I think the probability is close to zero because trying to "drill down" to force agreement between people results in fights, not in agreement.
We are not in agreement here! Do you think its possible to discuss this and have one or both of us change our initial stance or will that attempt merely result in a fight? Note, i am sure it is possible to result in a fight but i do not think its a forgone conclusion. On the contrary, i think most worthwhile points of view were formed by hearing one or more opposing views on the topic.
they will each support their own position by reasons which are effective for them but not for the other person
Why must that be the case? On a shallow level it may seem so but i think if you delve deep, you can find a best case solution. Can you give an example where two people must fundamentally disagree? I suspect any example you come up with will have a "lower level" solution where they will find it is not in their best interest. I recognize that the hidden premise on my thinking that agreement is always possible, stems from the idea that we are all trying to reach a certain goal and a true(er) map of reality helps us get there and cooperation is the best long term strategy.
I agree that we are not in agreement. And I do think that if we continue to respond to each other indefinitely, or until we agree, it will probably result in a fight. I admit that is not guaranteed, and there have been times when people that I disagree with changed their minds, and times when I did, and times when both of us did. But those cases have been in the minority.
"We are all trying to reach a certain goal and a truer map of reality helps us get there..." The problem is that people are interested in different goals and a truer map of reali...
I posted before about an open source decision making web site I am working on called WikiLogic. The site has a 2 minute explanatory animation if you are interested. I wont repeat myself but the tl;dr is that it will follow the Wikipedia model of allowing everyone to collaborate on a giant connected database of arguments where previously established claims can be used as supporting evidence for new claims.
The raw deduction element of it works fine and would be great in a perfect world where such a thing as absolute truths existed, however in reality we normally have to deal with claims that are just the most probable. My program allows opposing claims to be connected and then evidence to be gathered for each. The evidence will create a probability of it being correct and which ever is highest, gets marked as best answer. Principles such as Occams Razor are applied automatically as long list of claims used as evidence will be less likely as each claim will have its own likelihood which will dilute its strength.
However, my only qualification in this area is my passion and I am hitting a wall with some basic questions. I am not sure if this is the correct place to get help with these. If not, please direct me somewhere else and I will remove the post.
The arbitrarily chosen example claim I am working with is whether “Alexander the Great existed”. This has the useful properties of 1: an expected outcome (that he existed - although, perhaps my problem is that this is not the case!) and 2: it relies heavily on probability as there is little solid evidence.
One popular claim is that coins were minted with his face on them. I want to use Bayes to find how likely a face appearing on a coin is for someone who existed. As I understand it, there should be 4 combinations:
The first issue is that there are infinite people who never existed and did not have a coin made. If I narrow it to historic figures who turned out not to exist and did not have a coin made it becomes possible but also becomes subjective as to whether someone actually thought they existed. For example, did people believe the Minotaur existed?
Perhaps I should choose another filter instead of historic figure, like humans that existed. But picking and choosing the category is again so subjective. Someone may also argue that woman inequality back then was so great that the data should only look at men, as a woman’s chance of being portrayed on a coin was skewed in a way that isn’t applicable to men.
I hope i have successfully communicated the problem i am grappling with and what i want to use it for. If not, please ask for clarifications. A friend in academia suggested that this touches on a problem with Bayes priors that has not been settled. If that is the case, is there any suggested resources for a novice with limited free time, to start to explore the issue? References to books or other online resources or even somewhere else I should be posting this kind of question would all be gratefully received. Not to mention a direct answer in the comments!