Ecological complexity, fuzzy logic, and holism in indigenous knowledge

Berkes, F.

Berkes, M. K.




Some indigenous knowledge is said to be holistic in the way it deals with the environment. Given the difficulties of Western science with complex environmental problems, any insights from the holism of indigenous knowledge are of major interest. Based on examples from Inuit and other northern peoples, it appears that indigenous knowledge approaches complex systems by using simple prescriptions consistent with fuzzy logic. Specifically, indigenous knowledge pursues holism through the continued reading of the environment, collection of large amounts of information, and the construction of collective mental models that can adjust to new information. Such an approach serves the assessment of a large number of variables qualitatively, as opposed to focusing on a small number of variables quantitatively.

(Berkes & Berkes, 2009)


Development of a supplier selection model using fuzzy logic

Ordoobadi, S. M.



Supply Chain Management

Purpose: This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier. Design/methodology/approach: Companies often depend on their suppliers to meet customers’ demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker’s preferences on the importance of these criteria as inputs. Findings: Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker’s preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management. Practical implications: The procedure proposed here can help companies to identify the best supplier. Originality/value: The paper describes a decision model that incorporates decision maker’s subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

(Ordoobadi, 2009)