The main Algorithm

Abstract 

I propose that we build the SQL code that would facilitate an online forum. This forum would use a relational database to track reasons to agree and disagree with conclusions. It would also allow you to submit a belief as a reason to support another belief (see image 1 below): 

Figure 1: Arguments used to support other arguments

Arguments are currently made on websites, in books, and even in videos and songs. It would be powerful to outline all the arguments that agree or disagree with a conclusion and put them on the same page as seen below:



Figure 2: Arguments go from websites, books, songs, videos, into a relational database and are presented with their structure

Having the structure of how all these arguments are used to support each other, could allow us to automatically strengthen or weaken a conclusion's score based on the score of their assumptions.

The purpose of the Idea Stock Exchange is to find ways to give conclusions scores based on the quality and quantity of reasons to agree or disagree with them with an open sourced SQL database.

Pros and Cons are a tried and true method to evaluate a conclusion

Many people, including Thomas Jefferson and Benjamin Franklin advocated making a list of pros and cons, to help them make decisions. The assumption is that the quantity and quality of the reasons to agree or disagree with a proposed conclusion has some bearing as to underlining strength of that conclusion. I wholeheartedly agree. 

No one has yet harnessed the power of Pros and Cons in the information age. We can.

However, now that we have the internet, we can crowd source the brainstorming of reasons to agree or disagree with a conclusion.

The only trick is how do you evaluate the strength of each pro or con? Many people suggest putting the strongest pros or cons at the top of the list. Also, if we had enough time we might make a separate list FOR each pro or con.

For instance, FDR had to decide if we should join WWII or not. One pro might be that the German leaders were bad. There were many reasons to support this belief, and this belief was used to support another belief.

Not very many people have enough time to do a pro or con list for each pro or con. But on the internet we keep making the same arguments over and over again. For thousands of years we have been repeating the same arguments that Aristotle and Homer have made. Most of our arguments have been made thousands or millions of times. However no one has ever taken the time to put them into a database, and outline how they relate to each other. We can change this.

I propose that we find algorithms that attempt to promote good conclusions and arguments. This simplest and best method of scoring conclusions is to counting the number of reasons to agree, and subtracting the number of reasons that disagree. Because some arguments are better than other arguments, we should repeat this process for every argument until we reach verifiable data. The following equation represents this plan:

·         n = number of “steps” the current arguments is removed from conclusion

We can use algebra to represent each term, and make it look a little more mathematical, with the below formula:

·         n:                     Number of “steps” the current arguments is removed from conclusion

·         A_(n,i)/n:             When n=1 we are looking at arguments that are used directly to support or oppose a conclusion. The 2^ndsubscript is “i”. This is used to indicate that we total all the reasons to agree. So when n=1, we could have 5 “i’s” indicating there are 5 reasons to agree. These would be labeled A_(1,1), A_(1,2), A_(1,3), A_(1,4), and A_(1,5). N on the bottom indicates that reasons to agree with reasons to agree only contribute ½ a point to the overall conclusion. Thus reasons to agree with reasons to agree with reasons to agree would only contribute 1/3 of a point, and so on. If we decide to make the bottom of the equation n x 2, then these would contribute 1/6 of a point. It is obvious that some of their score should contribute to the conclusion scores, because weakening an assumption should automatically weaken all the conclusions built on that assumption. We could continually update n to give reasonable result, or each website could use its own secret sauce. 

·         D_(n,j)/n              Ds are reasons to disagree, and work the same as As but the number of reasons to disagree, are subtracted from the conclusion score. Therefore, if you have more reasons to disagree, you will have a negative score.  “J” is used, just to indicate that each reason is independent of the other.

·         The denominator is the total number of reasons to agree or disagree. This normalizes the equation, resulting the conclusion score (CS) representing the total percentage of reasons that agree. The conclusion score will range between -100% and 100% (or -1 and +1)

The above equation would work very well, if people submitted arguments that they honestly felt supported or opposed conclusions. We could probably find informal ways of making this work, similar to how Wikipedia trusts people, and has a team of editors to ensure quality. However, we could also introduce formal ways to discourage people from using bad logic.

For instance, people could submit that the “grass is green” as a reason to support the conclusion that we should legalize drugs. The belief that the grass is green, will have some good reasons to support it, and may have a high score. At first, to avoid this problem, I would just have editors remove bad faith arguments. But a formalized process would be to have for each argument a linkage score, between -1 and +1 that gets multiplied by the argument’s score that represents the percentage of that argument’s points that should be given to the conclusions points.

I believe the most elegant way to come up with a linkage score would be to just make a new argument, that “a” supports “b”, with all the normal reasons to agree and disagree. However, I also propose the percentage of up-votes compared to the percentage of down-votes and other good idea promoting algorithms below.

Also, without editors, you would run into the problem of duplication. If we had this at the time of the Gulf Wars, people could have been submitting the belief that Saddam Hussein was a bad person as a reason to support the belief that we should go to war. People would submit the belief that we don’t go to war with everyone who is bad, as a way of weakening the linkage between this conclusion and argument. But someone might also submit the belief that he was “evil”. How much is the world “evil” and “bad” the same thing? Is Evil just a worse kind of bad? These questions could be quantified, if for each argument, we brainstormed a list of “other ways of saying the same thing”. Of course we would use all of our algorithms to determine to what degree they are the same thing. If we determine that two items are 85% the same thing, then when both of them are used as reasons to support the same thing, then they would only count as 1.15x their two scores, not 2x.

Examples

We might be arguing the conclusion that “It was good for us to join WWII.” Someone may submit the argument that “Nazis were doing bad things” as a reason to support the conclusion about entering the war. The belief that Nazis were doing bad things might already have a score. Let’s suppose that this idea score has a high ranking of 99%. This might be awarded a linkage score of 90% (as a reason to support the conclusion that we should have gone to WWII).  In this situation it would contribute 0.495 points (0.99 X 0.5) to the conclusion score for the beliefs that “It was good for us to join WWII”. Someone else might submit a belief that “Nazis were submitting wide scale systematic genocide” as a reason to support the belief that “It was good for us to go to WWII”. Because we don’t go to war with every country that “does bad things”, we would assume that this linkage score would be higher, perhaps a 98%.

For example the belief that Nazi Germany leaders were evil, is a belief with many argument to support it. However it can also be used as an argument to support other conclusions, such as the belief that it was good of us to join WWII.

Assumptions

·         Reason ≝ Belief used to support another belief(For example the belief that Nazi Germany leaders were evil, is a belief with many argument to support it. However it can also be used as an argument to support other conclusions, such as the belief that it was good of us to join WWII).

·         Good Belief ≝ Good Reasons to Agree > Good Reasons to disagree

·         Bad Belief ≝ Good Reasons to Agree > Good Reasons to disagree

·         Great Belief ≝ Good Reasons to Agree >> Good Reasons to disagree

·         Terrible Belief≝ Good Reasons to Agree << Good Reasons to disagree

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.