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The Idea Stock Exchange is an innovative platform designed to facilitate automated conflict resolution and cost-benefit analysis. At its core is the Conclusion Score (CS), a metric that quantitatively evaluates the strength and validity of conclusions or beliefs by analyzing arguments and evidence.
The Conclusion Score (CS) is calculated based on multiple factors, ensuring a balanced, evidence-based deliberation process. Each belief or argument is scored dynamically, reflecting the strength of its supporting and opposing components.
| Component | Abbreviation | Description |
|---|---|---|
| Reasons to Agree/Disagree | RtA/RtD | Strength and number of arguments supporting or opposing a conclusion. |
| Evidence Strength | ES | Evaluates the reliability and relevance of supporting/disputing evidence. |
| Logical Coherence | LC | Assesses logical structuring and the absence of fallacies in arguments. |
| Verification and Credibility | VC | Measures evidence credibility based on unbiased, independent sources. |
| Linkage and Relevance | LR | Evaluates the argument's direct influence on the conclusion. |
| Uniqueness and Distinctiveness | UD | Recognizes originality, reducing redundancy in argumentation. |
| Argument Importance | AI | Weighs the significance of an argument's impact on the conclusion. |
CS = ∑((RtA - RtD) × ES × LC × VC × LR × UD × AI)
This algorithmic approach ensures that well-supported, logically coherent, and unique arguments receive higher scores, promoting informed decision-making.
Assessing a policy’s CS with:
- RtA: Scores of 4 and 3
- RtD: Score of 2
- ES, LC, VC, LR, UD, AI: Average weighted values of 0.8, 0.9, 1.0, 0.85, 0.9, and 0.95 respectively
CS = ((4 + 3 - 2) × 0.8 × 0.9 × 1.0 × 0.85 × 0.9 × 0.95)
This approach systematically evaluates all arguments in the Idea Stock Exchange to promote evidence-based conclusions.
Every belief and argument undergoes ReasonRank analysis, an adapted version of Google’s PageRank algorithm. This assigns scores based on the performance of pro/con sub-arguments, ensuring a self-correcting and adaptive framework where:
- Arguments are linked dynamically across different debates.
- The importance of evidence vs. reasoning is continuously updated based on meta-arguments about truth evaluation.
The Idea Stock Exchange is under active development. Below are key steps in its implementation:
- Develop the Logical Fallacy and Evidence Verification algorithms.
- Design a user-friendly interface for argument interaction.
- Integrate the database, user interface, and algorithms into a single system.
- Conduct unit and integration testing to ensure functionality.
- Implement bug tracking and security assessments.
- Validate the performance and usability of key features.
- Collect feedback from early adopters and testers.
- Prioritize enhancements based on impact and feasibility.
- Refine the algorithm and data analysis capabilities.
- Deploy the platform publicly and set up user support systems.
- Implement real-time monitoring for immediate issue resolution.
- Continue iterating based on user experience and feedback.
Below is a modified PageRank algorithm adapted to rank arguments based on supporting and opposing evidence:
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# ReasonRank:
ReasonRank: An adaptation of Google's PageRank to evaluate reasons based on the number and relative strength of pro/con reasons, factoring in the number of pro/con sub-arguments, similar to how Google ranks pages based on the number of links and the strength of those links determined by their sub-links.
````python
>>>>>>> Stashed changes
import numpy as np
def argumentrank(M, num_iterations: int = 100, d: float = 0.85):
"""ArgumentRank algorithm to evaluate the credibility of arguments based on interlinking support."""
N = M.shape[1]
v = np.ones(N) / N
M_hat = d * M + (1 - d) / N
for i in range(num_iterations):
v = np.dot(M_hat, v)
v = np.maximum(v, 0) # Prevent negative scores
v /= v.sum() # Normalize to sum to 1
return v
# Example argument linkage matrix
M = np.array([[0, -0.5, 0, 0, 1],
[0.5, 0, -0.5, 0, 0],
[0.5, -0.5, 0, 0, 0],
[0, 1, 0.5, 0, -1],
[0, 0, 0.5, 1, 0]])
v = argumentrank(M, 100, 0.85)
print(v)This algorithm ensures that valid, well-supported arguments rise in credibility while weaker arguments diminish over time.
- Clone the repository:
git clone https://github.com/your_github_username/idea-stock-exchange.git
- Navigate into the project directory:
cd idea-stock-exchange - Install dependencies:
npm install # Or pip install if using Python - Run the application:
npm start # Or python main.py depending on the tech stack
We welcome contributions! To contribute:
- Fork the repository
- Create a feature branch (
git checkout -b feature/NewFeature) - Commit changes (
git commit -m 'Add feature') - Push to branch (
git push origin feature/NewFeature) - Open a pull request
For inquiries, connect with us via:
- Twitter: @myclob
- Blog: myclob.blogspot.com
- Official Website: ideastockexchange.org
This project is licensed under the MIT License – promoting openness and collaborative development.
A huge thank you to all contributors and supporters of the Idea Stock Exchange. Your dedication to fostering evidence-based discourse is invaluable.
The Idea Stock Exchange is more than just a platform—it’s a movement toward transparent, logical, and evidence-based discussions. Join us today and help shape the future of intellectual discourse!