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pleasure-pain scale that tries to take into account the non-linearity and non-normality of valence ratings. One idea we came up with was a “deference”-type approach, where we ask open-ended questions about people’s best and worst experiences and have them rank them against each other. Although locally the data would be very sparse, the idea was that there might be methods to integrate the collective patterns of deference into an approximate scale. If extended to populations of people who are known to have experienced extremes of valence, the approach would even allow us to unify the various pain scales (Scoville, Schmidt, KIP, etc.) and assign a kind of universal valence score to different categories of pain and pleasure.1 That will be version 2.0. In the meantime, we thought to try to get a rough picture of the extreme joys and affections of members of the general public, which is what this article will focus on. Normal World vs. Lognormal World There is a world we could call the “Normal World,” where valence outliers are rare and most types of experiences affect people more or less similarly, distributed along a Gaussian curve. Then there is another, very different world we could call

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text:
the “long-tailed world” or if we want to make it simple (acknowledging uncertainty) “Lognormal World,” where almost every valence distribution is a long-tail. So in the “Lognormal World,” say, for pleasure (and symmetrically for pain), we would expect to see a long-tail in the mean pleasure of experiences between different categories across all people, a long-tail in the amount of pleasure within a given type of experience across people, a long-tail for the number of times an individual has had a certain type of pleasure, a long-tail in the intensity of the pleasure experienced with a single category of experience within a single person, and so on. Do we live in the Normal World or the Lognormal World? Predictions of Lognormal World If we lived in the “Lognormal World,” we would expect: That people will typically say that their top #1 best/worst experience is not only a bit better/worse than their #2 experience, but a lot better/worse. Like, perhaps, even multiple times better/worse. That there will be a long-tail in the number of appearances of different categories (i.e., that a large amount, such as 80%, of top experiences will belong to the same narrow set of categories, and that there

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text:
will be many different kinds of experiences capturing the remaining 20%). That for most pairs of experiences \(x\) and \(y\), people who have had both instances of \(x\) and \(y\), will usually agree about which one is better/worse. We call such a relationship a “deference.” More so, we would expect to see that deference, in general, will be transitive (\(a > b\) and \(b > c\) implying that \(a > c\)). To test the first and second prediction does not require a lot of data, but the third does because one needs to have enough comparisons to fill a lot of triads. The survey results we will discuss bellow are congruent with the first and second prediction. We did what we could with the data available to investigate the third, and tentatively, it seems to hold up (with ideas like deference network centrality analysis, triadic analysis, and tournament-style approaches). Survey Setup The survey asked the following questions: current level of pleasure, current level of pain, top 3 most pleasurable experiences (in decreasing order) along with pleasure ratings for each of them and the age when they were experienced, and the same for the top 3 most painful experiences. I specifically

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text:
did not provide a set of broad categories (such as “physical” or “emotional”) or a drop-down menu of possible narrow categories (e.g., going to the movies, aerobic exercise, etc.). I wanted to see what people would say when the question was as open-ended as possible. I also included questions aimed more directly at probing the long-tailed nature of valence: I asked participants to rate “how many times more pleasant was the #1 top experience relative to the #2 top experience” (and #2 relative to #3, and the same for the top most painful experiences). I also asked them to describe in more detail the single most pleasant and unpleasant experiences, and added a box for comments at the end in order to see if anyone complained about the task (most people said “no comment,” many said they enjoyed the task, and one person said that it made them nostalgic). I also asked about basic demographics (age and gender). Participants earned $1.75 for the task, which seems reasonable given the time it took to complete in most cases. Mechanical Turk: Participant Composition The survey was run on Mechanical Turk. I requested “Masters” qualifications for 90/110 of the survey responses. To be

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text:
a Mechanical Turk Master(clickhappier 2016) you need to have a good track record of task approval, which I reasoned would increase the rate of well thought-out high-quality responses (this turned out to be true). The participants’ genders were fairly balanced (50/97 women, 47/97 men), and the average age was 36.5 years (sd = 10): Figure 7: Age distribution Bots (easy to spot–unless extremely sophisticated) While reading the responses I discovered that some of them seemed to be generated by bots. Apparently task requesters started noticing the presence of bots(Dreyfuss 2018) a couple of years ago. This is obviously a problem for academics trying to use the service for their research, and for machine learning professionals using the service for data-tagging. Having been both of those things in the past, I can say that a few years ago I did not see any submissions that looked suspiciously bot-like. Though low-quality rushed responses did seem to be relatively common back then, I never observed bot-like responses to open-ended questions. Unfortunately, this time I was able to spot several responses clearly written by bots. For example, one respondent answered the question “write a brief essay about your #1 best experience” with: The reason

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text:
these types of assignments are given so often is that anyone can write about their own experience and it doesn’t require any outside resources or research. However, even though anyone can tell a story about their life, that does not mean anyone can write a good essay about that experience. As a professor and teacher for 30 years, I’ve read thousands of essays and can tell you there is a distinct difference from telling a story about yourself and writing an excellent personal experience essay. The difference between good and great: And it ends that way, with a colon indicating that the respondent is about to explain what the difference between good and great is. But it never does. In most cases the difference between a genuine response and a bot response was very obvious. That said, I erred on the side of caution for filtering bots and I got rid of answers even if they seemed just a little suspicious. This left me with 97 out of the 110 original responses. The following analysis was conducted on those 97 responses. Preprocessing Since the responses were open-ended I had to tag each of them with an experience category. To do

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text:
this I read each response and identified the key theme in them and classified them with a label that was specific enough to distinguish it from nearby experiences (e.g., different types of fractures), but not so specific that we would never get more than one response per category (e.g., “breaking the middle finger in elementary school”). In general, most responses fell into very unambiguous categories (e.g. “When my father passed away” and “Watching my father die and take his last few breaths.” were both classified as “Father death”). About 10% of the responses were relatively ambiguous: it wasn’t clear what the source of the pain or pleasure was. To deal with those responses I used the label “Unspecified.” When some detail was present but ambiguity remained, such as when a broad type of pain or pleasure was mentioned but not the specific source I tagged it as “Unspecified X” where X was a broad category. For example, one person said that “broken bones” was the most painful experience they’ve had, which I labeled as “Unspecified fracture.” Results I should preface the following by saying that we are very aware of the lack of scientific rigor in this survey; it remains

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text:
a pilot exploratory work. We didn’t specify the time-scale for the experiences (e.g., are we asking about the best minute of your life or the best month of your life?) or whether we were requesting instances of physical or psychological pain/pleasures. Despite this lack of constraints it was interesting to see very strong commonalities among people’s responses. Appearance Base Rates There were 77 and 124 categories of pleasure and pain identified, respectively. On the whole it seemed like there was a higher diversity of ways to suffer than of ways to experience intense bliss. Summoning the spirit of Tolstoy: “Happy families are all alike; every unhappy family is unhappy in its own way.” Here are the raw counts for each category with at least two appearances: Best experiences appearances (with at least two reports). Worst experience appearances (with at least two reports). For those who want to see the full list of number of appearances for each experience mentioned, see Appendix A (I also clarify some of the more confusing labels there too). A simple way to try to incorporate the information about the ranking is to weight experiences rated as top #1 with 3 points, those as top #2

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text:
with 2 points, and those as the top #3 with 1 point. If you do this, the experiences scores are: Weighted appearances of best experiences (#1 – 3 points, #2 – 2 points, #3 – 1 point). Weighted appearances of worst experiences (#1 – 3 points, #2 – 2 points, #3 – 1 point). Average Ratings Given the relatively small sample size, I will only report the mean rating for pain and pleasure (out of 10) for categories of experience for which there were 6 or more respondents: For pain: 1. Father death (n = 19): mean 8.53, sd 2.3 2. Childbirth (n = 16): mean 7.94, sd 2.16 3. Grandmother death (n = 13): mean 8.12, sd 2.5 4. Mother death (n = 11): mean 9.4, sd 0.62 5. Car accident (n = 9): mean 8.42, sd 1.52 6. Kidney stone (n = 9): mean 5.97, sd 3.17 7. Migraine (n = 9): mean 5.36, sd 3.11 8. Romantic breakup (n = 9): mean 7.11, sd 1.52 9. Broken arm (n = 6): mean 8.28, sd 0.88 10. Broken leg (n = 6): mean 7.33, sd 2.02 11. Work failure (n = 6): mean 5.88, sd 3.57 (Note: the

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text:
very high variance for kidney stones and migraine is partly explained by the presence of some very low responses, with values as low as 1.1/10 – perhaps misreported, or perhaps illustrating the extreme diversity of experiences of migraines and kidney stones). For pleasure: 1. Falling in love (n = 42): mean 8.68, sd 1.74 2. Children born (n = 41): mean 9.19, sd 1.64 3. Marriage (n = 21): mean 8.7, sd 1.25 4. Sex (n = 19): mean 8.72, sd 1.45 5. College graduation (n = 13): mean 7.73, sd 1.4 6. Orgasm (n = 11): mean 8.24, sd 1.63 7. Alcohol (n = 8): mean 6.84, sd 1.59 8. Vacation (n = 6): mean 9.12, sd 0.73 9. Getting job (n = 6): mean 7.22, sd 1.47 10. Personal favorite sports win (n = 6): mean 8.17, sd 1.23 Deference Graph of Top Experiences We will now finally get to the more exploratory and fun/interesting analysis, at least in that it will generate a cool way of visualizing what causes people great joy and pain. Namely, the idea of using people’s rankings in order to populate a global scale across people and show it in the form of

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text:
a graph of deferences. While the scientific literature has some studies that compare pain across different categories(Spence, Miller, and Hendricks 1992)(Adams 1987)(Dolan 2011). I was not able to find any dataset that included actual rankings across a variety of categories. Hence why it was so appealing to visualize this. The simplest way of graphing experience deferences is to assign a node to each experience category and add an edge between experiences with deference relationships with a weight proportional to the number of directed deferences. For example, if 4 people have said that A was better than B, and 3 people have said that B was better than A, then there will be an edge from A to B with a weight of 4 and an edge from B to A with a weight of 3. Additionally, we can then run a graph centrality algorithm such as PageRank(“Centrality” 2020) to see where the “deferences end up pooling.” The images below do this: the PageRank of the graph is represented with the color gradient (darker shades of green/red representing higher PageRank values for good/bad experiences). In addition, the graphs also represent the number of appearances in the dataset for each category with

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text:
the size of each node: Best experiences deferences – edge thickness based on number of deferences, node size based on number of appearances, and color scheme based on PageRank. Worst experiences deferences – edge thickness based on number of deferences, node size based on number of appearances, and color scheme based on PageRank. The main problem with the approach above is that it double (triple?) counts experiences that are very common. Say that, for example, taking 5-MeO-DMT produces a consistently higher-valence feeling relative to having sex. If we only have a couple of people who report both 5-MeO-DMT and sex as their top experiences, the edge from sex to 5-MeO-DMT will be very weak, and the PageRank algorithm will underestimate the value of 5-MeO-DMT. In order to avoid the double counting effect of commonly-reported peak experiences we can instead add edge weights on the basis of the proportion with which an experience defers to the other. Let’s say that \(f(a, b)\) means “number of times that \(b\) is reported as higher than \(a\).” Then the proportion would be \(f(a, b) / (f(a, b) + f(b, a))\). Now, this introduces another problem, which is that pairs of experiences that appear together

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