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Enlightenment Promotion Algorithm Variables

Our goal is to effectively evaluate arguments by breaking them down into individual components and closely examining the strength of each component using various algorithms. This enables us to link the strength of our overall conclusion to the strength of the evidence supporting it. To ensure transparency, we will show our math and provide an open process. The algorithms track important data for each belief, including:

Belief Score:

This score reflects the overall strength of a belief and is continuously updated based on the performance of the following scores. It embodies the wisdom of thinkers throughout history, from Aristotle to Carl Sagan, who recognized that "Extraordinary claims require extraordinary evidence" and that "The wise man proportions his belief to the evidence." These principles form the foundation of scientific, moral, and human progress. And now, they are explicitly defined in software code for the first time, allowing us to engage with each other and reason simultaneously.

def rank_arguments(arguments):
    """
    Ranks a list of arguments based on their pro/con score and strength of evidence
    """
    for arg in arguments:
        pro_score = arg['pro_votes'] / (arg['pro_votes'] + arg['con_votes'])
        con_score = arg['con_votes'] / (arg['pro_votes'] + arg['con_votes'])
        strength_score = arg['evidence_strength'] # assuming this is a score from 0-1
        
        # Calculate the overall argument score by combining the pro/con and strength scores
        argument_score = (pro_score * strength_score) - (con_score * strength_score)
        
        arg['score'] = argument_score
        
    # Sort the arguments by score, highest to lowest
    sorted_arguments = sorted(arguments, key=lambda x: x['score'], reverse=True)
    
    return sorted_arguments

See the pages to the right for all the algorithms we have so far.