FYI - there is an excellent Sentience Institute Report from 2018 which treated this issue in a lot of detail: https://www.sentienceinstitute.org/gm-foods
There's lots of points here. While they are possible, I would suggest they are not particularly common/well-suported in the psychological literature as it is today.
In addition, I don't know why these explanations would lead to desensitisation towards positive and negative events.
I can't think of a good theoretical reason why true effects should fall so significantly – like 40%. That's striking. The same attenuation result holds, even including income/age/event prevalence.
"Intuitively, if wellbeing saturates at the top end, having a really positive thing happen to me genuinely might not move the needle as much."
This is true. Another way of saying this is: "the true effects fall as you get happier". But then, given reported happiness has stayed constant, why would the effects fall?
Hm, I don't think I agree with you on linearity. Andrew Oswald was writing about this in 2008. One option is that the function is logistic/arctan: i.e,. quite concave/flat at high latent happiness levels. That is, you can't shift reported happiness above a 10 (a ceiling effect), even if you get happier.
In this case: even if the reporting function is non-linear (and assuming true effect sizes are constant), why would the observed effects fall? Because people are getting happier. Again, this is a different way of saying rescaling is happening.
I don't think you did mention this before...! I think this graph is just for 1 country. Perhaps Japan.
To be honest, I don't know what to think of the Wolfers/Stevenson objections! My only thought is: differences of, e.g,. 0.2 points, would look pretty small in comparison to the potential rescaling effects I suggest here.
Calibrating with biological measures. Hm, could be a interesting, albeit labour intensive ... !
I've seen this graph a couple times on the Forum, now. I am confused why these lines are going up, but LS is generally flat. The one thing that stands out to me is that the timeframes are generally smaller than multidecade ones used for most studies on the Easterlin Paradox.
I'd also guess it'd be harder to calibrate the categorical response happiness question (This'd certainly be the case if you used my method, here.)
On income increasing over time. I discuss this more in the paper. We think that increasing income is the main pathway that rescaling occurs through. So, including it as a control could introduce over-control bias.
Oh, and I rounded from .62 something to .6 for the indexed effect size :)
Thank you, this was interesting.
FYI - there is an excellent Sentience Institute Report from 2018 which treated this issue in a lot of detail: https://www.sentienceinstitute.org/gm-foods
Thanks for this Parker. I continue to think this research is insanely cool.
I agree with 1)
On 2), the condition you find makes sense, but aren't you implicitly assuming an elasticity of substitution of 1 with Cobb-Douglas?
Could be interesting to compare with Aghion et al. 2017. They look at a CES case with imperfect substitution (i.e., humans needed for some tasks).
https://www.nber.org/system/files/working_papers/w23928/w23928.pdf
Thanks, Alene! I appreciate that :)
Thank you for this interesting post.
“By assumption, the AI can perfectly substitute for human AI researchers.”
Any idea/intuition about what would happen if you relaxed this assumption?
Hello there,
There's lots of points here. While they are possible, I would suggest they are not particularly common/well-suported in the psychological literature as it is today.
In addition, I don't know why these explanations would lead to desensitisation towards positive and negative events.
Hello, Huw!
I can't think of a good theoretical reason why true effects should fall so significantly – like 40%. That's striking. The same attenuation result holds, even including income/age/event prevalence.
"Intuitively, if wellbeing saturates at the top end, having a really positive thing happen to me genuinely might not move the needle as much."
This is true. Another way of saying this is: "the true effects fall as you get happier". But then, given reported happiness has stayed constant, why would the effects fall?
Hm, I don't think I agree with you on linearity. Andrew Oswald was writing about this in 2008. One option is that the function is logistic/arctan: i.e,. quite concave/flat at high latent happiness levels. That is, you can't shift reported happiness above a 10 (a ceiling effect), even if you get happier.
In this case: even if the reporting function is non-linear (and assuming true effect sizes are constant), why would the observed effects fall? Because people are getting happier. Again, this is a different way of saying rescaling is happening.
I don't think you did mention this before...! I think this graph is just for 1 country. Perhaps Japan.
To be honest, I don't know what to think of the Wolfers/Stevenson objections! My only thought is: differences of, e.g,. 0.2 points, would look pretty small in comparison to the potential rescaling effects I suggest here.
Thanks, this is interesting. I wonder if this sort of individual-level noise might be smoothed out by large-n experience sampling.
Hello Vasco, thanks!
Calibrating with biological measures. Hm, could be a interesting, albeit labour intensive ... !
I've seen this graph a couple times on the Forum, now. I am confused why these lines are going up, but LS is generally flat. The one thing that stands out to me is that the timeframes are generally smaller than multidecade ones used for most studies on the Easterlin Paradox.
I'd also guess it'd be harder to calibrate the categorical response happiness question (This'd certainly be the case if you used my method, here.)
On income increasing over time. I discuss this more in the paper. We think that increasing income is the main pathway that rescaling occurs through. So, including it as a control could introduce over-control bias.
Oh, and I rounded from .62 something to .6 for the indexed effect size :)