# MEDIA Scoring Methodology
### ***Computational Logic***
The Diagram is an illustration of the bottom-up computational logic of the MEDIA score:
1. The first step is to transform and normalize the variables using a normalized tunable sigmoid function. This function ensures that the values of the variables are mapped to a standardized range, allowing for consistent comparison and analysis.
2. For each of the five dimensions (Monetary, Engagement, Diversity, Identity, and Age), a subscore is computed. This is achieved by taking a weighted sum of all the variables within that dimension. Each variable is assigned a weight that reflects its relative importance in determining the overall score for that dimension.
3. After calculating the sub-scores for each dimension, they are scaled to a range of 0 to 100. This scaling process standardizes the sub-scores, making them easier to interpret and compare across different dimensions.
4. Finally, the MEDIA score is calculated by taking a weighted sum of all the sub-scores. Each sub-score is multiplied by its respective weight, reflecting its significance in contributing to the overall value assessment.
### ***Sigmoid Transformation***
The Sigmoid function， represented by the equation $$y = \frac1{1+e^{-x}}$$ , is a non-linear S-shaped transformation function. As the input values x increase, the output y gradually transitions from 0 to 1. This gradual transition allows the sigmoid function to capture non-linear relationships. In the normalized tunable sigmoid function used in the MEDIA scoring system, there is a parameter that controls the pace or speed at which y transitions from 0 to 1 as x increases. This parameter allows for fine-tuning the behavior of the sigmoid function to match the desired range and sensitivity of the scoring system.
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