Organizations today have to make decisions on a daily basis, this, carried out mainly by leaders, but in order to carry out such action, it is necessary with all the information available in this regard.
The problem arises when when collecting all that information, some may not be very reliable, so then we are presented with diffuse information, which has the characteristic of being from merely subjective issues, and since it does not have numerical values it is more difficult to determine. classify and analyze.
Although, it should be emphasized that both in today's organizations and in the personal lives of each of its members, subjective issues are the daily bread, unlike information that has numerical characteristics.
That is why, in this article, you will delve into this interesting topic, where not only will the fuzzy logic that is the tool to analyze this type of information be addressed, but the necessary guidelines will be provided to obtain a database clear and reliable information that allows organizations to make correct decisions.
DIFFUSE INFORMATION
(García, Medel, Tequianez, & Carlos, 2009) They propose that in the Universe there is a large number of species or forms of life based on the ideas that have been exposed by some scientists such as Stephen Hawking, which present a variety of characteristics behavior that favor living conditions and that can also adapt to an environment throughout their temporary existence.
In this way they argue that intelligence characterizes the life forms that exist and that they are abundant in all areas of nature such as the appreciation of reality through our senses.
All this has been of great use to man in his search and desire to develop tools that make life easier and easier so that their systems are in total harmony with the environment that surrounds them.
The adaptation to these situations goes according to the evolution of science and technology, society has increasingly accelerated changes, where multiple technologies are used, such as flexible computing.
Systems that are based on fuzzy logic are a tool that uses control and digital filtering systems that allow them to be applied to intelligent systems and in different and very diverse areas of knowledge, the capacities of these types of systems are to interpret and characterize the operation of a system to be able to issue a response that is more appropriate and with a very natural language.
Origins of fuzzy systems
The first ideas of fuzzy logic are linked to the contributions of Professor Lofti Zadeh in 1965, referring to fuzzy sets, one of his objectives being to use a reasoning or soft type with different levels or degrees of operation, being patterns of reasoning similar to those of human thought unlike classical logic where more precise ideas are needed, and some concepts such as true and false are used to refer to reality; it is then when fuzzy logic manages to establish a better relationship through natural language with respect to the system with which it is interacting.
At present, diffuse systems are used as a tool to control underground transport, electronic equipment, disease diagnostic systems and some more complex industrial systems; among other systems known as "expert systems" since they have a relationship with the environment and according to this it performs actions that can affect the performance that it is desired to control in a given environment.
Decision making with fuzzy information
Decision making (DT) is a common activity in countless fields of application and research areas such as engineering, social sciences, psychology, and information technology. This range of fields of application has led to the study of problems from different approaches and points of view. (Herrera, Martínez, & RM, 2012)
However, Classical Decision Theory provides some tools and models that allow solving many decision problems, although most of these models are "deterministic" and "probabilistic" that are not easily adapted to problems that are not probabilistic in nature., although they are very common in real world situations.
To address these issues that allow dealing with this type of uncertainty, there are other approaches and tools, among which it is worth mentioning the «Fuzzy Logic» and the «Fuzzy Linguistic Approach» that make the models for decision making reliable and flexible.
Fuzzy set theory and fuzzy logic
(Badaró, Ibañez, & Agüero, 2013) As its name may indicate, it is an alternative logic to classical logic which aims to introduce a certain degree of vagueness in the things it evaluates.
In the real world, there is a lot of knowledge which is not perfect, commonly referred to as "vague", imprecise, inaccurate, ambiguous, or probabilistic in nature.
Human thought carries information of this type which it accumulates throughout its life, through experiences that are similar but not identical to the previous ones.
In another definition, we have that: "A fuzzy set is a collection of objects, which can be defined by listing its members or describing some of their distinctive characteristics." (Herrera, Martínez, & RM, 2012)
Lofti Zadeh postulated fuzzy sets as classes of objects with continuous degrees of membership. In a fuzzy set, the term "membership" is generalized, thus allowing degrees of membership.
Benefits of systems based on fuzzy logic
(Badaró, Ibañez, & Agüero, 2013) These systems respond to current technologies, since they are highly flexible, because they can be implemented in different processes, under different operating conditions and in addition to that they can be configured quickly and simply and it is possible to adapt it to the structure in which it operates.
The main benefits of fuzzy logic are described below:
- Miniaturization, since they must work in different operating environments and also with technologies that are increasingly smaller and with multiple functionalities Heterogeneous systems, where there are a large number of technologies establishing communication at different levels and degrees of operation Mobile networks, with wireless communication, work environments, access by groups and coverage areas. Intelligent systems require certain intelligence to be able to interpret their environment and thus be able to make the correct decision regarding the environment that surrounds them. Security and encryption, with protection of information, service levels and different types of access for each user of the system. Cost savings, thanks to the implementation of these systems in processes and systems, since they present savings in a short time.
Interpretation of operating levels
A fuzzy system can be considered stable when it keeps operating without any modification in a certain time, since all the variables and the responses are delimited by the restrictions of the process in which they are found.
The fuzzy system contains a base of all the response levels that the processes with which it remains in constant interaction can give, so that all possible responses are bounded in previously defined areas.
Management process of a fuzzy system
Fuzzy information management process
The process must encompass the entire organization and is a key point that is implemented by senior management, since it is in charge of raising the information needs from a strategic point of view. (Rosas, 2012)
This process encompasses the following stages:
- Determine the need for information Search for information Collection of all types of information (Fuzzy information) Use of tools to locate and obtain clear information Evaluation of information Processing of information Decision making
Each of these phases will be described below.
Determine the need for information
(Ramón, 2013) In order to determine what information is required, an internal and external analysis of the organization must first be carried out in order to know its general environment, then set out the objectives and interests of the company, it will also be necessary to take into account It takes into account the needs of customers and users, the production or operation systems, the available technology and finally the desired information can be obtained.
Information Search
(Osorio, 2013) In order to carry out this stage, it is first necessary to establish the search criteria, for which, he recommends developing a program that contains the tasks to achieve the objectives, order the activities in chronological order of execution, define which ones resources and media will be needed and finally set the execution time of the entire program.
Collection of all types of information (Fuzzy information)
(Rosas, 2012) During the completion of this process, it will be necessary to classify all the information that has been obtained during the search, to determine if the information that was obtained is diffuse, they should raise some questions:
- Is it accurate and complete? Is it duplicative or not? Is it appropriate to the needs of the organization? Is it accessible to all members of the organization? Is it secure (i.e. accessible, comprehensive and reliable?
Use of tools to locate and obtain clear information.
(Ramón, 2013) These tools arise when the information needs to be clarified and it is desired to facilitate its understanding, here are some tools for its management:
- Quantitative data analysis: It is clearer since it has numerical characteristics. Qualitative data analysis: They are more confusing due to their subjective nature, in this case, the information becomes fuzzy. Fuzzy logic: It is a procedure that provides a simple way to obtain a conclusion based on that vague information. It is a computational intelligence technique which allows processing information that has high levels of imprecision.
Information evaluation
This stage is very important since both the efficiency and effectiveness with which the organization has developed depend to a large extent on this process. (Rosas, 2012)
To carry out this stage it is necessary:
- Establish the objectives of the evaluation Distinguish the general from the particular (from the information) Apply adequate criteria to select, integrate and organize the information Give consistency to the information obtained Distinguish facts from opinions Find different points of view and compare them Identify sources of information Use reliable and valid information Feedback from the results obtained
Some reasons to carry out this evaluation are because a high command has requested it, to allow modifications, it is part of strategic planning, etc.
Information processing
(Ramón, 2013) The information processing is carried out with the objective of understanding, locating and differentiating in time and space, expressing and convincing so that a benefit is achieved to the organization and thus achieve success. Some actions to carry it out are: reflect, use diagrams, paraphrase, not copy and paste, know how to write an essay, etc.
Decision making
To be able to carry out decision-making objectively, it is first necessary to have all the information about the subject in question, since it is the basis for managers to make a decision with a general overview of the situation, the greater the the quality of the information provided will improve the quality of the decision made. (Osorio, 2013)
conclusion
As discussed in this article, diffuse information represents a series of conflicts or an obstacle for organizations today, since thanks to it it is not so easy to make decisions, since if the information were purely quantitative there are surely mathematical methods that they would allow it to be analyzed and it would surely provide the best answer to make a decision.
However, as already mentioned above, this is very difficult since both the daily life of people and within organizations, the information that exists is based on subjective issues, which causes diffuse information to be generated.
Although it is worth mentioning that if you have the ability to analyze it correctly, organize it and present it, far from being an obstacle for organizations, it is an advantage since a good database will allow more accurate decision making.
References
- Badaró, S., Ibañez, L., & Agüero, M. (2013). Expert Systems: Fundamentals, Methodologies and Applications. Science and Technology, 349-364.García, JC, Medel, J., Tequianez, A., & Carlos, SJ (2009). Systems with Fuzzy Logic. México, DF: Instituto Politécnico Nacional, Herrera, F., Martínez, L., & RM, R. (2012). Decision making with fuzzy information. ESTYLF Valladolid. Osorio, A. (May 2013). Gestiópolis. Retrieved February 2016, from http://www.gestiopolis.com/manejo-de-informacion-difusa-en-lasorganizaciones/Ramón, D. (November 2013). Gestiopolis. Retrieved February 2016, from http://www.gestiopolis.com/manejo-de-informacion-difusa-en-la-organizacion/Rosas, L. (2012). Gestiopolis. Retrieved 2016, from
