Sure, but why will they disagree?
From the correct perspective, it is more extraordinary that anyone agrees.
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.
Yes but that is not where the problems stop, it is where they get really bad. Object level disagreements can maybe be solved by people who agree on an epistemology. But people aren't in complete agreement about epistemology. And there is no agreed meta epistemology to solve epistemological disputes..that's done with same epistemology as before. And that circularity means we should expect people to inhabit isolated, self sufficient philosophical systems.
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.
Corrected by whose definition of correct? Do you not see that you are assuming you will suddenly be able to solve the foundational problems that philosophers have been wrestling with for millennia.
,If you do not agree, it would be really interesting to hear your thoughts on this. Thanks
From the correct perspective, it is more extraordinary that anyone agrees.
Correct by whose definition? In a consistent reality that is possible to make sense of, one would expect evolved beings to start coming to the same conclusions.
Corrected by whose definition of correct?
From this question i assume you are getting at our inability to know things and the idea that what is correct for one, may not be for another. That is a big discussion but let me say that i premise this on the idea that a true skeptic realizes we can not know anything for sure an...
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!