The cat is defined outside being a combination of traits of owner; that is the difference between the cat and IQ or any other psychological measure. If we were to say ‘pet’, the formula would have worked, even better if we had a purely black box qualifier into people who have bunch of traits vs people who don’t have bunch of traits, regardless of what is the cause (a pet, a cat, a weird fetish for pet related stuff).
It is however the case that narcissism does match sociopathy, to the point that difference between the two is not very well defined. Anyhow we can restate the problem and consider it a guess at the properties of the utility function, adding extra verbiage.
The analogy on the math problems is good but what we are compensating for is miscommunication, status gaming, and such, by normal people.
I would suggest, actually, not the Bayesian approach, but statistical prediction rule or trained neural network.
I would suggest, actually, not the Bayesian approach, but statistical prediction rule or trained neural network.
Given the asymptotic efficiency of the Bayes decision rule in a broad range of settings, those alternatives would give equivalent or less accurate classifications if enough training data (and computational power) were available. If this argument is not familiar, you might want to consult Chapter 2 of The Elements of Statistical Learning.
The cat is defined outside being a combination of traits of owner; that is the difference between the cat and IQ or any other psychological measure. If we were to say ‘pet’, the formula would have worked, even better if we had a purely black box qualifier into people who have bunch of traits vs people who don’t have bunch of traits, regardless of what is the cause (a pet, a cat, a weird fetish for pet related stuff).
It is however the case that narcissism does match sociopathy, to the point that difference between the two is not very well defined. Anyhow we can restate the problem and consider it a guess at the properties of the utility function, adding extra verbiage.
The analogy on the math problems is good but what we are compensating for is miscommunication, status gaming, and such, by normal people.
I would suggest, actually, not the Bayesian approach, but statistical prediction rule or trained neural network.
Given the asymptotic efficiency of the Bayes decision rule in a broad range of settings, those alternatives would give equivalent or less accurate classifications if enough training data (and computational power) were available. If this argument is not familiar, you might want to consult Chapter 2 of The Elements of Statistical Learning.