Media

Why we must link media to the beliefs they support or weaken:

To effectively challenge flawed ideas, it's crucial to identify the media that significantly contributes to each belief. By determining the best pro/con books, images, podcasts, and scientific studies for and against topics such as the existence of God, human-caused climate change, and the effectiveness of the estate tax, we can better understand these issues.

Additionally, presenting supporting and opposing media side by side facilitates a balanced understanding and encourages a comprehensive examination of diverse perspectives on a given issue. This approach promotes critical thinking and fosters a willingness to consider alternative viewpoints. Lastly, integrating arguments from various media sources into a forum that actively discourages manipulation ensures the inclusion of alternative viewpoints. It's essential to extract arguments from media such as poems, movies, and books, where they are not required to engage with opposing arguments, and subject them to a systematic analysis. This process helps to prevent the spread of misleading information and fosters a more informed public discourse.

How?

Our platform invites users to submit their top choices of various media types, such as books, movies, songs, laws, websites, videos, images, and podcasts, that support or oppose specific beliefs. Users can contribute their suggestions and provide pro/con arguments explaining why each media item should be considered the best in its category. We rank these submissions based on the performance of their supporting arguments and their overall scores, helping users access relevant and well-argued resources for each belief. The aim is to create an organized, comprehensive resource for engineers and programmers to better understand and engage with different perspectives.

Types of pro/con media to be included

• Books
• Movies
• Songs
• Laws
• Websites and web links
• Videos
• Images (e.g., political cartoons, photojournalism)
• Podcasts

Examples of media supporting or opposing beliefs

• "Freakonomics" by Steven D. Levitt and Stephen J. Dubner as a reason to explore the hidden side of various social and economic phenomena.
• "An Inconvenient Truth" documentary by Al Gore as a reason to believe in the urgency of addressing climate change.

Scoring system for sorting media

• Impact Score = (Sales Data Score) x (Engagement Score) x (UGC Score) x (Citation Score) x (Viewership Score) x (Validity Score) x (Linkage Score) x (Unique Score)
• Sales Data Score: Normalize book sales data from publishers or retailers, assigning a score between 0 and 1 based on the number of copies sold relative to other media in the same category.
• Engagement Score: Calculate an average engagement score based on the number of likes, shares, and comments for the media, assigning a score between 0 and 1 relative to other media in the same category.
• User-Generated Content (UGC) Score: Take the average rating from user-generated reviews on platforms like Amazon, Goodreads, or IMDb, and assign a score between 0 and 1 relative to other media in the same category.
• Citation Score: Normalize the number of citations or references in academic papers or other media, assigning a score between 0 and 1 based on the frequency of citations relative to other media in the same category.
• Viewership Score: For videos or films, normalize attendance or viewership numbers for live events, movie screenings, or online streaming platforms, assigning a score between 0 and 1 relative to other media in the same category. Validity Score: Assign a score between 0 and 1 based on the performance of the arguments found in the media on our forum. A higher score indicates a stronger performance of the arguments, while a lower score signifies a weaker performance.
• Linkage Score: Calculate the relevance score for links between the media and the belief it supports or opposes. This score represents a confidence interval, based on the performance of the pro/con arguments, which indicates the certainty that the media indeed supports or opposes each stated belief. A higher score indicates a stronger association between the media and the belief, while a lower score signifies a weaker association.

Website design and user interaction

• Enabling users to submit media that can be said to agree or disagree with an idea
• Voting on the relevance of submitted web media and why the media does or doesn't support the belief.
• Algorithm for credibility and sophistication
• Assigning more credibility to users who have read and purchased the media
• Considering users who have written essays on the book or shared their copy
• Promoting ideas with higher-ranking links of agreeing websites
• Weighing sources based on authority and relevance
• Improvements and opportunities
• Enhancing the algorithm with new features and refinements
• The potential impact of a more sophisticated algorithm on decision-making
• Conclusion
• The value of incorporating different forms of media in arguments
• Improving decision-making and understanding diverse perspectives through media inclusion

Book Accuracy Score (BAS)

We use various scores for books. One of them, the Book Accuracy Score, impacts the overall belief score. We operate under the assumption that superior ideas tend to be defended by better books, while inferior ideas often face criticism from higher-quality books. This is partly because books provide ample space for thorough exploration of complex ideas. Exceptional books, regardless of their stance, offer numerous well-structured and logically sound arguments, allowing us to score and rank their accuracy.

To use a book to enhance a belief score, we need to consider several factors: linkage accuracy (does the book genuinely support or oppose the belief as users claim), truthfulness (logical soundness), verifiability of the book's claims (averaged by the relative importance of each claim, assessed through a cost/benefit analysis), redundancy, and other features subject to debate. Through the performance of these debates, we aim to infer which beliefs are more likely to align with the truth.

Book Aesthetic Quality Score (BAQS)

Books are judged on more than just facts. We consider several important things:

• Clarity: How clearly does the book communicate its ideas?
• Flow: Do the sentences and ideas connect smoothly?
• Originality: Does the book bring new ideas or insights?
• Style: What makes the author's writing style unique?
• Engagement: Does the book grab your attention and make you think?
• Grammar and Mechanics: Is the writing free of errors?
• Brevity: Does the book get its message across without rambling?
• Humor: Does the book use humor effectively to make it more enjoyable?
• Imagery: Does the author use descriptions that draw you in?

We also look at whether a book's quality relates to the sophistication of its ideas. If we find that it doesn't, we won't use the BAQS score to change the belief score. But regardless of that, it's still useful to track how much people enjoy reading a book. This can help us identify the best books that agree or disagree with each belief, depending on how you define "best".

Equations

Here's the best equation I've come up with for adding points to a belief based on the number and quality of books suggested as reasons to support or disagree with a conclusion:

Book Accuracy Score (BAS) = Σ(BLS x BTS x BUS x BIS)

Where:

• BLS = Book Linkage Score. This score represents the cumulative performance of each point in the book that is said to support or oppose different beliefs. Therefore a single book, will have different "linkage scores" to different beliefs, depending on the book. This linkage, or relevance score measures the book's content's alignment with each belief being evaluated. For each point, we calculate the difference between the scores of arguments agreeing that the book supports the belief and the scores of arguments disagreeing. This is summed across all points in the book to provide the overall BLS. In other words: BLS = Σ(Pro Argument Score for Linkage of each point - Con Argument Score for Linkage of each point). This will show up for each idea as a list of books that agree or disagree, with columns for "linkage" score between each belief, and those books that can be said to support the idea.

• BTS = Book Truth Score. This cumulative score for each point in the book assesses its truthfulness, considering both logical soundness (absence of logical fallacies) and verification (substantiation through evidence or independent replication). Logical Soundness: This component evaluates whether the claims in the book are free from logical fallacies. It involves examining the book's arguments for logical consistency and validity. The score can be represented as LS, where LS = Σ(Pro Argument Score for Logical Soundness - Con Argument Score for Logical Soundness). Verifiability: This component assesses whether the claims in the book have been verified through independent replication or are likely to be replicated. The score can be represented as VS, where VS = Σ(Pro Argument Score for Verifiability - Con Argument Score for Verifiability). Considering these components, the Truth Score (BTS) can be computed as the average of these two scores. Therefore, BTS = (LS + VS) / 2. This ensures that both logical soundness and verifiability have equal weight in determining the truthfulness of the book's claims.

• BUS = Book Uniqueness Score. This score measures the distinctiveness of the book's arguments or data. It is an average of individual scores for each claim made in the book, based on its uniqueness. Each claim's uniqueness score is calculated using pro and con arguments regarding its novelty and distinct contribution to the discourse. The formula is:BUS = Σ((Pro Argument Score for Uniqueness of each claim - Con Argument Score for Redundancy of each claim) / Number of claims). To break it down, if a book makes a claim that is new or provides unique data, the pro argument score for that claim's uniqueness will be high, while the con argument score for redundancy will be low. This will result in a high uniqueness score for that claim. The BUS is the average of all these individual uniqueness scores, giving an overall assessment of how distinctive the book's content is. So, in summary, the total points added to a belief score based on a book are a product of the book's linkage to the belief (BLS), the reliability of its content (BTS), and the uniqueness of its arguments or data (BUS). This comprehensive scoring system helps us evaluate books' contributions to the belief in a balanced and detailed manner.

• BIS = The Book Importance Score. This score is a measure of the book's overall relevance and influence, and it is calculated by taking into account several different factors. Here is a more detailed breakdown: Sales Score (SS): This score represents how popular a book is, and it is determined by the number of copies that have been sold. A higher sales score implies that the book has reached a larger audience and therefore might have a more significant impact. Review Score (RS): This score reflects how well the book has been received by its readers and critics. It is calculated by aggregating ratings and reviews from various book review platforms, such as Goodreads and Amazon. A higher review score suggests that the readers and critics have found the book to be of high quality and value. Citation Score (CS): This score indicates the book's influence in the academic world or its relevant field. It is calculated based on the number of times the book has been cited in academic papers, other books, or influential articles. A higher citation score suggests that the book's ideas and arguments have had a substantial impact on its field. By considering these factors, the BIS provides a comprehensive measure of a book's importance. This makes it a valuable component of our overall scoring system, allowing us to better assess the potential impact of a book on the beliefs it supports or challenges.

Overall Code

def calculate_BAS(books):
    BAS = 0
    for book in books:
        BLS = calculate_BLS(book)
        BTS = calculate_BTS(book)
        BUS = calculate_BUS(book)
        BIS = calculate_BIS(book)
        
        BAS += BLS * BTS * BUS * BIS

    return BAS

Book Linkage Code 1

# An example code for calculating the BTS in Python. This code assumes that you have a list of arguments for Logical Soundness and Verifiability, each with a pro score and a con score. In this code, we first calculate the score for Logical Soundness (LS) and Verifiability (VS) by summing the difference between pro scores and con scores for each argument. Then, we calculate the average of LS and VS to get the BTS. 
def calculate_BLS(book):
    BLS = 0
    # Assuming book.points is a list of all points in a book
    for point in book.points:
        pro_arg_score = calculate_pro_arg_score_for_linkage(point)
        con_arg_score = calculate_con_arg_score_for_linkage(point)
        BLS += pro_arg_score - con_arg_score
    return BLS

Book Linkage Code 2

def calculate_BLS(linkage_args):
    BLS = sum([arg['pro_score'] - arg['con_score'] for arg in linkage_args])
    return BLS

linkage_args = [
    {'pro_score': 4, 'con_score': 2},
    {'pro_score': 5, 'con_score': 1},
    # Add more arguments as needed
]

BLS = calculate_BLS(linkage_args)
print(BLS)
''''

# Book Truth Code

````python
def calculate_BTS(logical_soundness_args, verifiability_args):
    LS = sum([arg['pro_score'] - arg['con_score'] for arg in logical_soundness_args])
    VS = sum([arg['pro_score'] - arg['con_score'] for arg in verifiability_args])

    BTS = (LS + VS) / 2

    return BTS

# Logical soundness and verifiability arguments
# Each argument is a dictionary with a pro score and a con score
logical_soundness_args = [
    {'pro_score': 5, 'con_score': 2},
    {'pro_score': 7, 'con_score': 3},
    # Add more arguments as needed
]

verifiability_args = [
    {'pro_score': 6, 'con_score': 2},
    {'pro_score': 8, 'con_score': 4},
    # Add more arguments as needed
]

BTS = calculate_BTS(logical_soundness_args, verifiability_args)
print(BTS)

Book Uniqueness Score

def calculate_BUS(uniqueness_args):
    BUS = sum([arg['pro_score'] - arg['con_score'] for arg in uniqueness_args])
    return BUS

uniqueness_args = [
    {'pro_score': 7, 'con_score': 3},
    {'pro_score': 6, 'con_score': 4},
    # Add more arguments as needed
]

BUS = calculate_BUS(uniqueness_args)
print(BUS)

Book Importance Score

def calculate_BIS(SS, RS, CS):
    # You may want to normalize or weight these scores depending on your specific needs
    BIS = (SS + RS + CS) / 3
    return BIS

SS = 7  # Sales Score
RS = 8  # Review Score
CS = 9  # Citation Score

BIS = calculate_BIS(SS, RS, CS)
print(BIS)

I'd appreciate your feedback on this approach and its potential effectiveness in promoting well-examined ideas. Below is an explanation of each term:

• B = Books that have been said to support or oppose the given conclusion
• BS = Book Score, which can take into account the number of books sold, scores given by book reviewers, etc.
• BLS = Book Link Score, which evaluates how well a book supports the proposed belief. Each argument that a book supports a belief becomes its own argument, and the book's "linkage score" is assigned points based on the equation provided above.

Equations

Here's a suggested algorithm:

Developing a comprehensive book score can be an intricate process, as it should ideally consider numerous factors to reflect a book's overall quality, relevance, and influence. Here's a suggested algorithm:

• Sales Score (SS): Reflects the popularity of the book. This can be determined based on the number of copies sold. A higher number of sales would translate to a higher score.
• Review Score (RS): Reflects the reception of the book by readers and critics. This can be determined by aggregating scores from book review platforms like Goodreads, Amazon, etc. The average rating can then be normalized on a scale of your choice, say 0-10 or 0-100. Relevance Score (RelS): Measures how much the book's content is related to the belief being evaluated. This can be determined by analyzing the book's synopsis, key themes, and arguments.
• Citation Score (CS): Reflects the influence of the book in academia or the relevant field. This can be determined by the number of times the book is cited in academic papers, other books, or influential articles.

Once you have these individual scores, you could combine them to get the final Book Score (BS). One possible way of combining could be a weighted average, for example:

• BS = w1SS + w2RS + w3RelS + w4CS

Here, w1, w2, w3, and w4 are weights that sum up to 1 and reflect the importance you attribute to each component of the score. For instance, if you believe sales and reviews are the most significant factors, you could assign them higher weights. This is just one way to approach creating a book score, and the algorithm can be further refined or adjusted based on your specific needs or the available data. Remember to normalize each component to ensure that they contribute fairly to the final score, despite having potentially different scales and distributions.