Andrés Gómez Emilsson | Logarithmic Scales of Pleasure and Pain (2nd round EAGxVirtual 2020 lightning talk application) @/Andr%C3%A9sG%C3%B3mezEmilsson | Uploaded June 2020 | Updated October 2024, 13 hours ago.
I briefly explain that while some distributions (e.g. the size of the leaves of a tree) follow a Gaussian bell-shaped pattern, many others (e.g. avalanches, size of asteroids, etc.) follow a long-tail distribution. Long-tail distributions have the general property that a large fraction of the volume is accounted for by a tiny percent of instances (e.g. 80% of the snow that falls from the mountain will be the result of the top 20% largest avalanches).
I then explain that based on previous research we have conducted at the Qualia Research Institute we have arrived at the tentative conclusion that the intensity of pleasure and pain follow a long-tail distribution. Why?
First, neural activity on patches of neural tissue follow log-normal distributions (an instance of a long-tail distribution).
Second, the extremes of pleasure and pain are so intense that they cannot conceivably be just the extremes of a normal distribution. This includes, on the positive end: Jhana meditation, 5-MeO-DMT peak experiences, and temporal lobe epilepsy (Dostoevsky famously saying he'd trade 10 years of his life for just a few moments of his good epileptic experiences). On the negative end, things like kidney stones, cluster headaches, fibromyalgia, and migraines top the charts of most intense pain.
And third, all of the quantitative analysis we conducted on a survey about people's best and worst experiences showed that the ratings, comparisons, and rankings of such experiences was far more consistent with a long-tail distribution than a normal distribution.
I then explain that this is an *important*, *tractable*, and *neglected* cause.
1) Important because we may be able to reduce the world's suffering by a significant amount if we just focus on preventing the most intense forms of suffering.
2) Tractable because there are already many possible effective treatments to these disorders (such as LSD microdosing for cluster headaches, and FSM for kidney stones).
3) And neglected because most people have no clue that pain and pleasure go this high. Most utilitarian calculus so far seems to assume a normal distribution for suffering, which is very far from the empirical truth. Bentham would recoil at the lack of an exponent term when additively normalizing pain scales.
I conclude by adding that in Effective Altruism there might be an implicit "youth" bias involved in the lack of knowledge of this phenomenon - due to the age of the people in the movement, most EA activists will not themselves have had intensely painful experiences. Thus, why it is so crucial to raise awareness about this topic in the community (it does not show up on its own). Simply put: because the logarithmic nature of pleasure and pain is *news* to most people in EA.
For more, see the original article: forum.effectivealtruism.org/posts/gtGe8WkeFvqucYLAF/logarithmic-scales-of-pleasure-and-pain-rating-ranking-and
And the presentation (given at EA New York): youtu.be/IeD3nZX1Sr4
I briefly explain that while some distributions (e.g. the size of the leaves of a tree) follow a Gaussian bell-shaped pattern, many others (e.g. avalanches, size of asteroids, etc.) follow a long-tail distribution. Long-tail distributions have the general property that a large fraction of the volume is accounted for by a tiny percent of instances (e.g. 80% of the snow that falls from the mountain will be the result of the top 20% largest avalanches).
I then explain that based on previous research we have conducted at the Qualia Research Institute we have arrived at the tentative conclusion that the intensity of pleasure and pain follow a long-tail distribution. Why?
First, neural activity on patches of neural tissue follow log-normal distributions (an instance of a long-tail distribution).
Second, the extremes of pleasure and pain are so intense that they cannot conceivably be just the extremes of a normal distribution. This includes, on the positive end: Jhana meditation, 5-MeO-DMT peak experiences, and temporal lobe epilepsy (Dostoevsky famously saying he'd trade 10 years of his life for just a few moments of his good epileptic experiences). On the negative end, things like kidney stones, cluster headaches, fibromyalgia, and migraines top the charts of most intense pain.
And third, all of the quantitative analysis we conducted on a survey about people's best and worst experiences showed that the ratings, comparisons, and rankings of such experiences was far more consistent with a long-tail distribution than a normal distribution.
I then explain that this is an *important*, *tractable*, and *neglected* cause.
1) Important because we may be able to reduce the world's suffering by a significant amount if we just focus on preventing the most intense forms of suffering.
2) Tractable because there are already many possible effective treatments to these disorders (such as LSD microdosing for cluster headaches, and FSM for kidney stones).
3) And neglected because most people have no clue that pain and pleasure go this high. Most utilitarian calculus so far seems to assume a normal distribution for suffering, which is very far from the empirical truth. Bentham would recoil at the lack of an exponent term when additively normalizing pain scales.
I conclude by adding that in Effective Altruism there might be an implicit "youth" bias involved in the lack of knowledge of this phenomenon - due to the age of the people in the movement, most EA activists will not themselves have had intensely painful experiences. Thus, why it is so crucial to raise awareness about this topic in the community (it does not show up on its own). Simply put: because the logarithmic nature of pleasure and pain is *news* to most people in EA.
For more, see the original article: forum.effectivealtruism.org/posts/gtGe8WkeFvqucYLAF/logarithmic-scales-of-pleasure-and-pain-rating-ranking-and
And the presentation (given at EA New York): youtu.be/IeD3nZX1Sr4
