The 20th century was characterized by the development of space exploration and computer technology that was focused around the human creativity, the immediacy in which things happen and the recognition that we can’t control everything (Duhl, 1983). The recognition that we can’t control all, encouraged attempts to formulate thinking models such as the fuzzy logic model which was developed by Zadeh (1965).
In the mid-60s Zadeh suggested the use of the term “fuzzy logic” to describe life situations that are complex and vague. He believed that the world is dominated by concepts that do not have sharp and well defined settings, and that the available information, in most cases, is partial and sometimes not reliable. In his opinion the scientists, by their search for accuracy, try to converse the real world to mathematical artificial models, which leave no room for ranking that expresses non-precision or partial precision. Zadeh believes that this attempts are futile and arise from the difficulty of humans to deal with many and different ideas at the same time. Therefore people are classifying and connecting similar themes to reduce the uncertainty and complexity of processing data.
Fuzzy logic is not a fuzzy way of thinking. This is a planned and organized type of thinking that refers to fuzzy and dim modes of life. An example of a common situation can illustrate the idea: when we try to park a car between two cars, we estimate the distance between the cars and park the car only if we see reasonable and appropriate distance. This case points to the “rough” design with respect to the space and approximate distance and not to a completely accurate measurement.
Fuzzy logic refers to life situations as partial truth. According to this view truth, age, beauty, wealth, color, race, etc., are affected by the dynamic nature of human perception and behavior, and are rated on a scale of values. Hence, the question is not whether something is true or not, but how much it is true or not.
According to Zadeh’s (1973) perspective there is a paradox in an attempt to achieve accuracy. Such an experience makes, in his opinion, the understanding of complex systems excessively complex and difficult. Therefore, he concluded that there is no need for comprehensive and detailed information to reach understanding of system operation. In his view, quite limited and representative information is enough for a reasonable analysis.
Zadeh realized that increased accuracy is far from being helpful and even constitutes an obstacle. In addition, he noted that verbal ambiguity provides flexibility to create groups of models which enable to capture the essence of the existing problem, while allowing for comparisons and analogies to similar situations and systems.
Based on this concept, it is possible to assume that most of the principles and foundations of the focused problem-solving brief therapy are based on this model of thinking. The brief-therapy approach assumes that limited information, sometimes partial and flexible, might be sufficient for a successful therapeutic strategic intervention. The focused problem-solving brief therapy, in order to achieve a therapeutic change, is not relying on full or accurate information (“absolute truth”) but utilizes four main components: (1) what is the difficulty; (2) how the customer tried to solve the difficulty; (3) what is the desired change for the customer; and (4) the suggested solution proposed by the therapist.
This four components show the possible direction and way for achieving the therapeutic change.