There is talk about the existence of a dichotomy between the theory of "rigid" (hard) systems and the theory of "flexible" (soft) systems. "Rigid" systems are typically those found in the physical sciences and to which it can be applied the traditional techniques of the scientific method and the science paradigm satisfactorily.
When the typical properties of "rigid" and "flexible" systems are compared, it is not surprising to find that the methods of science that can be applied to the former may not be entirely appropriate to the latter. Generally, "rigid" systems will admit formal reasoning processes, that is, logical-mathematical derivations. Proven data, as presented in those domains, is generally replicable, and explanations can be based on proven causal relationships. Very often the tests are accurate and the predictions can be made with a relatively high degree of confidence.
Hard systems are identified as those in which men and machines interact. In which the technological part is given greater importance in contrast to the social part. The social component of these systems is considered coma if the performance or behavior of the individual or social group were only a generator of statistics.
That is, human behavior is considered taking only its statistical description and not its explanation. In hard systems, it is created and acts as if the problems only consist of choosing the best means, the optimum, to reduce the difference between a state that is desired to be reached and the current state of the situation. This difference defines the need to satisfy the objective, eliminating or reducing it. It is believed that this end is clear and easily definable and that the problems have an easily identifiable structure.
Hard system characteristics
Systems Basics are a great way to analyze and treat both hard and soft systems. Now we will see how some concepts behave when applied to the treatment of a hard system (SD).
- Objectives Performance Measures Monitoring and Control
- The decision-making process is a process whose decision variables are measurable, quantitative and easy to determine When the future states of what may happen are clearly identifiable When the allocation of system resources to the areas that request it are easy and expeditious.
In general, systems allow formal reasoning processes in which logical-mathematical derivations play a very important role. In this way we can see that the experiments carried out in these systems are repeatable and the information and evidence obtained from them can be tested each time the experiment is carried out, thus having CAUSE - EFFECT relationships. Finally, and due to this type of CAUSE - EFFECT relationships, the forecasts or predictions of the expected future of the system under certain specific conditions are quite accurate and / or safe.
goals
Hard systems when studied, observed and analyzed have properties that do not lend themselves to interpretations of different meaning depending on the type of preparation and knowledge that the person carrying out the study has.
This is a characteristic of great weight in determining the degree of "HARDNESS" or "SOFTNESS" of a given system, since, even when the system is analyzed by an interdisciplinary team of people, the conclusions, comments and considerations of each equipment item as well as equipment as a whole must not differ significantly from each other.
The objectivity of hard systems also provides great advantages for the application of quantitative techniques that require variables that are easy to identify and that represent the characteristic of the system under consideration.
Mathematical Models
Another characteristic that has been found in the treatment of Hard Systems is the relative simplicity with which their operations, characteristics, relationships and objectives can be expressed in mathematical terms.
This situation is very useful for the engineer or analyst since, the construction of a mathematical model of the system does not present major difficulties that prevent the use of the model to optimize it or to simply simulate different policies or courses of action and observe the behavior of the system. modeled system without the need for expensive and sometimes dangerous experiments with the real system.
