Quantifying Reason: The Power of Truth Scores

Welcome to a revolutionary concept in argument evaluation - the direct integration of the scores for Logical Fallacy and the Evidence Verification. Our approach quantifies the strength of an argument by scrutinizing its logical integrity and empirical validation. The Logical Fallacy Score measures the impact of identified logical fallacies within an argument, while the Evidence Verification Score assesses the degree of independent corroboration, through rigorous methods like blind studies and scenario comparisons. Together, these scores not only elevate the accuracy and rationality of our beliefs but also promise a new era of intellectual discourse. We invite bright minds to join us in coding this vision into reality, unlocking the potential to change how we debate, learn, and grow.

Logical Fallacy Score: Indicates the relative performance of pro/con sub-arguments that an argument uses, or a belief depends on logical fallacies. Users can accuse beliefs and arguments of specific fallacies from a list and provide reasons to agree or disagree that the belief uses, or depends on it. The score reflects the relative performance of these arguments (using Google's PageRank Algorithm). Evidence Verification Score: Indicates the degree to which the belief has been independently verified (e.g., through blind or double-blind studies and the ratio and quantity of sufficiently similar scenarios, sorted by the number of replications or the degree of similarity).

Recognizing fallacious arguments is crucial for developing a forum for better group decision-making, creating an evidence-based political party, and for humans to make intelligent group decisions.

To achieve this, we propose using the scientific method of tying the strength of our beliefs to the strength of the evidence. The evidence takes the form of pro/con arguments for human arguments. These arguments are tied to data with logic. Therefore, we will explicitly tie the strength of our belief to the power, or score, of the pro/con evidence.

We will measure the relative performance of pro/con sub-arguments by weighing each argument using a specific logical fallacy. Once a user flags an argument as using a logical fallacy, or if the computer uses semantic equivalency scores to flag an idea as being similar to another idea that has been identified as using a logical fallacy, the site will create a dedicated space for reasons to agree or disagree with the fallacy accusation. The logical fallacy score reflects the relative performance of these accusation arguments, and our argument analysis algorithms will subject these arguments to grouping similar ways of saying the same thing, ranking the different types of truth separate from importance, and linkage (evidence to conclusion linkage).

There are several common types of fallacious arguments that are often used to support conclusions, but they are actually non-sequiturs, meaning they do not logically follow from the premises. Examples of these types of arguments include:

Ad hominem fallacy: This is when someone attacks the person making an argument rather than addressing the argument itself. For example, saying, "You can't trust anything he says because he's a convicted criminal," does not logically address the argument.

Appeal to authority fallacy: This is when someone claims something is true simply because an authority figure says it is true without providing any other evidence or reasoning. For example, saying, "Dr. Smith said it, so it must be true," does not logically prove that the argument is sound. Red herring fallacy: This is when someone introduces a completely unrelated topic or argument to distract from the original argument. For example, saying, "I know I made a mistake, but what about all the good things I've done for the company?" does not logically address the issue. False cause fallacy: This is when someone claims that because one event happened before another, it must have caused the second event. For example, saying, "I wore my lucky socks, and then we won the game, so my socks must have caused the win," does not logically prove causation. By identifying and avoiding these fallacies, individuals can contribute to a more rigorous and evidence-based decision-making process, which can ultimately lead to a more effective political system and better-informed public opinion. The Logical Fallacy Score allows for the identification of specific fallacious arguments and promotes critical thinking and reasoned discourse.

Algorithm Identify a list of common logical fallacies.

Allow users to flag arguments that may contain these logical fallacies.

Enable users to share evidence and reasoning to support or weaken the belief that the argument identified in step #2 contains a logical fallacy. Develop a system to automatically flag arguments that are similar to other statements already flagged as containing a logical fallacy. Create a machine learning algorithm to detect language patterns that may indicate a particular fallacy.

For each argument flagged as containing a logical fallacy, evaluate the score of logical fallacy sub-arguments that support or weaken the belief that the argument contains a logical fallacy.

Use the results of these evaluations to assign a Logical Fallacy Score confidence interval.

It's important to note that the Logical Fallacy Score is just one of many algorithms used to evaluate each argument. We will also use other algorithms to determine the strength of the evidence supporting each argument, the equivalency of similar arguments, and more. The Logical Fallacy Score is designed to identify arguments that contain logical fallacies, which can weaken their overall credibility.

We believe that this system will help people make better decisions by providing them with more information about the strength of the arguments they are considering.

Code Check here for the latest examples of code on GitHub: https://github.com/myklob/ideastockexchange/wiki#logical-fallacy-score Path Forward

A large and diverse dataset: To train the machine learning models used in the system, it would be helpful to have a large and diverse dataset of examples of logical fallacies. This dataset would ideally include examples from a wide range of domains (e.g., politics, business, science) and from different types of media (e.g., news articles, social media posts, speeches).

Domain-specific knowledge: Some types of logical fallacies may be more common in certain domains than others. For example, ad hominem attacks may be more common in political discourse than in scientific research. To improve the accuracy of the system, it would be helpful to incorporate domain-specific knowledge into the algorithms.

Human input and feedback: While machine learning algorithms can be very effective at detecting patterns in large datasets, they may still make mistakes or miss certain nuances. To address this, the system could incorporate human input and feedback. For example, users could flag examples of logical fallacies that the system missed, or provide feedback on examples that were flagged incorrectly. Continual improvement: Like any machine learning system, the logical fallacy detection system would benefit from continual improvement over time. This could involve collecting new data, refining the algorithms, and incorporating feedback from users. As the system improves, it could become more accurate and effective at identifying logical fallacies, which could ultimately lead to better decision-making and more informed public discourse.

An Alternate Explanation

The Logical Fallacy Score is a tool designed to evaluate the strength of arguments by identifying and tracking the use of logical fallacies. Logical fallacies are errors in reasoning that render an argument invalid. With this tool, users can identify and point out specific fallacies in beliefs and arguments from a list, providing reasons to agree or disagree that the belief employs or relies on them. The score reflects the relative performance of pro/con sub-arguments concerning whether an argument uses or a belief depends on logical fallacies.

The Logical Fallacy Score assists users in making more informed decisions and avoiding being misled by flawed reasoning. It takes into account several factors, including the type of fallacy, its severity, and the performance of pro/con arguments, up/down votes, and other algorithms. This score helps people track our confidence in the presence of a logical fallacy in an argument over time, similar to how we track stock performance. A low score indicates that the argument's validity likely does not depend on logical fallacies. However, as the score increases, it suggests that the argument relies more significantly on minor, moderate, or major logical fallacies. The Logical Fallacy Score combines the "argument importance score" with our confidence that each specific belief hinges on logical fallacies.

In summary, the Logical Fallacy Score is a powerful tool for evaluating argument strength and helping people avoid being misled by flawed reasoning. It can be used to make more informed decisions and promote critical thinking.