Entropy Weight Method: Objective Evaluation and Decision-Making Algorithms

In this article, we explore objective evaluation and decision-making algorithms along with their applications in modern technology. Objective evaluation algorithms are computer programs designed to assess the quality or value of certain entities without being influenced by subjective factors. These algorithms can be applied across various domains such as finance, healthcare, and e-commerce. Typical implementations involve mathematical models like the entropy weight method, which calculates indicator weights based on information entropy to measure data dispersion - higher entropy indicates more information content and greater weight assignment. The core calculation involves normalizing decision matrices and computing entropy values using logarithmic functions.

On the other hand, decision-making algorithms are computer programs that make determinations based on predefined rules or parameters. These algorithms find extensive applications in automated and intelligent decision-making systems, including traffic control, energy management, and financial risk management. Implementation often involves multi-criteria decision analysis (MCDA) techniques where algorithms process normalized data through weighted sum models or TOPSIS methods. Key functions typically include data normalization, weight calculation, and score aggregation, frequently implemented using matrix operations in programming languages like Python or MATLAB.

We will provide detailed explanations of these algorithms' operational principles and applications to foster deeper understanding of their significance in contemporary technology. Code examples may demonstrate how entropy values are computed using probability distributions and how decision rules are programmed using conditional statements and optimization functions.