I don’t understand why shminux’s comment was down to −6 (as of 11⁄17). I think this comment is good for thinking clearly. How reality is perceived to you is based off how you collect data, update, and interpret events. You can get really different results by changing any of those( biased data collection, only updating on positive results, redefining labels in a motte and bailey, etc.)
Going from a “one truth” to a “multiple frames” model helps communicating with others. I find it easier to tell someone
from a semantics viewpoint, ‘purpose’ is a word created by people to describe goals in normal circumstances. From this standpoint, to ask “What’s my purpose in life?” doesn’t make sense since a goal doesn’t make sense applied to a whole life [Note: if you believe in a purposeful god, then yes you can ask that question]
than stating more objectively (ie without the “from a semantics viewpoint”).
This is also good for clarifying metrics because different frames are better at different metrics, which should be pointed out (for clear communication’s sake).
Instead of denying whole viewpoints, this allows zeroing in on what exactly is being valued and why. For example, Bob is wishing people loving-kindness and imagining them actually being happy as a result of his thoughts. I can say this is bad on a predictive metric, but good on a “Bob’s subjective well-being” metric.
I don’t understand why shminux’s comment was down to −6 (as of 11⁄17). I think this comment is good for thinking clearly. How reality is perceived to you is based off how you collect data, update, and interpret events. You can get really different results by changing any of those( biased data collection, only updating on positive results, redefining labels in a motte and bailey, etc.)
Going from a “one truth” to a “multiple frames” model helps communicating with others. I find it easier to tell someone
than stating more objectively (ie without the “from a semantics viewpoint”).
This is also good for clarifying metrics because different frames are better at different metrics, which should be pointed out (for clear communication’s sake).
Instead of denying whole viewpoints, this allows zeroing in on what exactly is being valued and why. For example, Bob is wishing people loving-kindness and imagining them actually being happy as a result of his thoughts. I can say this is bad on a predictive metric, but good on a “Bob’s subjective well-being” metric.