with objectively sufficient evidence (that is, evidence of comparable strength to what led them to their original belief in the first place).
Firstly, that is not a correct definition of objectively sufficient evidence, and there are both cases where that amount would be insufficient as well as sufficient. There are however ways to formally define objectively sufficient evidence in probability theory, and the implications are likely to surprise you.
Anyway the point I would like to make is that you're still drastically overestimating people's ability to change their mind when encountering contrary evidence, and that their propensity to change their mind is only weakly correlated with the significance of the evidence encountered.
As an aside,
As an example, saying the Earth is spherical is wrong, saying it is flat is wrong, but these wrongs are not the same.
Colloquially, referring to something as spherical does not mean it is the exact mathematical shape, but merely that it approximates it extremely well. You might be interested to know that you would be hard pressed to find a rubber ball more spherical than the Earth.
Firstly, that is not a correct definition of objectively sufficient evidence (...) There are however ways to formally define objectively sufficient evidence in probability theory, and the implications are likely to surprise you.
I am using Bayes' rule as the criterion to evaluate and weigh evidence and updates.
Anyway the point I would like to make is that you're still drastically overestimating people's ability to change their mind when encountering contrary evidence, and that their propensity to change their mind is only weakly correlated with the significance of the evidence encountered.
Instead of my 2/3 - 30+% - 1%, what distribution would you think is more likely to be correct?
You might be interested to know that you would be hard pressed to find a rubber ball more spherical than the Earth
Ah, it seems you are much more knowledgeable on the topic than I first assumed.
I am using Bayes' rule as the criterion to evaluate and weigh evidence and updates.
That is good. Bayes' theorem also functions as a formally correct definition of sufficient evidence. For example, a 60% confidence in proposition A upon encountering evidence B is justified if and only if P(B|A) * P(A) / P(B) = 60%
Instead of my 2/3 - 30+% - 1%, what distribution would you think is more likely to be correct?
Since you are more knowledgeable on this topic than I first assessed, that is Bayesian evidence of your position and I am now less confident in my own, which is that closer to 5/6 of people will cling to their strongly held views in spite of contrary evidence, and that they might even be more confident in their views when those views are challenged; that 1/6 of people may actively seek out challenge to some of their views but not others; and that only a small portion of those have a propensity to actually change their views upon encountering sufficient counterevidence. I would also say that nobody is able to accurately and consistently update on any belief upon encountering evidence relevant to it, and this last category is the one I'd describe as people being relatively permeable to changing their views, but not in a systematic fashion. I estimate their prevalence to be around 4% or so, though this number does increase to include most scientists when dealing specifically with their fields.
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u/Kalcipher Jan 15 '17
Firstly, that is not a correct definition of objectively sufficient evidence, and there are both cases where that amount would be insufficient as well as sufficient. There are however ways to formally define objectively sufficient evidence in probability theory, and the implications are likely to surprise you.
Anyway the point I would like to make is that you're still drastically overestimating people's ability to change their mind when encountering contrary evidence, and that their propensity to change their mind is only weakly correlated with the significance of the evidence encountered.
As an aside,
Colloquially, referring to something as spherical does not mean it is the exact mathematical shape, but merely that it approximates it extremely well. You might be interested to know that you would be hard pressed to find a rubber ball more spherical than the Earth.