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very infrequently might get a very high proportion score due to a low sample size. In order to prevent this we use Laplace smoothing and modify the equation to \((f(a, b) + 1) / (f(a, b) + f(b, a) + 2)\). Finally, we transform this proportion score from the range of \(0\) to \(1\) to the range of \(-1\) to \(1\) by multiplying by \(2\) and subtracting \(1\). We call this a “rebalanced smoothed proportion” \(w(a, b)\): \[w(a, b) = 2 \frac{f(a, b) + 1}{f(a, b) + f(b, a) + 2}-1\] I should note that this is not based on any rigorous math. The equation is based on my intuition for what I would expect to see in such a graph, namely a sort of confidenceweighted strength of directionality, but I do not guarantee that this is a principled way of doing so (did I mention this is a pilot small-scale low-budget ‘to a first approximation’ study?). I think that, nonetheless, doing this is still an improvement upon merely using the raw deference counts as the edge weights. To visualize what \(w(a, b)\) looks like I graphed its values for a and b in the range of \(0\) to \(20\)

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text:
(literally typing the equation into the google search bar): To populate the graph I only use the positive edge weights so that we can run the PageRank algorithm on it. This now looks a lot more reasonable and informative as a deference graph than the previous attempts: Best experiences deference graph: Edge weights based on the rebalanced smoothed proportions, size of nodes is proportional to number of appearances in the dataset, and the color tracks the PageRank of the graph. Edge color based on source node. Worst experiences deference graph: Edge weights based on the rebalanced smoothed proportions, size of nodes is proportional to number of appearances in the dataset, and the color tracks the PageRank of the graph. Edge color based on source node. By taking the PageRank of these graphs (calculated with NetworkX) we arrive at the following global rankings: PageRank of the graph of best experiences with edge weights computed with the rebalanced smoothed proportion equation. PageRank of the graph of worst experiences with edge weights computed with the rebalanced smoothed proportion equation. Intuitively this ranking seems more aligned with what I’ve heard before, but I will withhold judgement on it until we have much more data.

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text:
Triadic Analysis With a more populated deference graph we can analyze in detail the degree to which triads (i.e. sets of three experiences such that each of the three possible deferences are present in the graph) show transitivity (cf. Balance vs. Status Theory). In particular, we should compare the prevalence of these two triads: Left: 030T, Right: 030C.(Batagelj and Mrvar 2001) The triads above are 030T, which is transitive, and 030C, which is a loop. The higher the degree of agreement between people and the higher the probability of the existence of an underlying shared scale, we would expect to see more triads of the type 030T relative to 030C. That said, a simple ratio is not enough, since the expected proportion between these two triads can be an artifact of the way the graph is constructed and/or its general shape (and hence the importance of comparing against randomized graphs that preserve as many other statistical features as possible). With our graph, we noticed that the very way in which the edges were introduced generated an artifact of a very strong difference between these two types of triads. In the case of pain there are 105 ‘030T,’ and 3 ‘030C.’

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text:
And for the pleasure questions there were 98 ‘030T,’ and 9 ‘030C.’ That said, many of these triads are the artifact of taking into account the top three experiences, which already generates a transitive triad by default when n = 1 for that particular triad of experiences. To avoid this artifact, we filtered the graph by only adding edges when a pair of experiences appeared at least twice (and discounting the edges where w(a, b) = 0). With this adjustment we got 2 ‘030T,’ and 1 ‘030C’ for the pain questions, and 1 ‘030T,’ and 0 ‘030C’ for the pleasure question. Clearly there is not enough data to meaningfully conduct this type of analysis. If we extend the study and get a larger sample size, this analysis might be much more informative. Latent Trait Ratings A final approach I tried for deriving a global ranking of experiences was to assume a latent parameter for pain or pleasure of different experiences and treating the rankings as the tournament results of participants with skill equal to this latent trait. So when someone says that an experience of sex was better than an experience of getting a new bike we imagine that “sex”

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text:
had a match with “getting bike” and that “sex” won that match. If we do this, then we can import any of the many tournament algorithms(Mathieu and Vladu 2011) that exist (such as the Elo rating system(“Elo Rating System” 2020)) in order to approximate the latent “skill” trait of each experience (except that here it is the “skill” to cause you pleasure or pain, rather than any kind of gaming ability). Interestingly, this strategy has also been used in other areas outside of actual tournaments, such as deriving university rankings based on the choices made by students admitted to more than one college (see: Revealed Preference Rankings of US Colleges and Universities). I should mention that the fact that we are asking about peak experiences likely violates some of the assumptions of these algorithms, since the fact that a match takes place is already information that both experiences made it into the top 3. That said, if the patterns of deference are very strong, this might not represent a problem. To come up with this tournament-style ranking I decided to go for a state-of-the-art algorithm. The one that I was able to find and use was Microsoft Research’s algorithm called

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text:
TrueSkill (which is employed to rank players in Xbox LIVE). According to their documentation, to arrive at a conservative “leaderboard” that balances the estimated “true skill” and the uncertainty around it, they recommend ranking by the expected skill level minus three times the standard error around this estimate. If we do this, we arrive at the following experience “leaderboards”: Conservative TrueSkill scores for best (left) and worst (right) experiences (mu – 3*sigma). Long-tails in the Responses to “How Many Times Better/Worse” Question The survey included four questions aimed at comparing the relative hedonic values of peak experiences: “Relative to the 1st most pleasant experience, how many times better was the 2nd most pleasant experience?” (This was one, the other three were the permutations of also asking about 2nd vs. 3rd and about the bad experiences): (Note: I’ll ignore the responses to the comparison between the 2nd and 3rd worst pains because I messed up the question - I forgot to substitute “better” for “worse”). I would understand the skepticism about these graphs. But at the same time, I don’t think it is absurd that for many people the worst experience they’ve had is indeed 10 or 100 times worse than

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text:
the second worst. For example, someone who has endured a bad Cluster Headache will generally say that the pain of it is tens or hundreds of times worse than any other kind of pain they have had (say, breaking a bone or having skin burns). The above distributions suggest a long-tail for the hedonic quality of experiences: say that the hedonic quality of each day is distributed along a log-normal distribution. A 45 year old has experienced roughly 17,000 days. Let’s say that such a person’s experience of pain each day is sampled from a log-normal distribution with a Gaussian exponent with a mean of 10 and a standard deviation of 5. If we take 100 such people, and for each of them we take the single worst and the second worst days of their lives, and then take the ratio between them, we will have a distribution like this (simulated in R): If you smooth the empirical curves above you would get a distribution that looks like these simulations. You really need a long-tail to be able to get results like “for 25% of the participants the single worst experience was at least 4 times as bad as the

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text:
2nd worst experience.” Compare that to the sort of pattern that you get if the distribution was normal rather than log-normal: As you can see (zooming in on the y-axis), the ratios simply do not reach very high values. With the normal distribution simulated here, we see that the highest ratio we achieve is around 1.3, as opposed to the empirical ratios of 10+.2 It is possible that some fine-tuning of parameters could give rise to long-tail ratios even with a normal distribution (especially if the mean is, say, a negative value and the standard deviation is very wide). But in the general case a normal distribution will have a fairly narrow range for the ratios of the “top value divided by the second top value.” So at least as a general qualitative argument, I think, the simulations do suggest a long-tailed nature for the reported hedonic values.] If you are inclined to believe the survey responses- or at least assign some level of credibility to the responses in the 90thpercentile and below-, the data is much more consistent with a long-tail distribution for hedonic values relative to a normal distribution. Discussion Key Pleasures Surfaced Birth of Children I have

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text:
heard a number of mothers and father say that having kids was the best thing that ever happened to them. The survey showed this was a very strong pattern, especially among women. In particular, a lot of the reports deal with the very moment in which they held their first baby in their arms for the first time. Some quotes to illustrate this pattern: “The best experience of my life was when my first child was born. I was unsure how I would feel or what to expect, but the moment I first heard her cry I fell in love with her instantly. I felt like suddenly there was another person in this world that I cared about and loved more than myself. I felt a sudden urge to protect her from all the bad in the world. When I first saw her face it was the most beautiful thing I had ever seen. It is almost an indescribable feeling. I felt like I understood the purpose and meaning of life at that moment. I didn’t know it was possible to feel the way I felt when I saw her. I was the happiest I have ever been in my

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entire life. That moment is something that I will cherish forever. The only other time I have ever felt that way was with the subsequent births of my other two children. It was almost a euphoric feeling. It was an intense calm and contentment.” “I was young and had a difficult pregnancy with my first born. I was scared because they had to do an emergency c-section because her health and mine were at risk. I had anticipated and thought about how the moment would be when I finally got to hold my first child and realize that I was a mother. It was unbelievably emotional and I don’t think anything in the world could top the amount of pleasure and joy I had when I got to see and hold her for the first time.” “I was 29 when my son was born. It was amazing. I never thought I would be a father. Watching him come into the world was easily the best day of my life. I did not realize that I could love someone or something so much. It was at about 3am in the morning so I was really tired. But it was wonderful nonetheless.”

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text:
“I absolutely loved when my child was born. It was a wave of emotions that I haven’t felt by anything before. It was exciting and scary and beautiful all in one.” No luck for anti-natalists(Emilsson 2018b)… the super-strong drug-like effects of having children will presumably continue to motivate most humans to reproduce no matter how strong the ethical case against doing so may be. Coming soon: a drug that makes you feel like “you just had 10,000 children.” Falling In Love The category of “falling in love” was also a very common top experience. I should note that the experiences reported were not merely those of “having a crush,” but rather, they typically involved unusually fortunate circumstances. For instance, a woman reported being friends with her crush for 7 years. She thought that he was not interested in her, and so she never dared to confess her love for him… until one day, out of the blue, he confessed his love for her. Other experiences of falling in involve chance encounters with childhood friends that led to movie-deserving romantic escapades, forbidden love situations, and cases where the person was convinced the lover was out of his or her league. Travel/Vacation

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text:
The terms “travel” and “vacation” may sound relatively frivolous in light of some of the other pleasures listed. That said, these were not just any kind of travel or vacation. The experiences described do seem rather extraordinary and life-changing. For example, talking about back-packing alone in France for a month, biking across the US with your best friend, or a long trip in South East Asia with your sibling that goes much better than planned. MDMA/LSD/Psilocybin It is significant that out of 97 people four of them listed MDMA as one of the most pleasant experiences of their lives. This is salient given the relatively low base rate of usage of this drug (some surveys saying about 12%(Maldonado, n.d.), which is probably not too far off from the base rate for Mechanical Turk workers using MDMA). This means that a high percentage of people who have tried MDMA will rate it as as one of their top experiences, thus implying that this drug produces experiences sampled from an absurdly long-tailed high-valence distribution. This underscores the civilizational significance of inventing a method to experience MDMA-like states of consciousness in a sustainable fashion(Emilsson 2019b)). Likewise, the appearance of LSD and psilocybin is

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