What are simulation models?
The article will talk about simulation models. This is a rather complex topic that requires separate consideration. That is why we will try to explain this question in an accessible language.
What is it about? To begin with, simulation models are necessary to reproduce any characteristics of a complex system in which elements interact. Moreover, such modeling has a number of features.
First, it is an object of modeling, which most often represents a complex complex system. Secondly, these are random factors that are always present and have a definite influence on the system. Thirdly, it is the need to describe a complex and lengthy process that is observed as a result of modeling. The fourth factor is that without the use of computer technology to get the desired results is impossible.
Development of a simulation model
It lies in the fact that each object has a certain set of its characteristics. All of them are stored on a computer using special tables.The interaction of values and indicators is always described using an algorithm.
The peculiarity and beauty of modeling is that each stage is gradual and smooth, which makes it possible to change characteristics and parameters step by step and get different results. The program, which involves simulation models, displays information about the results obtained, based on certain changes. Often, a graphic or animated representation of them is used, greatly simplifying the perception and understanding of many complex processes, which are quite difficult to understand in an algorithmic form.
Imitation mathematical models are based on the fact that they copy the quality and characteristics of some real systems. Consider an example when it is necessary to investigate the number and dynamics of the number of certain organisms. To do this, using simulation, you can separately consider each organism in order to analyze specifically its indicators. In this case, the conditions are most often set verbally. For example, after a certain period of time, you can set the reproduction of the body, and after a longer period - his death. The fulfillment of all these conditions is possible in the simulation model.
Very often, examples of modeling the movement of gas molecules are given, because it is known that they move erratically. You can study the interaction of molecules with the walls of the vessel or with each other and describe the results in the form of an algorithm. This will allow one to obtain averaged characteristics of the entire system and to perform an analysis. It should be understood that such a computer experiment, in fact, can be called real, since all characteristics are modeled very accurately. But what is the meaning of this process?
The fact is that the simulation model allows you to select specific and pure characteristics and indicators. She seems to be getting rid of random, unnecessary, and a number of other factors, about which researchers may not even guess. Note that very often determination and mathematical modeling are similar if, as a result, an autonomous strategy of actions should not be created. The examples that we have discussed above relate to deterministic systems. They differ in that they have no elements of probability.
The name is very simple to understand if you draw a parallel from ordinary life.For example, when you are standing in a queue at a store that closes after 5 minutes, and wondering if you have time to buy the goods. Also, the manifestation of chance can be noticed when you call someone and count the beeps, thinking with what probability you will get through. Perhaps, someone will find this surprising, but it was thanks to such simple examples that the newest branch of mathematics, namely, queuing theory, was born at the beginning of the last century. She uses statistics and probability theory to draw some conclusions. Later, researchers proved that this theory is very closely connected with military affairs, economics, production, ecology, biology, etc.
Monte Carlo method
An important method for solving a self-service problem is a statistical test method or a Monte Carlo method. Note that the possibilities of analyzing random processes analytically are quite complicated, and the Monte Carlo method is very simple and universal, which is its main feature. We can consider an example of a store in which one or more customers enter, the arrival of patients at a trauma center for one or a whole crowd, etc.At the same time, we understand that all these are random processes, and the intervals between some actions are independent events that are distributed according to the laws, which can be derived only by conducting a huge number of observations. Sometimes this is not possible, so the average is taken. But what is the purpose of modeling random processes?
The fact is that it allows you to get answers to many questions. It is trivial to calculate how much a person will have to stand in a queue when considering all the circumstances. It would seem that this is a fairly simple example, but this is only the first level, and there can be many such situations. Sometimes timing is very important.
You can also ask a question about how you can allocate time while waiting for service. An even more difficult question concerns how the parameters should relate so that the queue never reaches the newly-entered customer. It seems that this is a rather easy question, but if you think about it and start at least a little more complicated, it becomes clear that the answer is not so easy.
How does random modeling happen? Mathematical formulas are used, namely the laws of the distribution of random variables. Numeric constants are also used.Note that in this case it is not necessary to resort to any equations that are used in analytical methods. In this case, just an imitation of the same queue, which we talked about above, takes place. Only first, programs are used that can generate random numbers and relate them to a given distribution law. After that, a volume, statistical processing of the obtained values is carried out, which analyzes the data on the subject, whether they correspond to the initial purpose of the simulation. Continuing further, let us say that it is possible to find the optimal number of people who will work in the store so that the queue never arises. In this case, the used mathematical apparatus in this case is the methods of mathematical statistics.
Little attention is paid to the analysis of simulation models in schools. Unfortunately, this can affect the future quite seriously. Children should know some basic principles of modeling from school, since the development of the modern world is impossible without this process. In a basic computer science course, children can easily use the Life simulation model.
More thorough study can be taught in high school or in specialized schools. First of all, we must study the simulation modeling of random processes. Remember that in Russian schools such a concept and methods are just beginning to be introduced, so it is very important to keep the level of education of teachers, who will face a hundred percent guarantee from a number of questions from children. We will not complicate the task, focusing on the fact that this is an elementary introduction to this topic, which can be considered in detail in 2 hours.
After the children have learned the theoretical basis, it is worth highlighting the technical issues that relate to generating a sequence of random numbers on a computer. At the same time, it is not necessary to load children with information on how the computer works and on what principles the analyst is built. From practical skills they need to be taught to create generators of uniform random numbers on a segment or random numbers according to the law of distribution.
Let's talk a little about why imitational models of control are needed. The fact is that in the modern world it is almost impossible to do without modeling in any sphere.Why is it so popular and popular? Modeling can replace real events that are needed to produce specific results, the creation and analysis of which are too expensive. Or it may be the case when conducting real experiments is prohibited. Also, people use it when it is simply impossible to build an analytical model due to a number of random factors, consequences, and causal relationships. The last case, when this method is used, is when it is necessary to simulate the behavior of any system during a given period of time. For all this, simulators are created that try to reproduce as much as possible the qualities of the original system.
Simulation research models can be of several types. So, consider the approaches of simulation. The first is the system dynamics, which is expressed in the fact that there are interconnected variables, certain drives and feedback. Thus, two systems are most often considered, in which there are some common characteristics and intersection points. The next type of simulation is discrete-event.It applies to those cases where there are certain processes and resources, as well as a sequence of actions. Most often in this way they investigate the possibility of an event through the prism of a number of possible or random factors. The third type of modeling is agent. It lies in the fact that the studied individual properties of the organism in their system. At the same time, an indirect or direct interaction of the observed object and others is necessary.
Discrete-event modeling offers to abstract from the continuity of events and consider only the main points. Thus, random and unnecessary factors are excluded. This method is the most developed, and it is used in a variety of areas: from logistics to production systems. That he is best suited for modeling production processes. By the way, it was created in the 1960s by Jeffrey Gordon. System dynamics is a modeling paradigm where research requires a graphic representation of the connections and mutual influences of some parameters on others. This takes into account the time factor. Only on the basis of all the data a global model is created on the computer.It is this type that allows one to very deeply understand the essence of the event being studied and to reveal some reasons and connections. Thanks to this modeling, business strategies, production models, development of diseases, city planning and so on are built. This method was invented in the 1950s by Forrester.
Agent modeling appeared in the 1990s, it is relatively new. This direction is used to analyze decentralized systems, the dynamics of which in this case is determined not by generally accepted laws and rules, but by the individual activity of certain elements. The essence of this simulation is to get an idea of the new rules, in general, to characterize the system and find the connection between the individual components. At the same time, an element is studied that is active and autonomous, can make decisions independently and interact with its environment, as well as independently change, which is very important.
Now consider the main stages of the development of a simulation model. They include its formulation at the very beginning of the process, the construction of a conceptual model, the choice of a modeling method, the choice of a modeling apparatus, planning, and the execution of a task.At the last stage, all the data obtained is analyzed and processed. Building a simulation model is a complex and lengthy process that requires a lot of attention and understanding of the essence of the matter. Note that the steps themselves take a maximum of time, and the modeling process on a computer does not take more than a few minutes. It is very important to use the right models of simulation, because without this it will not be possible to achieve the desired results. Some data will be obtained, but they will not be realistic and not productive.
Summing up the article, I would like to say that this is a very important and modern industry. We looked at examples of simulation models to understand the importance of all these points. In the modern world, modeling plays a huge role, since it is the basis for the development of economics, city planning, manufacturing, and so on. It is important to understand that models of simulation systems are in great demand, since they are incredibly profitable and convenient. Even with the creation of real conditions, it is not always possible to obtain reliable results, since there are always influenced by many scholastic factors that are simply impossible to take into account.