Enriching Mathematical Learning Through the Practical Application of Algorithms

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 reasons to agree or disagree with a belief, and encourages further discussion by allowing additional reasoning to be submitted for these primary arguments. The algorithm integrates these layers of discourse, forming an assessment of the belief's validity by counting and comparing reasons to agree and reasons to disagree.

Why introduce this into your mathematics curriculum?

1. Innovation: This algorithm provides a unique application of mathematical principles in an area traditionally untouched by such methods - the evaluation and promotion of ideas.

2. Engagement: By combining mathematics with discourse, debate, and real-world application, students can experience the practical and impactful side of their mathematical studies.

3. Idealism: This project aligns well with the idealistic nature of young minds. It enables students to contribute positively to global conversations, fostering a deeper connection to their learning.

4. Potential Impact: Just as Google's PageRank algorithm revolutionized the internet by ranking web pages based on their inbound links, our algorithm seeks to enhance discourse by assessing and promoting good ideas. This creates an informed and critical thinking internet community.

5. Towards a Smarter World: The development and use of such algorithms can contribute to a more informed, critical and intelligent world.

I encourage you to review this proposal and consider the immense potential it holds for enriching your curriculum and inspiring your students. I am eager to answer any questions or provide further information at your convenience.

Thank you for your time and consideration.

Best regards,
Mike

An open letter to Math teachers

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:

1. 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. 
2. 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.
3. This simple that counts the reasons to agree with a conclusion, could change the wold, similar to how Google's web-link counting algorithm changed the world. When lots of people link to a website, Google assumes that website is a good one. Then when that good website links to another website, Google assumes the 2nd website is a good one. Similarly when you submit good reasons to support an argument, a smart web forum would also give points to the conclusions that are built on that assumption. 
4. The more people make good idea promoting algorithms, the less stupid world we will live in.

Optimal Algorithm for Online Forums Utilizing Relational Databases for Debate

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 supports.
• n represents the number of steps an argument is removed from an idea. For instance, a direct reason to agree or disagree is one step removed, whereas a reason to agree with a reason to agree is two steps removed.
• RS, ES, IS, BS, IMS, and MS are scores associated with reasons, evidence, investments, books, images, and movies respectively that support or counter the belief. Each score is associated with a weighting factor (RIW, EIW, IIW, BIW, IMIW, and MIW) to signify its importance.

The idea score is calculated by subtracting the sum of the argument scores multiplied by their respective linkage scores for the reasons to disagree from the sum of the argument scores multiplied by their respective linkage scores for the reasons to agree. This equation takes into account the relative strength and linkage of each argument in determining the overall idea score.

The equation for the linkage score can be represented as:

The linkage score is calculated by subtracting the sum of the sub argument scores that disagree from the sum of the sub argument scores that agree, and then dividing it by the total number of arguments. This value is then multiplied by 100% to express the result as a percentage. The linkage score represents the percentage of weighted scores that agree with the belief, indicating the strength of the agreement among the sub arguments in relation to the total number of arguments.

Unique Score (US) = [(Sum of scores agreeing that two statements are unique) - (Sum of scores disagreeing that two statements are unique)] / (Total argument scores) * 100

This score evaluates the uniqueness of two statements, normalizing it by the total argument scores. The score ranges from -100 to +100, where -100 indicates full agreement that two statements are not unique (or identical), and +100 indicates full agreement that two statements are indeed unique.

Math Question

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. 
• A₁ = Number of reasons to agree (Count as 1 point each, towards the idea)
• D₁  = Number of reasons to disagree (Count as 1 point each, towards the idea)
• A₂ = Number of reasons to agree with reasons to agree or disagree with reasons to disagree (Count as 1/2 point each, towards the idea)
• D₂  = 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:

Other Factors: Additional Evidence such as Movies, Songs, Expert Opinions

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 process is refined, certain multiplication factors may need to be applied to avoid giving too much or too little weight to certain factors.

** Credibility can often be gauged by looking at the source of information. For instance, those with a ".edu" email address from the philosophy department of an accredited university can be considered reliable, knowledgeable sources.

• Logical Arguments:
• Multidimensionality of Knowledge: Knowledge and perspectives can come from various sources, not limited to academic texts and discussions. Movies, songs, and expert opinions can provide rich and varied insights, supplementing our understanding.
• Supporting Evidence (data, studies):
• Numerous studies have demonstrated the educational potential of films and music (Marsh, Jackie. "Popular culture in the literacy curriculum: a 'Bourdieuan' perspective." Reading literacy and language (2003): 96-103.)
• Supporting Books:
• "Film as Philosophy: Essays on Cinema After Wittgenstein and Cavell" by Rupert Read and Jerry Goodenough: This book demonstrates the philosophical potential of films.
• "The Rest Is Noise: Listening to the Twentieth Century" by Alex Ross: It highlights the historical and cultural insights that can be drawn from music

Other Factors: Stuff, like movies, songs, experts, etc that agaree or disagree

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 university

Other Factors: Up/Down Votes

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.

Other Factors: Books that agree or Disagree

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.

Other Factors: Incorporating Input from Logic Professors

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 affirming the belief, which is reflected in the ratio.

It's important to note that these equations would be adjusted and fine-tuned over time to improve the site's user engagement and overall performance. Our goal is to create a system that is flexible, responsive, and continually improving based on user interaction and feedback. Your thoughts and suggestions on this proposed approach would be greatly appreciated.


a) Fundamental Beliefs or Principles one must reject to also reject this belief:

• Rejection of Expertise: To disregard the idea of using evaluations from philosophy professors who have taught formal logic means rejecting the concept that individuals who have studied and taught critical thinking and formal logic possess a special skill set that can be used to assess the validity of arguments effectively.
• Rejection of Academic Knowledge: This also entails the rejection of the principle that academia, specifically in the field of philosophy and formal logic, contributes significantly to understanding and assessing arguments.
• Rejection of Objective Assessment: This further implies rejecting the idea that an argument's validity can be objectively analyzed based on established principles of formal logic.

b) Alternate Expressions:#LogicCheckedByAcademics

• #FormalLogicValidation
• "Endorsed by Philosophy Educators"

c) Objective Criteria to Measure the Strength of this Belief:

1. Number of Philosophy Professors who have taught Formal Logic that endorse the argument.
2. Consistency of their evaluation with established principles of formal logic.
3. The acceptance and application of their assessments in resolving disagreements or strengthening arguments.

d) Shared Interests between Those Who Agree/Disagree:

• Both sides likely value logical consistency and sound arguments.
• Both would probably appreciate a fair and objective assessment process.
• Both parties likely want the discussion or debate to contribute to truth and understanding, not merely winning an argument.

e) Key Opposing Interests between Those Who Agree/Disagree (that must be addressed for mutual understanding):

• Those who agree might feel that input from Philosophy Professors who have taught Formal Logic adds credibility and objectivity to the discussion.
• Those who disagree might fear this approach overly privileges academic knowledge, potentially excluding valuable perspectives from non-academics or individuals with practical, rather than formal, understanding of logic.
• For constructive dialogue, it is necessary to acknowledge the value of expert input while ensuring that all meaningful and insightful contributions are given due consideration.

f) Solutions:

• Create a balanced system where validations from philosophy professors who have taught formal logic are one of many factors considered in an argument's strength.
• Incorporate input from a diverse range of experts, not just philosophy academics experienced in formal logic.
• Implement a system that allows users to challenge or question the validations from these philosophy professors, fostering an open dialogue.

1. Logical Arguments:
1. Expertise Principle: Philosophy professors who have taught formal logic have acquired expert knowledge, making them well-suited to evaluate logical coherence in arguments.
2. Supporting Evidence (data, studies):
1. Studies on expertise suggest that experts, due to their training and experience, have deeper knowledge and insights in their areas of specialization (Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: evidence of maximal adaptation to task constraints. Annual review of psychology, 47(1), 273-305).
3. Supporting Books:
1. "Thinking Fast and Slow" by Daniel Kahneman: This book, while not directly related to philosophy professors, discusses the differences between expert thinking and intuitive thinking.
4. Supporting Videos (movies, YouTube, TikTok):
1. "Crash Course Philosophy" on YouTube: A video series that provides an introduction to philosophy and logical reasoning.
5. Supporting Organizations and their Websites:
1. The American Philosophical Association (apaonline.org): An organization supporting the work of philosophers and the value of their expertise.
6. Supporting Podcasts:
1. "Philosophy Bites" is a podcast that showcases the insights of contemporary philosophers on a wide range of topics.
7. Unbiased Experts:
1. Professors of Philosophy who have taught formal logic, as their training ideally positions them to be impartial arbiters of logical consistency.
8. Benefits of Belief Acceptance (ranked by Maslow categories):
1. Psychological Needs: Encourages intellectual growth and cognitive satisfaction through engaging with logically sound arguments.
2. Belonging and Love Needs: Facilitates fair and meaningful dialogue, promoting a sense of community.
3. Esteem Needs: Upholds the value of academic knowledge and expertise, contributing to societal respect for intellectual pursuits.
4. Self-Actualization: Supports the pursuit of truth and understanding, key aspects of personal and societal development.

Other Factors: Logic Professors

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.

Other Factors: Books that agree or Disagree

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

Other Factors: Up/Down Votes

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.

Put your money where your BRAIN is: how money could be used to help weigh the validity of a belief

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 in a belief, +1/2 the money invested in beliefs that agree with this belief, etc – Money invested against this belief, -1/2 the money invested in beliefs that disagree with this belief, etc
• M_dn/n: This equation does the same as above but subtracts the total amount of money invested in arguments that disagree with it.
• TM = Total Money invested in the forum
• #B = number of beliefs
• The average amount of money invested in an idea = TM / #B. The goal of this idea is to assign 1 point for the average belief, and 2 points for a belief that has twice the average amount of money invested.
• The assumption is that people would be able to purchase “stock” in a belief at its idea score. They would purchase it assuming that the idea score was going to go up. We would have to set the transaction fee high enough, to ensure that we don’t lose money, and only smart people are making money. We would also only sell stock in relatively stable ideas. 

The code for an application of my equation in SQL: 

Coming soon! As soon as I learn SQL... Please help by contributing to my open source google project: http://code.google.com/p/ideastockexchange/

Your equation in math:

Do you have a better equation that would use people's aversion to part with their money, that would promote good arguments, and beliefs? Leave a comment, or a link, and I will link to your project. I don't need the credit, I just don't want to live in a world of non-structured beliefs, and conclusions that people don't even try to support in an intelligent manor. Is money the answer? No, but it could help people try to really evaluate the true strength of a conclusion, if they felt that that conclusion would go up in value based on its truth strength... Do you disagree? Leave a comment. 

Put your money where your BRAIN is: how money could be used to help weigh the validity of a belief

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 in a belief, +1/2 the money invested in beliefs that agree with this belief, etc – Money invested against this belief, -1/2 the money invested in beliefs that disagree with this belief, etc
• M_dn/n: This equation does the same as above but subtracts the total amount of money invested in arguments that disagree with it.
• TM = Total Money invested in the forum
• #B = number of beliefs
• The average amount of money invested in an idea = TM / #B. The goal of this idea is to assign 1 point for the average belief, and 2 points for a belief that has twice the average amount of money invested.
• The assumption is that people would be able to purchase “stock” in a belief at its idea score. They would purchase it assuming that the idea score was going to go up. We would have to set the transaction fee high enough, to ensure that we don’t lose money, and only smart people are making money. We would also only sell stock in relatively stable ideas. 

The code for an application of my equation in SQL: 

Coming soon! As soon as I learn SQL... Please help by contributing to my open source google project: http://code.google.com/p/ideastockexchange/

Your equation in math:

Do you have a better equation that would use people's aversion to part with their money, that would promote good arguments, and beliefs? Leave a comment, or a link, and I will link to your project. I don't need the credit, I just don't want to live in a world of non-structured beliefs, and conclusions that people don't even try to support in an intelligent manor. Is money the answer? No, but it could help people try to really evaluate the true strength of a conclusion, if they felt that that conclusion would go up in value based on its truth strength... Do you disagree? Leave a comment. 

"My life story": Alta Lealette, Anderson Laub.

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 after arriving, they were called to go to Circle Valley to help build up that area. And that is where my mother, Martha Caroline Thomander was born 12th of June 1866.

Because of the Indian troubles the Saint of Circle Valley, Piute County were recalled to Ephraim and it was there eight of the 10 children of my parents were born:

• · 14th of April 1887, James IRA. 
• · 1st of November 1888, James (still born); 
• · 9th of February 1890. Lucretia; 
• · 8th of July 1892, Drusilla Naomi; 
• · 13th of September 1894, Ada Beulah, 
• · 2nd of September 1896, Hugh Preston, 
• · 2nd of May 1899 Onedia May 
• · 26 Nov 1902, Luella Theora; 

My family moved to spring to Spring Glen in 1904. Spring Glen is a small picturesque town situated on the bench land east of the Rail Road and Highway that connect Helper and Price, in Carbon County Utah. It was given its name because of the lovely green and inviting appearance in the spring of the year. To this community my parents made their way a few months before my birth 21 July 1905. I was given the name of Alta Lealette, the second name being that of my oldest sister’s music teacher. I was never called Alta or Lealette by my family, but called Lea or Leah, have been known as Alta on most of my Church records. I was named or Christened by Thomas Rhodes, presiding Elder of the Spring Glen Branch of the Latter-day Saints Church, when I was 12 days old 3 August 1905.

When Work Is Punished: The Tragedy Of America's Welfare State

http://www.zerohedge.com/news/2012-11-27/when-work-punished-tragedy-americas-welfare-state

Exactly two years ago, some of the more politically biased progressive media outlets (who are quite adept at creating and taking down their own strawmen arguments, if not quite as adept at using an abacus, let alone a calculator) took offense at our article "In Entitlement America, The Head Of A Household Of Four Making Minimum Wage Has More Disposable Income Than A Family Making $60,000 A Year." In it we merely explained what has become the painful reality in America: for increasingly more it is now more lucrative - in the form of actual disposable income - to sit, do nothing, and collect various welfare entitlements, than to work. This is graphically, and very painfully confirmed, in the below chart from Gary Alexander, Secretary of Public Welfare, Commonwealth of Pennsylvania (a state best known for its broke capital Harrisburg). As quantitied, and explained by Alexander, "the single mom is better off earnings gross income of $29,000 with $57,327 in net income & benefits than to earn gross income of $69,000 with net income and benefits of $57,045." realize that this is a painful topic in a country in which the issue of welfare benefits, and cutting (or not) the spending side of the fiscal cliff, have become the two most sensitive social topics. Alas, none of that changes the matrix of incentives for most Americans who find themselves in a comparable situation: either being on the left side of minimum US wage, and relying on benefits, or move to the right side at far greater personal investment of work, and energy, and... have the same disposable income at the end of the day.

Naturally, the topic of wealth redistribution is paramount one now that America is entering the terminal phase of its out of control spending, and whose response to hike taxes in a globalized, easily fungible world, will merely force more of the uber-wealthy to find offshore tax jurisdictions, avoid US taxation altogether, and thus result to even lower budget revenues for the US. It explains why the cluelessly incompetent but supposedly impartial Congressional Budget Office just released a key paper titled "Share of Returns Filed by Low- and Moderate-Income Workers, by Marginal Tax Rate, Under 2012 Law" which carries a chart of disposable income by net income comparable to the one above.

ut perhaps the scariest chart in the entire presentation is the following summarizing the unsustainable welfare burden on current taxpayers:

• For every 1.65 employed persons in the private sector, 1 person receives welfare assistance
• For every 1.25 employed persons in the private sector, 1 person receives welfare assistance or works for the government.

The punchline: 110 million privately employed workers; 88 million welfare recipients and government workers and rising rapidly.

And since nothing has changed in the past two years, and in fact the situation has gotten progressively (pardon the pun) worse, here is our conclusion on this topic from two years ago:

We have been writing for over a year, how the very top of America's social order steals from the middle class each and every day. Now we finally know that the very bottom of the entitlement food chain also makes out like a bandit compared to that idiot American who actually works and pays their taxes. One can only also hope that in addition to seeing their disposable income be eaten away by a kleptocratic entitlement state, that the disappearing middle class is also selling off its weaponry. Because if it isn't, and if it finally decides it has had enough, the outcome will not be surprising at all: it will be the same old that has occurred in virtually every revolution in the history of the world to date.

But for now, just stick head in sand, and pretend all is good. Self-deception is now the only thing left for the entire insolvent entitlement-addicted world.

* * *

Full must read presentation: "Welfare's Failure and the Solution"

The article, "When Work Is Punished: The Tragedy of America's Welfare State," published on ZeroHedge, discusses the dichotomy between welfare benefits and employment in America. It asserts that for many Americans, it's more lucrative to stay unemployed and collect welfare entitlements than to work. The article refers to an illustrative chart from Gary Alexander, Secretary of Public Welfare, Commonwealth of Pennsylvania, indicating that a single mother earning a gross income of $29,000 with welfare benefits has more disposable income than if she earned a gross income of $69,000.

Here's an outline for this argument:

a) Fundamental beliefs or principles one must reject to also reject this belief: 

- The current welfare system is fair and doesn't disincentivize work.

- All welfare recipients are in desperate need and cannot survive without assistance.

b) Alternate expressions(e.g., metatags, mottos, hashtags):

- #WelfareStateTragedy

- #WorkVsWelfare

- #WelfareIncentive

c) Objective criteria to measure the strength of this belief:

- Comparison of disposable income of working individuals and those on welfare.

- Number of people who choose not to work due to more advantageous welfare benefits.

- Studies showcasing welfare system exploitation.

d) Shared interests between those who agree/disagree:

- Both sides typically want a fair system that supports those in need without creating a disincentive to work.

- Everyone wants a thriving economy.

e) Key opposing interests between those who agree/disagree (that must be addressed for mutual understanding):

- Differing views on the role of government and welfare.

- Disagreements over the extent of welfare abuse or the incentive it creates to not work.

  

f) Solutions:

- Welfare reform that balances the need for social safety nets and incentivizing work.

- More rigorous checking mechanisms for welfare eligibility.

  

g) Strategies for encouraging commitment to a resolution to evidence-based solutions:

- Public awareness campaigns about the unintended consequences of welfare exploitation.

- Legislative advocacy for welfare reform.

- Constructive dialogue between differing viewpoints to foster understanding and compromise.

Sure, here are some supporting elements for the belief expressed in the article:

1) Logical arguments:

   - The financial incentive argument: If welfare benefits result in higher disposable income than working, there is a logical financial incentive to remain on welfare rather than seek employment.

   

2) Supporting evidence (data, studies):

   - The article refers to data from Gary Alexander, Secretary of Public Welfare, Commonwealth of Pennsylvania. More research and studies would be needed for robust evidence.

3) Supporting books:

   - "Losing Ground: American Social Policy, 1950-1980" by Charles Murray discusses some of the negative impacts of welfare.

4) Supporting videos (movies, YouTube, TikTok):

   - Various YouTube videos and documentaries discuss the impacts of welfare on work incentives, although specific examples would need to be sought out.

5) Supporting organizations and their Websites:

   - Heritage Foundation, a conservative think tank, often publishes reports and articles discussing the drawbacks of the current welfare system.

   

6) Supporting podcasts:

   - "The Daily Signal" is a podcast from the Heritage Foundation that often discusses topics related to welfare and work incentives.

7) Unbiased experts:

   - Economists, sociologists, and public policy experts could provide unbiased analysis, but specific names would depend on the research and analysis they've conducted on the topic.

   

8) Benefits of belief acceptance (ranked by Maslow categories):

   - Economic (Basic needs): Reforming welfare to encourage employment could potentially lead to improved economic outcomes for individuals and society as a whole.

   - Esteem (Psychological needs): Individuals moving from welfare to work may experience improved self-esteem and sense of accomplishment.

   - Self-Actualization (Self-fulfillment needs): People might achieve more personal and professional growth through employment as compared to long-term welfare reliance.