This work presents a data‐fusion mathematical object that incorporates the optimism level of a decision‐making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA‐experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA‐expertons. Finally, the paper ends with an application in business decision‐making regarding the calculation of expected benefits.

This article provides a tool for determining, on the basis of experts’ sales forecasts, the extent to which a company's sales forecasts can be regarded as a particular value. To this end, different alternatives for assigning triangular fuzzy numbers are presented whose vertices are obtained by means of OWA-type aggregation functions. Said functions act on the experts’ sales forecasts, eliminating or softening outliers in line with the personal opinion of the entrepreneur or business owner, thus making it possible to include in the forecasts company-specific knowledge unknown to the experts.

Purpose: The aim of this study is to show in detail the theoretical and practical foundations of a new feasibility technique for cash flow forecasting (CFF) based on triangular fuzzy numbers.Design/methodology/approach: One of the most complicated problems business people face is determining if they have enough cash to be able to meet all future payments of a specific period. The uncertainty of forecasting the data to solve the problem suggests that a model based on fuzzy logic tools may provide a good way to obtain new techniques to ensure the feasibility of cash flow management.Findings: This study shows how a specific company can obtain a quantitative idea of the risk of not being able to meet payments in a specific period. This idea can be put into practice with the usual computer tools.Research limitations/implications: This work presents a technique to predict the feasibility of CFF using triangular fuzzy numbers. There are other fuzzy numbers with which we can model the study problem and that offer certain advantages over to triangular ones.Practical implications: A qualitative procedure is currently used to calculate the feasibility of a CFF. This work represents a step forward since it shows how to model quantitative feasibility.Originality/value: The originality and value of this contribution consists of providing a complete model for a feasibility technique of CFF, as well as several proposals to mechanize the calculations and make the results more intuitive by means of spreadsheet graphs.

The aim of this article is to present in a formal way the concept of the experton, which is commonly used in practice, and use this formalization to j...

This paper suggests the possibility of incorporating the methodology of fuzzy logic theory into Harrod’s economic growth model, a classic model of economic dynamics for studying the growth of a developing economy based on the assumption that an economy with only savings and investment income is in equilibrium when savings are equal to investment. This model was the first precursor to exogenous growth models, which in turn gave rise to endogenous growth models. This article therefore represents a first step towards introducing fuzzy logic into economic growth models. The study concerned considers consumption and savings to depend on income by means of uncertain factors, and investment to depend on the variation of income through the accelerator factor, which we consider uncertain. These conditions are used to determine the equilibrium growth rate of income and investment, as well as the uncertain values for these variables in terms of fuzzy numbers. As a result, the new model is shown to expand the classical model by incorporating uncertainty into its variables.