With the Alexander the Great issue, you not only have the question of the probability that someone would have a coin minted and so on, but also the probability that he actually did have a coin minted with his face on it, given that someone says he did. And you cannot assign that to 100% even if you have seen the coin yourself, because it may not have been intended to be the face of Alexander the Great, but someone else.
And basically all of those probabilities are subjective, and cannot be made objective. So your project might work for an individual to map out their own beliefs, allowing them to assign subjective probabilities to things they consider priors. But it cannot work for a community, because people will disagree with the priors you assign, and so there will be no reason for them to agree with the probabilities your program comes up with.
Sure, but why will they disagree? If I say there is 60% chance of x and you say no it is more like 70% then i can ask you why you think its 10% more likely. I know many will say "its just a feeling" but what gives that feeling? If you ask enough questions, i am confident one can drill down to the reasoning behind the feeling of discomfort at a given estimate. Another benefit of WL is it should help people get better at recognizing and understanding their subconscious feelings so they can be properly evaluated and corrected. If you do not agree, it would be really interesting to hear your thoughts on this. Thanks
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!