-
Notifications
You must be signed in to change notification settings - Fork 1
Home
Welcome to the ideastockexchange wiki!
Google's PageRank algorithm can be repurposed to assess the strength of arguments presented in pro/con debates. This method effectively ties the credibility of a final conclusion to its supporting arguments, mirroring how PageRank assigns website importance based on backlinks.
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.
Mapping the Path to Truth: Leveraging Linkage Scores for Evidence and Arguments
We're developing a revolutionary approach to rational discourse and decision-making, using Evidence-to-Conclusion and Argument-to-Conclusion Linkage Scores. Our method gauges the strength of causal relationships between evidence, arguments, and conclusions, determining their relevance and impact. It's grounded in the collective wisdom of pro/con arguments and the consensus on an evidence's relevance, all evaluated through an adaptation of Google's PageRank Algorithm. This project promises a new era in assessing validity and truth, harnessing the power of collective reasoning to clarify the path to better beliefs and decisions. Come join us in building this groundbreaking tool for the future of rational discourse.
Argument Importance Score: Measures the degree to which a belief's truth relies on the truth of a particular argument, by comparing the performance of pro and con sub-arguments and determining whether it is the primary deciding factor in the belief's strength. The pro/con arguments are evaluated using Google's PageRank Algorithm.
Belief Impact Score (BIS): Evaluates arguments by assessing their strength in components. It combines the Argument Impact Score and cost-benefit analysis to determine the impact of accepting a belief. The BIS helps identify essential beliefs for society and those with the greatest potential for improving culture. Users gather beliefs and reasons and assess argument strength using linkage scores. A user-friendly template collects necessary information, stored in a database. Scoring algorithms consider costs, benefits, risks, sources of information, consensus level, and other factors. Scores are normalized to add up to 100% and presented using a web interface. The pro/con arguments are evaluated using Google's PageRank Algorithm.
Equivalency Score: Initially determined by semantic similarity metrics and machine learning algorithms, this score is also subjected to our pro/con argument evaluation process. Specific to equivalency, users can submit and vote on reasons why beliefs are similar or superior to the other. We track the performance of these arguments and related up/down votes (or other measures of user approval) to increase confidence in the equivalency score, similar to how stock prices are tracked over time. These are used to group similar ways of saying the same thing and develop "unique scores" when similar arguments are used to support the same topic, resulting in redundancy. The pro/con arguments are evaluated using Google's PageRank Algorithm. Better Ways of Saying the Same thing: Listing and scoring this helps users find other beliefs that express the same idea as the one they are looking at. It is based on the Belief and Equivalency scores discussed earlier. When users click on a belief, they will see a list of similar beliefs sorted by this score. They can also browse these beliefs separately by their individual Equivalency or overall Belief scores. The pro/con arguments are evaluated using Google's PageRank Algorithm.
Automated conflict resolution and collective intelligence techniques can foster democratic values, enhance online interactions, and promote healthier decision-making. By focusing on objective criteria, prioritizing interests over positions, and pursuing mutual gain, solutions can be collectively evaluated using ReasonRank, our version of Google's PageRank. Scores will be generated to determine each proposal's likelihood of resolving conflict, factoring in aspects such as conflict resolution probability. The Resolution likelihood will be determined using an analysis of pro/con arguments of reasons why the proposal will meet the valid interest of each side and the degree to which each side is motivated by valid interests. Conflict resolution probability will also address the required compromise level, an analysis of costs and benefits, and other significant obstacles to resolution.
The Belief Stability Confidence Score (BSCS) gauges the consistency and trustworthiness of a belief's score over time. Considering the number of unresolved pro/con branches (or sub-arguments), this would track the belief's stability over time. The goal is to represent a consensus that an interested, focused, representative group of knowledgeable, rational users would reach. This involves using conflict resolution, formal logic, cost-benefit analysis techniques, and tools to monitor and promote these methods. We would reverse engineer the types of algorithms that enable obvious conclusions, such as the widely accepted wrongness of theft, pollution, and murder. For example, suppose we count reasons to disagree, with reasons to disagree as reasons to agree (the double negative). In that case, I am currently agnostic about how much to reduce an argument's contribution proportional to its distance from the original belief. This argument would be two levels removed from the conclusion. Should it provide at most 1/2 (1/n) of its points? We'll have to play with it. However, a belief that has had thousands of hours of analysis with a stable score means much more than a belief that has had 1 hour of analysis with a stable score. We can quantify that now, but I fear losing people by getting into the weeds too much.
Welcome to a revolutionary concept in argument evaluation - the integration of the Logical Fallacy Score and the Evidence Verification Score. 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. In assessing the strength of an argument, two important scores to consider are the Logical Fallacy Score and Evidence Verification Score. The Logical Fallacy Score reflects the relative performance of sub-arguments that point out a specific logical fallacy used in the top-level argument. Meanwhile, the Evidence Verification Score indicates the degree to which the belief has been independently verified, such as through blind or double-blind studies and the number and similarity of scenarios. By taking into account these scores, we can arrive at more informed and rational beliefs.