Next, we are going to take you on a path that allows us to see the way you interpret the concept of the model and the action of the modeling so that we can then introduce it to a different vision than the one you are probably used to.
- What do you understand by model? How do you interpret the modeling process? What is the purpose of modeling? What results do you expect from the built model?
Computer modeling of social and economic systems is more than three decades old. In all this time, models have been paraded in front of models that have been used to analyze countless topics such as inventory management, the performance of national economies to the optimal distribution of fire stations in New York, or the interrelation of the global population, resources, food and pollution to name a few. Certain models have become the headline of newspapers, such as The Limits to Growth (Meadows et al. 1972), generating great controversy and misinterpretations.
Computer modeling has become a major industry, generating hundreds of millions of dollars of annual revenue.
During our lives, each one of us will have faced the result of models and will have made judgments about their relevance and validity. Many people, unfortunately, cannot make these decisions in an informed and intelligent way, because for them models are like "black boxes": devices that operate in a completely mysterious way. Because computer models are poorly understood by most people, it is therefore easy for them to be misused, whether accidentally or intentionally. Thus, there have been many cases in which computer models have been used to justify a decision already made and actions previously decided.
If these bad uses stop and if the modeling becomes a more rational tool for the generality of people, they will be far from being technological elements with a special magic, and then becoming a more widely used tool.
The computer models being described are of the type used to look to the future and to conduct policy analysis.
Mental Models and Computer Models
Fortunately, everyone is familiar with the models. People use mental model models daily. Our decisions and actions are not based on the real world, but on our mental images of that world, of the relations between the parts, and of the influence that our actions have on it.
Mental models have some powerful advantages. A mental model is flexible; it can take into account a wide range of information, not just numerical data: it can adapt to new situations and can be modified as long as new information exists. Mental models are filters through which we interpret our experiences, evaluate plans, and select among possible courses of action.
But mental models also have disadvantages. They are not easily understood by third parties; their interpretations differ. The assumptions on which they are based are usually difficult to examine, and ambiguities and contradictions may go undetected, unquestioned, and unresolved.
In theory, computer models offer improvements over mental models in several ways:
- They are explicit; their assumptions are prefixed in the written documentation and open for consultation or review. They can inevitably compute the logical consequences of the assumptions made. They are understandable and are able to interrelate many factors simultaneously.
A computer model that contains these characteristics has powerful advantages over mental models. In practice, the models are far from the ideal:
- They may be so poorly documented and with such a level of complexity that no one can examine their assumptions. They are called “black boxes”. They are so complicated that the user does not trust their consistency or reality of their assumptions. They are not able to face the interrelationships and factors that are difficult to quantify, for which there are no numerical values, or that they are outside the specialty of those who build the model.
Model Types: Optimization vs. Simulation.
There are many types of models, and they can be classified in many ways. Models can be static or dynamic, mathematical or physical, stochastic or deterministic. However, a useful classification is one in which we divide the models between those that optimize and those that simulate. The distinction between optimization and simulations is particularly important because these types of models are suitable for fundamentally different purposes.
Optimization
The dictionary defines optimization as: «getting the best out of; develop to get the most. ” The result of an optimization model is the declaration of the best way to obtain some goal. Optimization models do not tell you what could happen in a certain situation. Instead they tell you what to do in order to get the best out of a situation; they are normative or descriptive models.
An optimization model takes as income the following three types of information: the goals to be achieved, the choices to be made and the restrictions to be met.
Simulation
The Latin verb simulare means to imitate or imitate. The purpose of a simulation model is to copy the real system in such a way that its behavior can be studied. The model is a laboratory replica of the real system, a microworld (Morecroft 1988).
By creating a representation of the system in the laboratory, a modeler can perform experiments that would be impossible, unethical, or prohibitive because of the costs involved in the real world. There are many very different techniques, including stochastic modeling, system dynamics, discrete simulation, and role play games.
Optimization models are prescriptive, but simulation models are descriptive. A simulation model does not calculate what can be done to achieve a particular goal, but it clarifies what can happen in a given situation. The purpose of the simulations is to predict how the systems may behave in the future under the assumed conditions or to design policies (design of new decision-making strategies or organizational structures and evaluate their effects on the behavior of the system).
In other words, simulation models are tools to see what if? Often the information obtained from a What if? it is more important than the knowledge of an optimal decision.
Modeling as Learning
Can modeling be used only to analyze tangible physical processes? Can modeling be used only by expert technicians who need to be trusted? No place for decision makers? The answer to all these questions can be a categorical NO.
Modeling as Learning is a consulting methodology to support decision-making that involves the use of analytical tools in complete connection with clients. Consultants act as facilitators of the group process. They provide the interactive approach to capture the ideas and assumptions of customers (managers or administrators) in a way that is understandable and enjoyable for applying the right analytical tools.
The resulting models give clients the ability to check the coherence of their ideas considering their consistency and consequences. These models constitute a representation of the business system with which they can experiment with the strategy to be carried out. The objective of the process is to improve understanding of appropriate business issues, focus on discussion, and generate new opinions and ideas which ultimately improve decision-making.
The key aspects of this process are:
- Modeling is an integral part of managerial discussion Facilitators (consultants) provide tools that capture and express clients' mental models Soft issues are considered (such as brand equity, morale, etc.) Clients have membership on the models. The models are run and used by the clients.
conclusion
Incrementally, it is being seen that models have a role as instruments to support strategic thinking, group discussion, and learning about management issues. Models can be seen as maps that capture and activate knowledge, models can be seen as approaches that filter and organize knowledge, and models can be seen as microworlds for experimentation, cooperation, and learning.
