Original scientific paper

https://doi.org/10.7906/indecs.23.4.1

Quantification of Everything Method in Economics

Constantinos Challoumis ; National and Kapodistrian University of Athens, Athens, Greece *

* Corresponding author.

Abstract

This article is dedicated to the Quantification of Everything method in the field of economics, but it could be used in any scientific discipline by converting theoretical frameworks into positive and quantitative forms. Hinged on the concept of the axiomatics method, the Quantification of Everything method enables researchers to systematically extract conclusions in research. This approach has been applied multiple times in studies that scrutinize the mathematical foundations of theories, aiming to align initial hypotheses with final results. Fuzzy logic, as outlined in the theory, plays an important role in the application of the Quantification of Everything method. The methodology consists of three steps. The first step involves formulating the hypothesis of the model. The second step applies fuzzy logic via a generator, which serves as the foundation of the process by using the principles of fuzzy logic to extract results. The third step is the final stage, where the scientist evaluates the outcomes to decide whether to modify the model, leverage a feedback loop, or terminate the process if the results align with the initial hypothesis. The Quantification of Everything method represents a blend of mathematics, methodological modeling, and behavioral approaches, with decisions guided by the scientist. A key advantage of this method is its versatility, allowing it to be applied in every field of science, transforming theoretical concepts into mathematical equations. This article highlights the application of the Quantification of Everything method with a paradigm of a simulation process and the use of a feedback mechanism. The results confirm that through the feedback process and the iterations performed using the generator, the equations of the model and their behavior were determined and represented graphically. This study quantified qualitative data and validated the model’s behavior through iterative feedback. The feedback loop allowed for continuous checking and refinement of results against the initial hypothetical equations, ensuring their consistency with the underlying theoretical framework. The main contribution of this article is that it enables any scientific field to transition from a purely theoretical framework to one that incorporates mathematical analysis.

Keywords

Q.E. method; behavioral economics; fuzzy logic; quantification method

Hrčak ID:

334797

URI

https://hrcak.srce.hr/334797

Publication date:

30.8.2025.

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