In the last article, I have illustrated the superiority of well-managed synthetic neutrality over any singular neutrality. By processing biases in multiple news media, we can synthesize the biases into a synthetic bias that is less than each of the component biases. To do this, we have to get the singular bias of each news media first. The bias, however, is an abstract context and cannot be measured directly, so we have to measure the intensity of the factor of bias (phrased as the source of bias in the last article) to reflect that of the bias itself. For example, the measurement of a news media’s political position can reflect its biasedness in news reporting. In geometric terms, we can equate the numerical axis of bias with that of the factor of bias, measuring the latter roughly equivalent to measuring the former.

The reality is more complicated than that, as there are usually more than one factor of biases. How can we take all the factors into account when we calculate a news media’s singular bias? We may first start with a literal interpretation.

We cherish a wide range of different things in our life, but we do so unequally. The cherished things have varied importance. Practically, we assign different weights to them to reflect the priority of each of them and to decide how much of each of them to take while keeping the cost of the takings in mind. This guides our actions and strategies in our life.

Similar logic applies here as well. Some factors of bias produce more bias than others, resulting in heavier weight assigned to them in our calculation of the singular bias of a news media, which now becomes a weighed aggregate of multiple factors of bias rather than one. For example, the political position of a news media may matter more in its bias than its national background does, keeping other variables constant. Thus, we assigned greater weight to political position than to national background.

By repeating the weighed aggregation process for each of our selected media, we get their singular bias score and synthesize them to a synthetic bias. To minimize the synthetic bias, it is preferable, although not necessary, to minimize the singular bias in each of our news media.

To formulate this weighed aggregation into a more quantified model, I used a two-dimensional graph. Keep in mind that each factor of bias has its own numerical axis, onto which a news media is put according to its value in that factor of bias. Thus, in this graph, I included the axis of bias and all the axes of the factor of bias, putting them in a way that their origins (point of zero value) supersede. The direction of axes varies, and for illustrative purposes, I put the axis of bias in a horizontal direction. Now, I will name the axis of bias as axis-M and a specific axis of the source of bias as axis-A and use them as an example.

In the simplified case we discussed in the last article, axis-M and axis-A perfectly supersede each other, since we assumed that there was only one factor of bias, which thus had a weight of 100%. Here, in contrast, axis-M and axis-A cannot perfectly supersede, since one factor of bias does not singlehandedly determine the value of bias (i.e. it does not have a weight of 100%). There must be a positive angle between them (let’s focus on the angle smaller than 180°). After we get a numerical value on axis-A for a news media, we make a point on it and call it point P. Next, we make a perpendicular line to axis-M passing through point P, and the point where the line intersects with axis-A is named point Q. With the point of origin O, we now get a triangle of POQ.

In this way, the length of PO represents the value of the factor of bias, and the length of OQ, which is the projection of PO on axis-M, represents the impact of the factor of bias on the overall bias. OQ must be smaller than PO because of the positive angle between axis-M and axis-A. In mathematical terms, OQ equals PO multiplied by the cosine of the angle POQ, with the cosine necessarily less than one. The cosine here then reflects the potency of a factor of bias in its effect in determining the overall bias of a new media. We can call the cosine “the weight multiplier” of a factor of bias. In this way, how important a factor of bias is determines how we put its axis relative to the central axis of bias. The heavier the weight we assign to a source of bias, the smaller the inter-axes angle will be, and the larger the weight multiplier will be. If a factor is completely irrelevant to the overall bias of news media, the factor’s axis will be perpendicular to the axis of bias. The projection of the factor’s value on the axis of bias always equals zero.

The length of OQ is then the bias resulting from the corresponding bias factor. We can repeat the same process of projection and calculation for each of the bias factors and get their OQs as well. Their values can be negative or positive, and by summing the values together, we get the singular bias of one news media. Repeating all the processes for other news media that we get information from, we sum up their singular bias and finally get our synthetic bias. In the end, we get a bunch of axes placed together.

The model is more mathematically intensive, but I’m not arguing a quantitatively precise calculation in real-life practices. This is more a way of thinking, a way to organize the rather scattered information from naturally different categories in our daily life. Quantification makes things more certain and standardized and enables them to be evaluated in one common framework, brushing off their qualitative differences that put them into their respective alien realms. This improves the efficiency, impartiality, applicability, and universality of our deeds.

While we cannot quantify everything, a quantitative way of thinking still preserves the advantages of quantification. The discussion becomes more philosophical here, as I assumed that there was perfectly precise and true quantification of everything in an objective manner, which human beings cannot perfectly grasp but can approach by a quantitative way of thinking and repeated attempts of actually quantifying things.

The discussion of synthetic neutrality comes to be more fruitful as I elaborated on it. Much more can be addressed in its other significance and limitations as well as its real-life application. The series will go on!

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