires only memorization. Deep learning gives the meaning that students can learn scientific methods including the importance of model building. Actually, experiments and simulations are the way scientists do their work. Science’s student should be familiar with the scientific methods which applied in science’s subjects. Using instructional simulations gives students concrete formats of what it means to think like a scientist and do scientific work. The relationships among variables in a model or models. Simulation allows students to change parameter values and see what happens. Students develop a feel for what variables are important and the significance of magnitude changes in parameters.

The ability to match simulation results with an analytically derived conclusion is especially valuable in beginning classes, where students often struggle with sampling theory. Simulations help students understand that scientific knowledge rests on the foundation of testable hypotheses. Actively engaging in student-student or instructor-student conversations needed to conduct a simulation. Instructional simulations by their very nature cannot be passive learning. Students are active participants in selecting parameter values, anticipating outcomes, and formulating new questions to ask. Transferring knowledge to new problems and situations. A well done simulation is constructed to include an extension to a new problem or new set of parameters that requires students to extend what they have learned in an earlier context.

Stella Software

Stella is one of the computer simulation software, system thinking in education and research that is simple and easily carried out for the beginner (Figure 1).  It provides us with sample of experiments that allow students to try it out. One of the sample experiments is the relationship between the prey and predator (predator-prey dynamic). The hare is the prey while Lynx is the predator. If a group of students is going to do some observation or experiment on the interaction between lynx and hare, it might take time as  they have to observe the changes in number of both species. So, with Stella software, they can observe any changes between the two species without time limitation. Besides that, if a student would like to study more on the interaction, they can just adjust the parameter which in this case, the student will manipulate the size of one time lynx harvest. The results from the manipulated parameter can be seen clearly from the graph obtained.

By using Stella, students will be able to carry out the experiment by just manipulate the parameters and they will get the results at the same time. The figures below show the different size of one time lynx harvest. The size of the lynx in one time harvest is the manipulated variable which we can adjust. Figure 2 shows the controlled variable with 0.0 sizes of one time lynx harvest, Figure 3 with 200 sizes of one time lynx harvest and the last Figure 4 show 750 size of one time lynx harvest. From these graphs obtained, students can obviously observe the changes of the graph amplitude as the number of lynx harvest increases or decreases. The changes of the graph line will trigger students to know why the graph had changed?. They will start questioning why it does change as the manipulate the parameters. This situation will make students to think and motivate them to learn more and make any prediction.

 Here’s (Figure 6), a very simple map of Hare population dynamics. From this simple map, it can explain everything that happen in the ecosystem. The box represents the number of hare in the population at any point in time. This stock accumulates the flow of births, net of the flow of deaths. Here, instead of water, we are accumulating hares as the prey in this predator-prey system. The logic shown here says that hares breed like rabbits which the larger the population, the greater the birth flow. The little circle called hare birth fraction. This circle represents the number of offspring produced on average, per hare in the population per year. Lynx eat hare. This is one component of the predator-prey interactions. The consuming of prey by predators.

The number of hares killed per lynx per year is assumed to depend on hare density. The greater the density of hares in the ecosystem, the larger the number of hares consumed per lynx per year. This linkage from hare, to density, to hares killed per lynx, to hare deaths, and back to hare again, forms a Counteracting feedback loop. An increase in the number of hare in the system propagates around the loop to lead to an increase in the hare death flow, and thus brings the number of hares back down again. From the map also show that some fraction of the lynx population dies each year. That fraction is determined by the density of the hare population in the ecosystem. A higher density of hares means fatter, happier and longer-lived lynx. On the other hand, as the population density of hares declines, a larger portion of the lynx population will die of malnutrition and starvation. All these explaination can be clearly seen from the changes of graph line’s amplitude. Rather than explaining in the words, students will be able to see it clearly the changes from the graph obtained.

As mentioned before, by using Stella or other computer simulation, there is no need to wait for a long time just to see the results from the interaction of prey and predator. As we know to study the interaction of prey-predator will take times. The limit of time can be overcome by using Stella. If a teacher is using the Stella in teaching and learning process, it does help a lot to give better understanding to students. In this case, students will be able to do prediction from the trend of graphs obtained. Students also can explore and try on the software. They can manipulate the parameters. By learning this way, is it like playing with computer games. Of course this is more interesting than learning on the traditional way. Like a new game, students will be excited to explore the software. Hence, teachers can build the motivation in students to learn. They will feel enjoy learning through the computer simulation as they can see what actually happen and why it happen clearly. If the teacher only teaches by giving lectures only, it does not mean all students will really understand what they had learnt. Teaching and learning process will be more meaningful when students get the benefits and gain knowledge.

Advantages and disadvantages of simulation

There are some advantages of computer simulation as an educational tool or for training. The apparatus necessary to be able to carry out an experiment in reality is too expensive and often this apparatus can only be operated by specialists, if it can be obtained at all. The student or trainee can now exercise as much as necessary. Owing to the practice beforehand precious time and apparatus can be put to optimum use. Besides that, the process to be investigated takes place so quickly in reality that it cannot be examined through the traditional experiment, for example, certain chemical processes. Changes in a chemical reaction should be presented at such a pace in educational situations that observation is possible. In reality those changes can hardly be noticed and they are not interesting for calculations, but only for the acquisition of insight.

Rather than that, computer simulation can overcome the problem which related to time. For example, the process to be examined can proceed too slowly in reality such as the prey-predator dynamic. So, by doing computer simulation we can carry out the observation or experiment by using computer simulation. This simulation also used to examine the complex system for traditional research, such as economic systems. If the system to be examined can be on too large or too small a scale, such as planetary movements in space and molecular movements, simulation will give us the real experiences and pictures. In some condition, there will be a situation which we cannot carry out the research as it can be dangerous to manipulate such as a nuclear reactor, a ship or a human body, simulation is the best solution.

In teaching and learning, simulation experiments can be used prior to a course for students or trainees as an introduction to a new subject or certain parts of it. Simulation often goes hand in hand with visualization. The results of the changes that a student puts into a model are directly shown on the screen. This generally applies to students. Simulation can be very purposive and for certain students very useful, such as students who need some insight before they are able to learn and understand a new concept. The student can insert those parameter values that he or she thinks will produce a result which is of interest to him. The student can devote his attention to parts that interest him. The student can skip other parts or aspects. This way he or she learns how to experiment systematically. A student can choose how he or she wants to approach a simulation experiment, how often he or she wants to repeat the experiment and to which degree he or she wants to intervene. This is same for the Stella software  that had been used to determine the interaction between the hares and lynx.

In computer simulation there are usually many ways to achieve the goals the student has set himself. If well-designed, learning how to operate a computer simulation program generally requires little effort. A short introduction by the teacher is often sufficient to enable the student to work with the program. It can be an advantage that the student perceives that not everything can be used as input. The student realizes that variable and parameters have their limits, and learns what input is reasonable for a particular variable and what input yields relevant information.

There are not only advantages connected with the use of computer simulation programs in education and training. Limitations are in some cases the result of the wrong or inappropriate use of such programs. Possible limitations of a general and educational kind such as simulation concerns the manipulation of a number of variables of a model representing a real system. However, manipulation of a single variable often means that the reality of the system as a whole can be lost. Certain systems or components of a realistic situation are not transparent. Some factors have a lot of influence on the whole, but they have indistinct relations in the whole and can therefore not be represented in a model. These factors, however, cannot be forgotten in the learning process.

A computer simulation program cannot develop the students' emotional and intuitive awareness that the use of simulations is specifically directed at establishing relations between variables in a model. So this intuition has to be developed in a different way. Furthermore, computer simulation cannot react to unexpected 'sub-goals' which the student may develop during a learning-process. These sub-goals would be brought up during a teacher-student interaction but they remain unsaid during the individual student use of a simulation. Computer simulation programs may function well from a technical point of view, but they are difficult to fit into a curriculum. Often a computer simulation program cannot be adapted to take into different student levels into account within a group or class. A computer simulation program can certainly be made to adapt to different circumstances if the designer bears that in mind. However, for many computer simulation programs this has not happened. During the experience of interaction with a computer simulation program, the student is frequently asked to solve problems in which creativity is often the decisive factor to success. The fact that this creativity is more present in some students than in others is not taken into account by the simulation. Mutual collaboration and discussion among students while using the software could be a solution for this.

Conclusion

Computer simulation is an interactive way to be applied in educational system in Malaysia. As Malaysia is towards to the technology application in school, teaching and learning process will be more interesting and meaningful. In Science subject, the computer simulation will make the learning process more interesting and easier to be carry out activities. It also will save time for teachers to teach students according to the syllabus.  However, to integrate simulation in teaching and learning process, it is not that easy. Some improvement should be taken and considered before applying it. We should not forget the traditional way learning process to develop soft skills which cannot get from the computer simulation. We must bear in mind that nothing is perfect. Everything will have positive side and negative side.