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Dear Esteemed Mathematics Educators, I am reaching out to you today with an exciting proposition - a unique opportunity to engage your students in the application of mathematical principles in an unconventional and meaningful way. It involves a novel algorithm designed to evaluate and promote ideas based on the strength of the reasoning and evidence provided. Here is the formula we're discussing: Conclusion Score (CS) = ∑ [(LS * RS_agree - LS * RS_disagree) * RIW] + ∑ [(LS * ES_agree - LS * ES_disagree) * EIW] + ∑ [(LS * IS_agree - LS * IS_disagree) * IIW] + ∑ [(LS * BS_agree - LS * BS_disagree) * BIW] + ∑ [(LS * IMS_agree - LS * IMS_disagree) * IMIW] + ∑ [(LS * MS_agree - LS * MS_disagree) * MIW] More detailed information about these variables can be found on our websites: https://github.com/myklob/ideastockexchange and https://www.groupintel.org/. In an era where discourse is increasingly digitized, this algorithm operates within a web-based forum. It allows users to submit reaso...

I am writing you to ask for your assistance in promoting "good idea promoting algorithms" such as the following: The above formula would work in an environment were you were able to submit reasons to agree or disagree with a belief, and then you could submit reasons to agree or disagree with those arguments. With this format it place you could count the reasons to agree and subtract the number of reasons to disagree, and then you could integrate the series of reasons to agree with reasons to agree. You should use this equation because: It is unique. I have never seen someone use an algorithm in an attempt to promote good ideas. Math can become more interesting when kids see the variety of ways it can be applied.  Kids are idealistic, and often want to improve the world. Challenging them to try to come up with a good idea promoting algorithm can use this energy, to learn math. This simple that counts the reasons to agree with a conclusion, could change the wold, similar to...

In an online forum that utilizes a relational database to track arguments either supporting or countering conclusions, and allows users to submit their beliefs as reasons to support other beliefs, the deployment of the following algorithm can prove highly advantageous: Or with math: The equation for the idea score can be represented as: Basic Algorithm: Conclusion Score (CS) is a weighted sum of different types of scores, each calculated as the difference between supporting and opposing elements for the given conclusion. It is represented as: Conclusion Score (CS) = ∑ [(LS * RS_agree - LS * RS_disagree) * RIW] + ∑ [(LS * ES_agree - LS * ES_disagree) * EIW] + ∑ [(LS * IS_agree - LS * IS_disagree) * IIW] + ∑ [(LS * BS_agree - LS * BS_disagree) * BIW] + ∑ [(LS * IMS_agree - LS * IMS_disagree) * IMIW] + ∑ [(LS * MS_agree - LS * MS_disagree) * MIW] Where: CS: Conclusion Score LS is the Linkage Score, representing the strength of the connection between an argument and the conclusion it suppo...

I have a math equation I want to express correctly, but I have been out of college for 10 years and I’m a little rusty. This is my attempt, but I’m not sure I have the series written correctly: n = number of steps an argument is removed from an idea, where a reason to agree is one step removed, but a reason to agree with a reason to agree is two steps.   A 1 = Number of reasons to agree (Count as 1 point each, towards the idea) D 1  = Number of reasons to disagree (Count as 1 point each, towards the idea) A 2 = Number of reasons to agree with reasons to agree or disagree with reasons to disagree (Count as 1/2 point each, towards the idea) D 2  = Number of reasons to disagree with reasons to agree or agree with reason to disagree (Count as 1/2 point each, towards the idea) and so on I’m not sure I have enough summation symbols.  If I define A sub 1 as “Number of” can I leave out the extra summation symbols shown in this equation:

Similar to books, various forms of media like movies (particularly documentaries), songs, or expert opinions can offer support or opposition to different perspectives. For instance, the website Rotten Tomatoes offers scores for movies which can be an indicator of the general consensus about the argument or message a film is putting forward. This data could be integrated into the evaluation of a belief or argument, along with any formal logical arguments presented within the media content. The Link Score (L): When beliefs are submitted as reasons to support other beliefs, there's a risk of irrelevant arguments being included. For example, someone might claim that the belief "the grass is green" is a reason to believe "the New York Giants will win the Super Bowl." Although the belief that "the grass is green" might have a high agreement score, the relevance or "Link Score" will be close to zero due to the lack of a logical connection. As this p...

Similar to how I say books can support or oppose different conclusions, movies (often documentaries) can support or oppose different conclusions. Rotten tomatoes gives scores to movies. All of this data could be imported, as well as the formal logical arguments that a movie actually attempts to support or oppose a belief. L = Link score. When we submit beliefs as reasons to support other beliefs, and give higher scores to conclusions that have more reasons to agree with them, people will try to submit beliefs that don’t really support the conclusion. For instance someone might post the belief that the grass is green as a reason to believe the NY Giants will win the super bowl. The beliefs that the grass is green will receive a high score, but the “Link Score” as will be close to zero. * As we work this out we may have to apply multiplication factors to not give too much or too little weight to a factor. ** Who has a “.edu” e-mail address from the philosophy department of an accredited ...

I think if we tracked the number of up votes and compared it to the number of down votes it might tell us a little about the quality of an argument, or at least its perceived quality. I think the more information the better. This is the best equation I can come up with for adding points to a belief based on the number of up or down votes. I would love your feedback. Below is an explanation of each term. Up/Down Votes UV /DV  = Up or Down Vote #U = Number of Users We will have overall up or down votes. We will also have votes on specific attributes like: logic, clarity, originality, verifiability, accuracy, etc.

I believe that tracking the number of books suggested as reasons to agree or disagree with a conclusion could help develop algorithms that promote beliefs that have been thoroughly examined and supported. 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: Points = Σ(BS * BLS) 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...

I once took a course in logic taught by a professor of philosophy, a discipline in which formal logic often plays a crucial role. My proposal involves quantifying the input of logic professors who "authenticate" the logic of an argument, juxtaposed against those who "contend" with the logic of the same argument. Such data could potentially bolster the credibility of ideas that have been meticulously scrutinized and validated. Consider this modified equation, using a ratio to add or subtract points from a belief based on the input of logic professors: Ratio = Number of times a certified logic instructor has authenticated the logic of a given argument (LPV) / Number of times a certified logic instructor has contested the logic of a given argument (LPC). Using this ratio, if a logic professor opposes a reason that underpins your conclusion, the overall score would decrease proportionately. This is because the action of contesting is twice removed from directly affirmin...

I had a logic professor. He was in the philosophy department, and he taught a course on logic. Every professor has a few philosophy teachers that teach formal logic. I think if we tracked the number of logic professors that "certify" the logic of an argument and subtract the number of logic professors that "discount" the logic of an argument, we could use that data to promote ideas that have been more thoroughly examined, and supported. This is the best equation I can come up with for adding points to a belief based on the number logic professors that support or oppose the logic used in an argument. I would love your feedback! Below is an explanation of each term. NPA/D = Number of times a certified logic instructor has verified/discounted the logic of a reason to disagree Summing “NPA or NPD” would mean that if a logic professor disagreed with a reasons to support your conclusion, that would take away ½ a point, because that action is twice removed.

I think if we tracked the number of books that are suggested as reasons to agree with a conclusion, or disagree, we could come up with algorithms that use this data to promote beliefs that have been more thoroughly examined, and supported. This is the best equation I can come up with for adding points to a belief based on the number of and the quality of each book that is suggested as a reason to support or disagree with a conclusion.  I would love your feedback! Below is an explanation of each term. B = Books that have been said to support or oppose the given conclusion BS = Books Score. Books scores can take into account number of books that are sold, as well as the score given from book reviewers, etc BLS = Book link score. You can have a good book, that doesn’t actually support the proposed belief. Each argument that a book supports a belief, becomes its own argument that that its own book “linkage score” that is given points according to the above formula

I think if we tracked the number of up votes and compared it to the number of down votes it might tell us a little about the quality of an argument, or at least its perceived quality. I think the more information the better. This is the best equation I can come up with for adding points to a belief based on the number of up or down votes. I would love your feedback. Below is an explanation of each term. Up/Down Votes UV /DV  = Up or Down Vote #U = Number of Users We will have overall up or down votes. We will also have votes on specific attributes like: logic, clarity, originality, verifiability, accuracy, etc.

This function will add points to conclusions that have money invested in them or their supporting arguments, and subtract points from conclusions that tend to have money invested against them. Why do this? Because Vegas understands the relative strength of football team. Wall Street understands the relative strength of each company, and Intrade will tell you which president will win. Why don't we use “markets” to tell us the relative strength of each argument, before we make life or death decision in the Middle East?  Why do we have more processing power dedicated to analyzing football games than we do life or death problems? Below is an explanation of the terms in my equation. I would love input! My equation in words: My equation in math: M an /n:  When n is 1, this equation will add all the money invested in a belief. When n is equal to 2 it will take the money invested in arguments that support the belief, divides it by 2, adds that to the conclusion score. Money invested i...

This function will add points to conclusions that have money invested in them or their supporting arguments, and subtract points from conclusions that tend to have money invested against them. Why do this? Because Vegas understands the relative strength of football team. Wall Street understands the relative strength of each company, and Intrade will tell you which president will win. Why don't we use “markets” to tell us the relative strength of each argument, before we make life or death decision in the Middle East?  Why do we have more processing power dedicated to analyzing football games than we do life or death problems? Below is an explanation of the terms in my equation. I would love input! My equation in words: My equation in math: M an /n:  When n is 1, this equation will add all the money invested in a belief. When n is equal to 2 it will take the money invested in arguments that support the belief, divides it by 2, adds that to the conclusion score. Money invested i...

This is my dad's mom's mom's life history. Also check out the ongoing projects for my dad , and mom , and mom's mom . (I (Michael Laub) typed the below from a photocopy of the original, which was typed from my Grandmother. I did it pretty fast, and know there are lots of spelling mistakes... ) My Parents were Mormon pioneers, my father, James Peter Anderson, was born 28th of August 1862, Ephraim, Sandpete County Utah, was the son of Neil's Anderson, who came from Lond Sweden, and his mother Ingaborg Paulsen who came from Dyver Norway for the Gospel. My mother's father was Peter Thomander, son of one of Sweden’s Great Theology professors, John Henric Thomander who was head of the theological department at Lund University when the first Mormon Missionaries went to Sweden. Peter Thomander met Ingaborg Pearson, his wife to be, on the ship which brought them to America, where they were to join the Saints in Ephraim, Sanpete County, Utah. Shortly afte...

Welfare / Incentives Reasons to Agree: 🔗 Reasons Database Welfare Pays More Than Low-Wage Jobs – Some studies suggest that government assistance provides more disposable income than minimum-wage employment. Welfare Cliff Effect – Many welfare programs reduce benefits as income increases, sometimes at a rate that discourages full-time work. Labor Market Participation Decline – Expanded welfare benefits post-pandemic have correlated with a decrease in workforce participation in some sectors. Reasons to Disagree: 🔗 Reasons Database Many Welfare Recipients Already Work – Most able-bodied adults on welfare already have jobs but remain eligible due to low wages and high living costs. Work Requirements Exist in Most Programs – Programs like TANF and SNAP require work participation, limiting long-term dependency. Flawed Comparisons – Some studies compare gross wages to total welfare benefits, without factoring in taxes, childcare, and transportation costs. Evidence That Agrees: 🔗 Evidence S...

Background  Megan took James to his yearly doctors appointment. James was cute, and asked many time: "what are you doing to me". He didn't try to stop the nurse when she gave him the nasal inhaler flu shot. James is tall and growing fast (+1, unresolved) Best reasons to agree : +3 He is in the 95.72 percentile. This means of 100 kids, 4.28 of them would be taller.  He gained 7 lbs, and 3" this year.  We could say that we are using the McDonald's systems of measurement: small, medium, and large. Using these categories we could sort of "grade on a curve" and decide 33% of people are small, 33% of people are average, and 33% of people are tall. James, at 95.72 percentile, would be in the "tall" category as long as the sample population is limited to people born in O4.  James has doubled his size in just a few years. The universe will take billions of years to double its size. Therefore, James is growing fast.  ...