There are many things web designers can do to help people resolve their conflicts +4.16

Reasons to agree:

1. It would help us move towards understanding if web forum designers rewarded those who can demonstrate that they understand those with whom they disagree with. 
1. There are many ways discussion forum designers can reward those who demonstrate that they understand those whom they disagree with.
1. Web-designers could test users ability to properly identify similar concepts, from multiple choice options.
1. Perhaps people who have their comments evaluated could have special consideration in evaluating weather or not the person who disagreed got their statement right. 
2. Maybe before you disagree with someone you have to put into your own words exactly which part you disagreed with. You could do this by highlighting or bolding the part that you disagree with. 
2. Web designers would help online debate if they created web forums that allowed users to identify specifically which portions of text they agree and disagree with. 
1. Not identifying exactly which portion you disagree with results in confusion.
2. Psychologist could help out in this section. 

Score:

# of reasons to agree: +2

# of reasons to disagree: -0

# of reasons to agree with reasons to agree: +3/2+2/4+1/6=2.16

# of reasons to agree with reasons to disagree: -0

Total Idea Score: +4.16

Reframing Online Debates for Constructive Dialogues

It's essential to restructure online debates to ensure that reasons supporting and opposing a belief coexist on the same platform. True understanding and resolution in any debate come not from overlooking the counterarguments but from directly engaging with them.

Ignoring an opponent's perspectives and data is akin to navigating a debate with blinders on. It limits the depth of the discussion and often leads to an echo chamber effect, where one's own beliefs are amplified without challenge, stunting intellectual growth and understanding. 

Constructive debates require acknowledging and addressing the full spectrum of views, which is why having reasons to agree and disagree presented together is crucial. This approach fosters a more holistic and nuanced understanding of issues, allowing participants to weigh different viewpoints fairly and make more informed decisions.

By structuring online debates in this way, we encourage not just the exchange of ideas but the cultivation of respect, empathy, and a genuine quest for truth.

If we entered our beliefs and arguments into databases, there are many features of relational databases that could help us come to better conclusions

1. If our beliefs and arguments were entered into a relational databases, we could: 
1. tag arguments as either a reason to agree or disagree with a particular belief. This would be beneficial because: 
1. We could post the results so that reasons to agree or disagree with a conclusion would be on the same webpage.
2. It would be beneficial to have all the reasons to agree and disagree with a belief on the same page.  
2. assign scores to arguments
3. assign scores to beliefs, based on the score of the arguments for and against the beliefs
4. assign scores to beliefs, based on other beliefs that are used to support or oppose them. For instance the belief that the middle class should get a tax break, has many reasons to agree or disagree with it, and it can also be used as a reasons to support or oppose other beliefs, like the belief that we should support politicians who agree or disagree with a middle class tax cut. 
5. tag them with intelligent meta data, to allow computers to help organize the argument for us. 

We need to back up our beliefs with clear logic and well found reasoning

Reasons or arguments people use to agree:

1. Evidence-free metaphysical speculations or politicized wish-fulfillment fantasies will destroy us.
1. We can't just adopt socialism because it makes us feel good, without first knowing that it will work, and that it won't put our good freedom loving nice guys at a disadvantage in competition with non freedom loving dictators. 
2. Bertrand Russell was right when he said. "It is undesirable to believe a proposition when there is no ground whatsoever for supposing it is true."
3. When you make an assumption you make an ass out of you and me. 
4.  If we don't use good logic to make our arguments, we will come to bad decisions. 
5. If we want to survive as a species, we need to make good decisions. 
6. Our beliefs affect our happiness
1. If you want to enjoy your life, you should spend your time on rewarding activities. 
7. Our beliefs affect our actions.
8. Our beefs affect our personal success
1. If believe it is important to not be seen as a a nerd, and we believe nerds are well educated, we will not want to be well educated. Your chances for success will be improved with education.

Our conclusions and reasons to coming to them are all tied together in complex nonlinear ways similar to a relationship database

1. Our conclusions have many reasons to agree and disagree with them and each of these beliefs has many reasons to agree and disagree with them. As these arguments branch out and arguments multiply, it becomes too much for our brains to handle all at once.
2. Assumptions are beliefs that are used to support other beliefs. If you change one assumption, it will change the strength of each conclusion that builds on that assumption. In a relational database you can say 5 people live together, then when you change one person's address, it can change all of their addresses. In a similar way, if we strengthen or weaken any assumption in a relational database, it will strengthen or weaken all of the conclusions that are based on these assumptions. Defining all these relationships is the only way we can ever make any progress at weighing all the data that we have.   

We should crowdsource a database of beliefs and the arguments people use to support them.

Reasons or arguments people use to agree:

1. Our beliefs should be backed by sound logic. Score: 9
2. A relational database outlining our beliefs can be built cost-effectively.
3. Entering beliefs and arguments into databases allows us to leverage relational database features to reach better conclusions.
4. If we can sequence millions of lines of human DNA, organizing our thoughts and beliefs should be achievable.
5. Sequencing the human genome requires advanced scientific methods, but outlining beliefs only needs a database.
6. Using a relational database to associate arguments with the beliefs they support enables the creation of a scoring system that evaluates the validity of people's arguments and the cumulative validity of their belief

1. Other ways of saying the same thing:

• We should create a collaborative database for beliefs and their supporting arguments.
• A crowdsourced repository for beliefs and their justifications should be developed.

2. The best Assumption that must also be accepted if we accept this belief:

• People are willing to contribute and engage with a database that outlines beliefs and their supporting arguments.

3. The best Assumption that must also be rejected if we reject this belief:

• A database of beliefs and arguments cannot effectively improve our understanding and evaluation of different perspectives.

4. The most compelling reason to agree with the belief:

• A crowdsourced database can help identify logical inconsistencies, improve critical thinking, and foster constructive discussions.

5. The most compelling reason to disagree with the belief:

• The potential for biased or misleading arguments, manipulation of data, and the challenge of maintaining accurate, unbiased information.

6. The most likely Benefit of accepting the belief above:

• Enhanced understanding of diverse perspectives, leading to better-informed decisions and more effective problem-solving.

7. The most likely Cost of accepting the belief above:

• Time, effort, and resources required to build, maintain, and moderate the database to ensure its reliability and usefulness.

8. The best Book that can be said to support this belief:

• "The Wisdom of Crowds" by James Surowiecki

9. The best Book that can be said to oppose this belief:

• "The Filter Bubble" by Eli Pariser

10. The most well-known, unbiased, and educated People supporting this belief:

• Jimmy Wales (co-founder of Wikipedia)
• Clay Shirky (author and educator on the social and economic effects of Internet technologies)

11. The most well-known, unbiased, and educated People opposing this belief:

• Jaron Lanier (computer scientist, virtual reality pioneer, and author critical of the digital economy's impact on society)
• Evgeny Morozov (scholar and writer known for his criticisms of the Internet's impact on society and politics)

We should crowd source a database of things that people believe and arguments they use

Reasons or arguments people use to agree:

1. We need to back up our beliefs with good logic  Score: 9
2. We can build a relational database that outlines our beliefs relatively cheaply 
3. If we entered our beliefs and arguments into databases, there are many features of relational databases  that could help us come to better conclusions. 
4. If we can sequence millions of lines of Human DNA, you would think that we could organize our thoughts and beliefs. 
5. You need advanced scientific methods to sequence the human genome, but all you need is a database to outline the things people believe.
6. If you use a relational database to associate arguments with the beliefs they support, you could design a scoring system  that analyze the validity people's arguments, and then the cumulative validity of their beliefs. 

A relational database is the best way of outlining our beliefs

Reasons or arguments people use to agree:

1. Our beliefs are are all all tied together in nonlinear ways similar to a relationship database. 

Other good idea promoting algorithms: laws

I believe that tallying the number of laws that agree or disagree with a belief can serve as a measure of how strongly society deems something to be wrong.

For example, every society considers murder to be wrong and typically addresses it through some form of criminal justice system.

To quantify this, we can assign scores to conclusions based on the number of laws supporting a belief (e.g., murder is wrong) and the quality of arguments linking a law to that belief. Factors to consider could include the relationship score between the belief and the law, the severity of punishment for violating the law, and the relative number of laws that agree or disagree with the belief or any of its supporting arguments. This can be achieved by creating an equation and implementing it in software.

A comprehensive algorithm could be designed to account for all these factors in the following manner:

Law Score = Sum of scores for (Laws that agree - Laws that disagree) * Linkage Strength between law and the belief (the score of the arguments that the law is truly established on the belief) * Punishment Sevarity

We can represent the relationship between laws and beliefs more concisely using algebra and the following definitions:

Definitions:

• LAn/LDn: Laws that are argued to agree (LAn) or disagree (LDn) with a conclusion.
• LAn+LDn: Total number of laws submitted in this forum as reasons to agree or disagree with a conclusion. To normalize this factor and ensure it carries the appropriate weight, we could tweak multiplication factors or allow users to adjust them.
• LSn: Linkage Score - The linkage would be treated as its own argument, with reasons to agree or disagree, and a score ranging from -1 to 1. A negative score indicates a law that contradicts the intended suggestion, 0 means the law has no relation, and 1 signifies that the law fully supports the intended conclusion.
• Psn: Punishment Severity - This factor considers the classification of the offense (felony or misdemeanor) and the typical punishment duration (e.g., years of imprisonment).

By applying these definitions, we can create a comprehensive algorithm that takes into account various factors to determine the strength of the relationship between laws and beliefs.

Examples: Should the burqa be required or banned?

For instance, the fact that nearly all countries outlaw "murder of innocent adults" indicates the level of validity that most societies attribute to this belief. In some cases, laws might disagree and agree on controversial topics. For example, there are countries that both ban and require women to wear burqas. To measure society's opinion on whether it is wrong to wear a burqa, one could compare the number of countries that ban them (e.g., France) and the number of countries that require them (e.g., Afghanistan, Saudi Arabia). Depending on which side is being supported, the percentage of countries banning the burqa could be added or subtracted from the total number of countries with relevant laws.

Examples: Is shooting an intruder a justifiable action that protects law-abiding citizens, or is it an immoral act that ends a life prematurely?

In this case, one could consider the percentage of states that deem it wrong to shoot an intruder as evidence supporting the belief that it is morally wrong to do so.

Other good idea promoting algorithms: laws

I believe that we can count the number of laws that agree or disagree with a belief, as a way of measuring how much  society believes something is wrong.

For example every society believes that murder is wrong, and often punishes it with some sort of criminal justice program.

A way of quantifying this so that you can give scores to conclusions based on the quantity of laws that are said to support a belief (like murder is bad) and the quality of arguments that a law supports a certain belief about a behavior being bad, the relationship score between the belief and the law, the severity of punishment for breaking the law, and the relative number of laws that can be said to agree or disagree with the belief, or any of the supporting arguments, would be to make an equation and build it in software.

A way of counting all of this with a powerful algorithm could be expressed this way:

Or we could represent the math more simply by substituting algebra, with the following definitions:

Definitions:

·         L_An/L_Dn: Laws that are argued to agree or disagree or disagree with a conclusion

·         L_An+L_Dn::Number of laws submitted in this forum as reasons to agree or disagree with a conclusion. I’m just trying to find some way of normalizing this factor, or weigh it, so that it doesn’t carry too much or too little weight. Obviously, like any other factor on this forum, we could tweak multiplication factors, or allow users to tweak them.

·         LS_n: Linkage Score: The linkage would become its own argument, with reasons to agree, and a score between -1 and 1. A negative score would be a law that actually makes a counter argument to the intended suggestion, 0 has no relation, and 1 fully supports the intended conclusion.

·         P_sn: Punishment severity. For instance is the punishment a felony or a misdemeanor. How many years of prison are people typically punished.  

Examples: Is the Burqa so important that it should be required, or so bad it should be banned?

For example, the fact that almost all countries outlaw “murder of innocent adults” represents the amount of validity that most societies attribute to a belief. It may be rare that you would have laws that disagree and agree on non controversial topics. However, there are countries that ban and require women to wear Burqas. A way of measuring if mankind thinks it is wrong to wear the burqa would be to take the number of countries that ban them (France, etc) and subtract the number of countries that require them (Afghanistan, Saudia Arabia, etc).  Depending on what side of this is used to support, you would subtract or add the percentage of countries that ban it compared to the total number of countries that have laws on the use of an item.

Examples: Is shooting an intruder a good activity that helps protect law followers, or is it a bad activity that ends a life too soon

You could add the percentage of states that say it is wrong to shoot an intruder, as evidence to support the belief that is wrong to do this.

Scoring Beliefs and Conclusions: An Algorithmic Approach for an Online Forum

This online forum employs a relational database to catalog reasons supporting or opposing various conclusions. It allows users to submit a belief as evidence to back another belief (Refer to Figure #1). According to Equation #1, a conclusion receives a score derived from the scores of its underpinning assumptions. Similarly, assumption scores are calculated based on their corresponding assumptions, until we reach verifiable data.

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.

In this equation, each element (reason, evidence, investment, etc.) is first multiplied by its respective Linkage Score (LS) to represent the strength of the association with the conclusion, and then it contributes to the overall conclusion score (CS) in proportion to its importance weight (RIW, EIW, IIW, etc.). The score for each type is calculated as the difference between the scores for supporting and opposing elements.

Assistance Needed!
I'm seeking feedback on the clarity and feasibility of this concept. If there are any areas of confusion, or if the mathematical notation could be improved, I'd greatly appreciate suggestions.

Assumptions:
For instance, consider the belief that the leaders of Nazi Germany were evil. This belief is bolstered by many arguments, yet it can also serve as an argument itself, supporting other conclusions such as the idea that it was justifiable for the US to join WWII.

· Numerator: The numerator is obtained by subtracting the count of unique reasons to disagree from the count of unique reasons to agree. Consequently, if the "valid" reasons to agree are fewer, the numerator will be negative.

· Denominator: The denominator comprises the total count of reasons, both for and against. This computation normalizes the equation, rendering the conclusion score (CS) as the overall percentage of agreeable reasons. Therefore, the conclusion score will fluctuate between -100% and 100% (or -1 and +1).

· Definitions:
- A: Represents unique arguments that agree with a conclusion.
- D: Represents unique arguments that disagree with a conclusion.
- L: Stands for Linkage Score, a number between zero and 1. This metric quantifies the strength with which an argument is purported to support a conclusion.

· Unique Arguments: Every belief would have a template that enables the proposal of a statement that articulates the same idea more effectively. This statement would become a new argument with its unique conclusion score. For instance, if the rephrased belief gets a similarity rating of 98%, then it would contribute just 2% of its score to the new conclusion.

· For n = 1: Arguments like A(1,1) and A(1,2) are the first and second reasons to agree, respectively. Each contributes one point to the conclusion score, with the contribution moderated by the L (linkage score) multiplier.

· For n = 2: Arguments such as A(2,1) and A(2,2) are the first and second reasons to agree, where n equals 2. These could either be reasons agreeing with a reason to agree or reasons disagreeing with a reason to disagree. Each of these contributes half a point to the conclusion score due to the equation's design. Modifying the equation could change these contributions to a quarter point each to the conclusion score. This contribution is crucial since weakening an assumption logically weakens all conclusions built upon it. This value of n could be iteratively updated for reasonable results or uniquely determined by each platform. Note that D(n,j) represents reasons to disagree, and they function similarly. Here, j instead of i is used to indicate their independence. As such, second-order reasons to disagree could include reasons disagreeing with reasons to agree or reasons agreeing with reasons to disagree.

· L = Linkage Score: Let's consider the conclusion, "It was good for us to join WWII." An argument submitted could be, "Nazis were doing bad things," to support the conclusion. If this belief already has a high score of, say, 99%, it could be granted a linkage score of 90% towards supporting the conclusion. As a result, it would contribute 0.495 points (0.99 X 0.5) to the conclusion score for the belief, "It was good for us to join WWII". Another belief submitted could be, "Nazis were committing wide-scale systematic genocide," supporting the conclusion. Given that not all countries that "do bad things" justify a war, this linkage score could potentially be higher, perhaps 98%.

Investment-Based Scoring:

M = Money invested in a belief
TM = Total Money invested in the forum
#B = Number of beliefs

The average amount of money invested in an idea is computed as TM / #B. The aim of this metric is to assign 1 point for an idea with an average investment, and 2 points for a belief with double the average investment.

This concept is predicated on the idea of users being able to purchase “stock” in a belief based on its idea score, with the expectation that the idea score would rise. Transaction fees would be set high enough to prevent financial losses and ensure that only those with sound judgment profit from this mechanism. Additionally, stocks would only be sold for ideas that are relatively stable.

Certified Logic Instructors

• NP: Represents the number of times a certified** logic instructor has validated or invalidated the logic of a reason to disagree. By summing "ns", we can account for instances where a logic professor disagrees with a specific belief.

In our context, a "certified" logic instructor is defined as someone possessing a ".edu" email address associated with the philosophy department of an accredited university. This stipulation helps ensure that the individuals evaluating the logic of arguments have a credible academic background in the field of philosophy.

Book References

• B: Denotes books that support or oppose the given conclusion.
• BS: Book Score - takes into account factors like the number of books sold, as well as reviews and ratings of the books.
• BLS: Book Link Score - It's possible to have a well-regarded book that doesn't necessarily support the proposed belief. Each argument suggesting that a book supports a belief becomes its own claim with its own book "linkage score" that is calculated according to the above formula.

Voting System

• UV/DV: Upvotes or Downvotes.
• #U: Number of Users.
  We'll use an overall upvote or downvote system, in addition to votes on specific attributes like logic, clarity, originality, verifiability, and accuracy, among others.

Additional Evidence Sources

Movies, songs, expert opinions, and other sources of evidence can also support or oppose different conclusions, similarly to books. For example, movies - often documentaries - could be evaluated based on scores from sites like Rotten Tomatoes. This data could be imported, along with formal logical arguments that a movie attempts to support or oppose a belief.

**Certification for logic instructors is assumed to be from a reputable institution or organization. The exact criteria for certification can be defined based on the requirements of the forum.

Link Score (L)

When users submit beliefs as reasons to support other beliefs, they may occasionally attempt to forge a link where one doesn't truly exist. For instance, someone might submit the belief "The grass is green" as a reason to support the conclusion "The NY Giants will win the Super Bowl". Although the belief that "the grass is green" might receive a high score for its general acceptance, the "Link Score" in this context would be near zero due to its irrelevance to the conclusion.

• As we refine this system, we might have to apply multiplication factors to some elements to ensure that they do not carry too much or too little weight in the overall scoring.