Automated Conflicts Resolution and Cost Benefit Analysis

I’m looking for help to launch a political party focused on creating a forum and backing candidates who are dedicated to using the analytical outcomes generated by this platform. The forum aims to automate cost-benefit evaluations and conflict resolution for various issues.

The goal is to use a specific algorithm to correlate the strength of conclusions with the quality of supporting or opposing evidence. While the formula may appear complex, it’s essentially a structured way to present a central belief, along with reasons to agree or disagree. This algorithm will prioritize the most compelling pro and con arguments and evidence, placing them at the top of their respective columns. The forum will feature questions designed to guide users through conflict resolution, cost-benefit analysis, and organized evaluation of issues.

If executed correctly, the volume and quality of arguments should yield reliable scores. Here’s the formula for reference:

CS = ∑ (the sum of) (RtA – RtD) × (SE – WE) × LV × V × L × U × I

In this formula, CS represents the Conclusion Score, which is calculated by summing up various factors such as reasons to agree (RtA) and disagree (RtD), evidence that agrees (EA) and disagrees (ED), and other multipliers for logical validity (LV), verification (V), linkage (L), uniqueness (U), and importance (I).

Below is more detailed description of each variable:

• Conclusion Score (CS): The ultimate score, derived by contrasting reason and evidence scores and multiplying them by their respective weights for logical validity, verification, linkage, uniqueness, and importance. Higher CS values suggest that the supporting evidence currently outweighs the opposing evidence. Confidence intervals are developed based on the cumulative weighted level of stability among each branch of supporting or opposing sub-arguments, considering their importance and relevance (linkage) levels.

• RtA/ (Reason to Agree/Disagree): Represents the score of argument that support or oppose a particular conclusion.

• Logically Valid (LV): A score that represents the relative performance that the argument is logically valid, or free from logical fallacies.

• Verification Level (V): A score that represents the degree to which evidence has been verified by independent sources.

• L (Link Score): A multiplier that evaluates how closely an argument is connected to its conclusion and how much strengthening the argument would inherently bolster the conclusion. It uses the Google Page Rank Algorithm to assess the collective weight of supporting sub-arguments.

• U (Unique Score): A multiplier that minimizes the impact of repetitive arguments. It categorizes similar statements as essentially the same and ranks them based on synonym substitution and levels of agreement.

• I (Importance Score): Assesses the significance of an argument if it were true and relevant. It also considers pro/con sub-arguments that evaluate the relative importance of different claims.

• SE/WE (Supporting/Weakening Evidence): Numerical value indicating the strength of evidence supporting or weakening the conclusion.

We can break out the reason and evidence score separately, and multiply them by the relative importance factors.

• Reason Score (RS): The difference between the reason to agree score and the reason to disagree score (i.e., ∑_(n=1)^∞ (RtA – RtD) × LV × V × L × U × I). This score represents the strength of the arguments supporting or opposing a particular conclusion.

• Evidence Score (ES): The difference between the supporting and opposing evidence scores (i.e., CS = ∑_(n=1)^∞ (EA – ED) × LV × V × L × U × I). This score represents the strength of the arguments supporting or opposing a particular conclusion. Eventually, we can have a multiplication factor for each argument to determine if the

More Linkage Score Explanation

This is a weight or multiplier used to evaluate the connection between an argument and its conclusion. It also measures how much strengthening the argument would inherently bolster the conclusion. The Google Page Rank Algorithm is employed to quantify the collective weight of supporting sub-arguments. Each argument has its own independent conclusion score. However, when these arguments are used to back other conclusions, specific linkage scores are required. These scores indicate how much the argument would inherently support that other conclusion. For instance, the concept of man-made global warming has its own conclusion score. Yet, it could also have varying linkage scores when it serves to support different policies, such as a carbon tax or a cap-and-trade system. Strengthening the case for man-made global warming would naturally heighten the urgency or need for these other policies. However, their overall scores might be influenced by a cost-benefit analysis, while their importance would automatically rise in tandem with stronger evidence for man-made global warming.

More Unique Score Explanation

This is a weight or multiplier used to reduce the impact of multiple arguments that say essentially the same thing. It involves grouping similar statements, such as “Trump is an idiot” and “Trump is a moron,” as effectively equivalent. The score ranks these statements based on synonym substitution and levels of agreement, recognizing that two statements may convey the same basic idea. However, it also identifies variations in intensity (e.g., from “Trump is not smart” to “Trump is a blathering idiot”), specificity (e.g., from “Trump is not well in the head” to “Trump has borderline personality disorder”), and tone (positive or negative connotations).

More Importance Score(I) Explanation: Evaluates the significance of the argument if it were true, logically sound, and relevant to the conclusion. It assesses the degree to which strengthening an argument would necessarily impact the conclusion’s evaluation. This score is determined by the relative performance of reasons to agree and disagree with the belief that “if this argument were true, it would significantly impact the truth of the conclusion.” It also considers pro/con sub-arguments that assess the relative importance of different aspects or claims, such as “A is more important than B.” The Importance Score further considers specific comparisons between its own importance and that of other arguments and evidence given to support or weaken the conclusion.