That is indeed another problem I did not consider. It definitely decreases the value of knowing how many people others have a crush on in general. But still, the fact how many people have a crush on me in particular should be somewhat correlated to how many people they have a crush on in general. Since, as you pointed out, it is impossible for me to get the specific percentage for myself I’ll have to go with the general one.
Concerning your juggling comparison. Maybe I did not express myself clearly. If you want to find out if someone likes you, then of course the most important thing is interacting with him/her. It is way more important than knowing the prior. I do not expect that after finding out the prior and reading about flirting signals I will be able to skip Interacting with people. I believe it will help me interpreting the interaction with people the right way.
The only way I can see the prior being irrelevant is when flirting signals are 100% perfect filters. Let us say I am flirting with someone and giving a signal and getting a response. If that person gives that response 100% to a person she has a crush on and 0% to a person she does not have a crush on than the prior would indeed be irrelevant. If however this person gives the response with 100% Chance to a Person he/she has a crush on, but also with 50% chance to a person he/she just likes as a friend, then this signal will only help me differentiate between the states “friend” and “love intrest” with 50% probability. And now it becomes relevant on how many of his/her friends this person has a crush.
Let us say the person has 10 friends and has a crush on 5 of them. Than on average he/she would give 5 correct positive signals 2.5 false positive signals and 2.5 correct negative signals. So if I get a positive signal, than that means that with a probability of 2⁄3 that person would have a crush on me and with a probability of 1⁄3 he/she would not.
If that same person would only have a crush on one of his/her 10 friends than on average he/she would give 1 correct positive signal 4,5 false positive signals and 4,5 correct negative signals. So if I would get a positive signal in this case than with a probability of 18% (1/4.5+1)) that person would have a crush on me and with probability of 82% (4.5/4.5+1)) he or she would not have a crush on me.
So in the first case I am reasonably sure that the other person has a crush on me and can proceed giving more obvious hints, while in the second case I should still gather more information before moving on to the next stage.
I am of course aware that in real live I will not know the exact probability for how often a certain signal is given to a friend or a love interest, but I can still get acceptable estimates for that.
Is there anything wrong with this reasoning or do you just thing that flirting signals are usually 100% perfect filters?
Flirting signals aren’t just positive and negative.
If a girl is nervous when around you when she isn’t nervous while around other people and there no real reason to be nervous, that can be a sign that she has a crush.
At the same time it can also be a sign that a girl likes you when she’s very relaxed while around you, has open body language and is comfortable with physical touch. Crucially more comfortable being touched by you than being touched by other people.
There are various things wrong with this reasoning, but I don’t think you’re getting my general point: this entire approach is misguided and it will not lead you to good outcomes.
I accept that you and most people here think this aproach is not helpfull. I will therefor abandon it. However you said that there are various other things wrong with my reasoning even if the aproach was not generally bad. Is my general process of asigning probabilities to believes wrong? Would the thing I did in the following paragraph also be wrong in an abstract scenario, where I would for example want to differentiate between blue and red balls instead of someone having a crush on me or not?
″ If however this person gives the response with 100% Chance to a Person he/she has a crush on, but also with 50% chance to a person he/she just likes as a friend, then this signal will only help me differentiate between the states “friend” and “love intrest” with 50% probability. And now it becomes relevant on how many of his/her friends this person has a crush.
Let us say the person has 10 friends and has a crush on 5 of them. Than on average he/she would give 5 correct positive signals 2.5 false positive signals and 2.5 correct negative signals. So if I get a positive signal, than that means that with a probability of 2⁄3 that person would have a crush on me and with a probability of 1⁄3 he/she would not.”
My claim is that your model is far too simple to model the complexities of human attraction.
Let’s use your example of pulling red and blue balls from an urn. Consider an urn with ten blue balls and five red balls. In a “classical” universe, you would expect to draw a red ball from this urn one time in three. A simple probabilistic model works here.
In a “romantic” universe, the individual balls don’t have colours yet. They’re in an indeterminate state. They may have tendencies towards being red or blue, but if you go to the urn and say “based on previous observations of people pulling balls out of this urn, the ball I’m about to pull out should be red one third of the time”, they will almost always be blue. Lots of different things you might do when sampling a ball from the urn might change its colour.
In such a universe, it would be very hard to model coloured balls in an urn. As far as people being attracted to other people are concerned, we live in such a universe.
My claim is that your model is far too simple to model the complexities of human attraction.
Probably. But that doesn’t mean that it can’t be modelled. Or are you instead claiming that it shouldn’t be modelled?
The first can be remedied by better models—and starting with a simple approximate model surely isn’t a bad first step. The latter can’t be fixed by modelling obviously.
That is indeed another problem I did not consider. It definitely decreases the value of knowing how many people others have a crush on in general. But still, the fact how many people have a crush on me in particular should be somewhat correlated to how many people they have a crush on in general. Since, as you pointed out, it is impossible for me to get the specific percentage for myself I’ll have to go with the general one.
Concerning your juggling comparison. Maybe I did not express myself clearly. If you want to find out if someone likes you, then of course the most important thing is interacting with him/her. It is way more important than knowing the prior. I do not expect that after finding out the prior and reading about flirting signals I will be able to skip Interacting with people. I believe it will help me interpreting the interaction with people the right way.
The only way I can see the prior being irrelevant is when flirting signals are 100% perfect filters. Let us say I am flirting with someone and giving a signal and getting a response. If that person gives that response 100% to a person she has a crush on and 0% to a person she does not have a crush on than the prior would indeed be irrelevant. If however this person gives the response with 100% Chance to a Person he/she has a crush on, but also with 50% chance to a person he/she just likes as a friend, then this signal will only help me differentiate between the states “friend” and “love intrest” with 50% probability. And now it becomes relevant on how many of his/her friends this person has a crush.
Let us say the person has 10 friends and has a crush on 5 of them. Than on average he/she would give 5 correct positive signals 2.5 false positive signals and 2.5 correct negative signals. So if I get a positive signal, than that means that with a probability of 2⁄3 that person would have a crush on me and with a probability of 1⁄3 he/she would not.
If that same person would only have a crush on one of his/her 10 friends than on average he/she would give 1 correct positive signal 4,5 false positive signals and 4,5 correct negative signals. So if I would get a positive signal in this case than with a probability of 18% (1/4.5+1)) that person would have a crush on me and with probability of 82% (4.5/4.5+1)) he or she would not have a crush on me.
So in the first case I am reasonably sure that the other person has a crush on me and can proceed giving more obvious hints, while in the second case I should still gather more information before moving on to the next stage.
I am of course aware that in real live I will not know the exact probability for how often a certain signal is given to a friend or a love interest, but I can still get acceptable estimates for that.
Is there anything wrong with this reasoning or do you just thing that flirting signals are usually 100% perfect filters?
Flirting signals aren’t just positive and negative.
If a girl is nervous when around you when she isn’t nervous while around other people and there no real reason to be nervous, that can be a sign that she has a crush.
At the same time it can also be a sign that a girl likes you when she’s very relaxed while around you, has open body language and is comfortable with physical touch. Crucially more comfortable being touched by you than being touched by other people.
There are various things wrong with this reasoning, but I don’t think you’re getting my general point: this entire approach is misguided and it will not lead you to good outcomes.
I accept that you and most people here think this aproach is not helpfull. I will therefor abandon it. However you said that there are various other things wrong with my reasoning even if the aproach was not generally bad. Is my general process of asigning probabilities to believes wrong? Would the thing I did in the following paragraph also be wrong in an abstract scenario, where I would for example want to differentiate between blue and red balls instead of someone having a crush on me or not?
″ If however this person gives the response with 100% Chance to a Person he/she has a crush on, but also with 50% chance to a person he/she just likes as a friend, then this signal will only help me differentiate between the states “friend” and “love intrest” with 50% probability. And now it becomes relevant on how many of his/her friends this person has a crush.
Let us say the person has 10 friends and has a crush on 5 of them. Than on average he/she would give 5 correct positive signals 2.5 false positive signals and 2.5 correct negative signals. So if I get a positive signal, than that means that with a probability of 2⁄3 that person would have a crush on me and with a probability of 1⁄3 he/she would not.”
I haven’t yet abandoned this approach and I’m not sure you should do so either. At least not until some more comments on this topic have come in.
My claim is that your model is far too simple to model the complexities of human attraction.
Let’s use your example of pulling red and blue balls from an urn. Consider an urn with ten blue balls and five red balls. In a “classical” universe, you would expect to draw a red ball from this urn one time in three. A simple probabilistic model works here.
In a “romantic” universe, the individual balls don’t have colours yet. They’re in an indeterminate state. They may have tendencies towards being red or blue, but if you go to the urn and say “based on previous observations of people pulling balls out of this urn, the ball I’m about to pull out should be red one third of the time”, they will almost always be blue. Lots of different things you might do when sampling a ball from the urn might change its colour.
In such a universe, it would be very hard to model coloured balls in an urn. As far as people being attracted to other people are concerned, we live in such a universe.
Probably. But that doesn’t mean that it can’t be modelled. Or are you instead claiming that it shouldn’t be modelled?
The first can be remedied by better models—and starting with a simple approximate model surely isn’t a bad first step. The latter can’t be fixed by modelling obviously.