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author: niplav, created: 2023-01-04, modified: 2024-10-22, language: english, status: in progress, importance: 7, confidence: certain

There are too many possible quantified self experiments to run. Do hobbyist prediction platforms¹ make priorisation easier? I test this by setting up multiple markets, in order to run two experiments (the best one, and a random one), mostly for the effects of various nootropics on absorption in meditation. After one experiment, the log score of the market is -0.326 — pretty good. This gains us 0.202 bits of evidence in favor of the accuracy of the markets.

Using Prediction Platforms to Select Quantified Self Experiments

dynomight 2022 has a very cool proposal:

Oh, and by the way are you THE NSF or DARPA or THE NIH or A BILLIONAIRE WHO WANTS TO SPEND LOTS OF MONEY AND BRAG ABOUT HOW YOU ADVANCED THE STATE OF HUMAN KNOWLEDGE MORE THAN ALL THOSE OTHER LAME BILLIONAIRES WHO WOULDN’T KNOW A HIGH ROI IF IT HIT THEM IN THE FACE? Well how about this:

1. Gather proposals for a hundred RCTs that would each be really expensive but also really awesome. (E.g. you could investigate SALT → MORTALITY or ALCOHOL → MORTALITY or UBI → HUMAN FLOURISHING.)
2. Fund highly liquid markets to predict the outcome of each of these RCTs, conditional on them being funded.
   • If you have hangups about prison, you might want to chat with the CFTC before doing this.
3. Randomly pick 5% of the proposed projects, fund them as written, and pay off the investors who correctly predicted what would happen.
4. Take the other 95% of the proposed projects, give the investors their money back, and use the SWEET PREDICTIVE KNOWLEDGE to pick another 10% of the RCTs to fund for STAGGERING SCIENTIFIC PROGRESS and MAXIMAL STATUS ENHANCEMENT.

—dynomight, “Prediction market does not imply causation”, 2022

Well, I'm neither a billionaire nor the NSF or DARPA, but I have run two shitty self-blinded RCTs on myself already, and I'm certainly not afraid of the CFTC. And indeed I don't have a shortage of ideas on things I could run RCTs on, but the time is scarce (I try to collect m=50 samples in each RCT, which (with buffer-days off) is usually more than 2 months of data collection).

So I'll do what @saulmunn pointed out to me is a possibility: I'm going to do futarchy (on) myself by setting up a set of markets of Manifold Markets with respect to the outcomes of some pre-specified self-blinded RCTs, waiting until the prices on them equilibriate, and then running two of those RCTs (the "best" one, by my standards, and a random one) and using the results as resolutions, while resolving the others as ambiguous.

Timeline

If the markets receive enough liquidity, I'll start the first experiment early in 2024, and the second one sometime in 2024 (depending on the exact experiment), hopefully finishing both before 2025.

Markets

Some experiments can be self-blinded, especially ones that involve substances, others can not because they require me to engage in an activity or receive some sensory input, so I distinguish the two, and will slightly prioritise the experiments that can be blinded.

In all experiments, I will be using the statistical method detailed here, code for it here, unless someone points out that I'm doing my statistics wrong.

I will be scoring the markets based on the variables specified in the prediction market title, but I'll of course be collecting a lot of other data during that time that will also be analyzed.

Table of Current Market Status

Self-Blinded Experiments

In general, by meditative absorption I mean the concentration/tranquility (in Buddhist terms samatha) during a ≥30 minute meditation session in the morning, ~45 minutes after waking up and taking the substance (less if the substance starts working immediately). I will be doing at least 15 minutes of anapanasati during that meditation session, but might start (or end) with another practice).

Past meditation data can be found here.

1. L-Theanine + Caffeine vs. Sugar → Meditative Absorption: 50 samples in the morning after waking up, 25 intervention with 500mg l-theanine & 200mg caffeine and 25 placebo (sugar pills). Expected duration of trial: ~2½ months (one sample every day, but with possible pauses).
   1. Expected effect size: 0.06*1+0.14*0.8+0.26*0.4+0.3*0.1+0.2*0=0.306
2. Nicotine vs. Normal chewing gum → Meditative Absorption: 40 samples, with blocking after waking up, 20 intervention with 2mg nicotine, 20 placebo (similar-looking square chewing gum). Expected duration of trial: ~4½ months (two samples/week, to avoid getting addicted to nicotine).
   1. Expected effect size: 0.1*1+0.25*0.8+0.28*0.4+0.25*0.1+0.11*0=0.437
3. Modafinil vs. Sugar → Meditative Absorption: 40 samples, again with blocking directly after waking up, 20 intervention with 100mg modafinil and 20 placebo (sugar pills). Expected duration of trial: Also ~4½ months with two samples per week, as to prevent becoming dependent on modafinil.
   1. Expected effect size: 0.03*1+0.2*0.8+0.29*0.4+0.31*0.1+0.18*0=0.337
4. Vitamin D vs. Sugar → Meditative Absorption: 50 samples, taken after waking up, 25 intervention (25μg Vitamin D₃) and 25 placebo (sugar pills). Expected duration of trial: ~2½ months (taken ~every day, with possible pauses).
   1. Expected effect size: 0.04*1+0.06*0.8+0.1*0.4+0.41*0.1+0.38*0=0.169
5. Vitamin B12 vs. Sugar → Meditative Absorption: 50 samples, taken after waking up, 25 intervention (500μg Vitamin B12 + 200μg folate) and 25 placebo (sugar pills). Expected duration of trial: 2½ months (short interruptions included).
   1. Expected effect size: 0.03*1+0.09*0.8+0.1*0.4+0.4*0.1+0.38*0=0.182
6. LSD Microdosing vs. Water → Meditative Absorption: 50 samples in the morning, 25 intervention (10μg LSD), and 25 placebo (distilled water). Expected duration of trial is ~4 months (4 samples per week, with some time left as a buffer).
   1. Expected effect size: 0.06*1+0.14*0.8+0.21*0.4+0.3*0.1+0.29*0=0.286
7. CBD Oil vs. Similar-Tasting Oil → Meditative Absorption: 50 samples in the morning, 25 intervention (240mg CBD in oil, orally), and 25 placebo (whatever oil I can find that is closest in taste to the CBD oil). Expected duration of the trial: ~2½ months (taken ~every day, with possible pauses).
   1. Expected effect size: 0.04*1+0.1*0.8+0.18*0.4+0.35*0.1+0.35*0=0.227
8. L-Phenylalanine vs. Sugar → Meditative Absorption: 50 samples, taken directly after waking up, 25 intervention (750mg L-Phenylalanine), and 25 placebo (sugar pills). Duration of trial: 2½ months (one sample a day).
   1. Expected effect size: 0.06*1+0.16*0.8+0.2*0.4+0.34*0.1+0.23*0=0.302
9. Bupropion vs. Sugar → Happiness: 50 samples taken after waking up, 25 intervention (150mg Bupropion), and 25 placebo (sugar pills). Duration is typical 2½ months again.
   1. Expected effect size: 0.05*1+0.19*0.8+0.27*0.4+0.27*0.1+0.22*0=0.337
10. THC Oil vs. Similar-Tasting Oil → Meditative Absorption: 50 samples in the morning, 25 intervention (4mg THC in oil, orally), and 25 placebo (whatever oil I can find that is closest in taste to the THC oil). Expected duration of the trial: ~2½ months (taken ~every day, with possible pauses).
    1. Expected effect size: 0.07*1+0.18*0.8+0.27*0.4+0.22*0.1+0.26*0=0.344

Non-Blinded Experiments

Some experiments can't be blinded, but they can still be randomized. I will focus on experiments that can be blinded, but don't want to exclude the wider space of interventions.

1. Intermittent Fasting vs. Normal Diet → Happiness: 50 samples, 25 intervention (eating only between 18:00 and midnight), 25 non-intervention (normal diet, which is usually 2 meals a day, spaced ~10 hours apart), chosen randomly via echo -e "fast\ndon't fast" | shuf | tail -1. Expected duration of the trial: ~2 months.
   1. Expected effect size: 0.03*1+0.18*0.8+0.36*0.4+0.3*0.1+0.13*0=0.348
2. Pomodoro Method vs. Nothing → Productivity: 50 samples, 25 intervention (I try to follow the Pomodoro method as best as I can, probably by installing a TAP of some sort), 25 non-intervention (I just try to do work as normally), chosen randomly via echo -e "pomodoro\nno pomodoro" | shuf | tail -1. Expected duration of trial: 2 months.
   1. Expected effect size: 0.07*1+0.19*0.8+0.39*0.4+0.29*0.1+0.06*0=0.397
3. Bright Light vs. Normal Light → Happiness: 50 samples, 25 intervention (turning on my lumenator of ~30k lumen in the morning), 25 non-intervention (turning on my normal desk lamp of ~1k lumen), selected via echo -e "lamp\nno lamp" | shuf | tail -1. Expected duration of trial: 4 months, as I often don't spend all my day at home.
   1. Expected effect size: 0.11*1+0.29*0.8+0.27*0.4+0.23*0.1+0.1*0=0.473
4. Meditation vs. No Meditation → Sleep duration: 50 samples, 25 intervention (2 consecutive days of ≥2h/day of meditation), 25 non-intervention (no meditation), selected via echo -e "meditation\nno meditation" | shuf | tail -1. Expected duration of trial: 5 months, as I might not always find a 2-day interval in which I'm sure I can meditative 2h/day.
   1. Expected effect size: 0.04*1+0.08*0.8+0.21*0.4+0.53*0.1+0.15*0=0.241

Further Ideas

I have a couple more ideas on possible experiments that I could run, and will put them up as I acquire more mana. I might also just farm highly-rated but rarely-investigated methods from troof 2022 and experiences reported here.

Blindeable:

1. Semaglutide vs. Sugar → Productivity (tracking conscientiousness)
2. Melatonin vs. Sugar → Sleep duration
3. Orexin-A vs. Sugar → Sleep duration
4. Neuropeptide S vs. Sugar → Sleep duration
5. Sodium Oxybate vs. Sugar → Sleep duration
6. Galantamine vs. Sugar → Number of dreams

Not blindeable:

7. Binaural Beats vs. Silence → Meditative Absorption
8. Brown Noise vs. Silence → Meditative Absorption
9. Brown Noise vs. Music → Productivity
10. Silence vs. Music → Productivity
11. Time Since Last Masturbation → Productivity
12. Starting Work Standing vs. Starting Work Sitting → Productivity

Pleas

This little exercise may need your participation! I have three pleas to you, dear reader:

1. Please predict on the markets! If people predict on the markets, I both get more information about the value of the different experiments, and I also get mana back. It would be cool to know whether hobbyist prediction markets can be used for choosing experiments, and the worst result would be a "well, we can't really tell because liquidity on the markets was too small".
2. Maybe send me mana for me to create more markets or subsidise existing ones. I'd love to subsidise my markets on Manifold a whole bunch, but don't have enough mana for that at the moment. clippy and Tetraspace both already send me mana, which I greatly appreciate. With more mana, I could also put up more markets, and thereby explore a larger space of possible experiments. However, maybe the value of another market isn't so high, so this one is way less urgent.
3. Give me ideas for more experiments to run. If you have an idea you're enthusiastic about and which you've always wanted to have tested, but you're kind of lazy about actually doing it, I might be able to jump in. Most interesting to me are experiments that are:
   1. Affordable: Expensive substances, high-end devices etc. are too prohibitive (unless you want to buy the thing for me to perform the experiment).
   2. Safe: Sorry, I'm not going to take methamphetamine, even though it might make me much more productive.
   3. Measurable: The variable the intervention is supposed to affect should be measurable in at least one of the ways I currently collect data, or at least easily measurable. In particular cognitive performance is hard to get a grip on: IQ test can't be repeated very often, but maybe there's a game that measures cognitive performance reliably, or could I use dual n-back?
   4. Fast: I can't do 50 samples of an intervention where one sample takes 2 weeks to take effect. Daily is best, but for really good options I might be willing to tolerate 2 samples a week.

Other than that, I also welcome all critiques at any level of detail of this undertaking.

Further Ideas

If I could create more markets, I might be able to put up markets on different variables I measure during the day. That way, I could select interventions that dominate others across multiple dimensions.

If there were prediction platforms that supported them, combinatorial prediction markets or latent-variable prediction markets could be incredibly cool, but we don't live in that world (yet).

Results

On 2024-01-25, I decided to select the experiment. seq 1 14 | shuf | tail -1 output 12, which corresponds to the experiment Pomodoro Method vs. Nothing → Productivity.

The market with the highest expected effect size is Bright Light vs. Normal Light → Happiness, so those are the two experiments I am going to run.

I am a bit weary of selecting these two markets: The Bright Light market has the lowest trading volume of all markets, at only M̶104, and both these markets are not blindeable.

But a commitment I have made, so a commitment I have to follow through with.

Pomodoros

I ran the experiment from 2024-01-29 to 2024-06-17, using spt with this script, managed by this script.

The data on whether a particular day was a pomodoro-method day was saved in this file, and the data on the pomodoros was saved in this file.

The code for loading and transforming the pomodoro data isn't particularly interesting, if you're curious you can find it in this file.

datasets=get_datasets_pom()

Let's proceed to the analysis, then (using the same methodology as for my nootropics experiments:

res=analyze(datasets)

And the results are:

>>> res
    productivity  creativity     sublen       happy     content     relaxed       horny
d       0.257836   -0.130323  -0.140056   -0.073699   -0.132798   -0.038319   -0.144040
λ       5.413354    0.508335   4.058103    0.318865    1.078502    1.232905    7.756272
p       0.117179    0.930744   0.256304    0.959552    0.827240    0.795999    0.022903
dσ     -0.044201    0.006098   0.037463    0.007177    0.047723   -0.252365    0.744675
k      54.000000   54.000000  54.000000  111.000000  111.000000  111.000000  111.000000

I didn't meditate or do flashcards during that time.

So the pomodoro method somewhat increases productivity (at the edge of statistical significance), and maybe decreases subjective length of the day a bit. It also decreases horniness a little bit, which I find pretty funny².

Scoring the Market

I can now score the market:

def logscore(o,p):
        return np.mean(o*np.log(p)+(np.ones_like(o)-o)*np.log(np.ones_like(p)-p))
p=np.array([0.06, 0.29, 0.39, 0.19, 0.07])
o=np.array([0, 0, 1, 0, 0])
logscore(outcomes, p)
-0.3258531953347593

Honestly: The market did pretty well.

0.202 Bits of Evidence For Futarchy

So, I put up some prediction markets on the results of quantified self RCTs. I ran one of the experiments, and scored one market on the results.

How much should the performance of the market change our opinion about the viability of using prediction platforms to predict RCTs, and thus be plausibly useful in selecting experiments to run and actions to perform?

We can define the maximum entropy distribution (our prior on how good causal Futarchy markets should be) over possible log scores as having the mean of the log score of random forecasts, namely -0.6931…

The maximum entropy distribution for a given mean on the positive reals is the exponential distribution.

The exponential distribution is defined by one parameter, which is $\lambda=\frac{1}{μ}$ (the mean of the distribution), in this case $\lambda=\frac{1}{0.6931} \approx 1.4427$ (for convenience flipping the distribution to be defined over positive reals). The logscore observed for the Pomodoro method market was 0.3258, so the posterior distribution is $\text{Exponential}(λ + 1/x)$: $λ_{n} = 1.4427 + 1/0.326 ≈ 4.5102$.

To calculate the bits of evidence we got from running the market, we calculate the information gain, the bits of evidence are calculated by log₂(posterior odds / prior odds).

For continuous distributions, we use probability densities, for the exponential distribution:

I don't really have a comparison point which to compare this result to, but ≈0.2 bits of evidence seems fairly small to me. I guess I'll have to run some more experiments for further evidence.

Acknowledgements

Many thanks to clippy (twitter) for M̶500, and Tetraspace (twitter) for M̶1000, which I used to subsidize the markets. Also many thanks to the manifold admin Genzy for subsidizing each market with M̶450.

Your funding of the sciences is greatly appreciated.

My gratitude also goes out to all the traders on the markets. You help me prioritize, you help us gain knowledge.

See Also

• Replication Markets, Prediction markets for Science, and Social Science Prediction Platform
• What I learned gathering thousands of nootropic ratings (troof, 2022)
• [Part 1] Amplifying generalist research via forecasting – Models of impact and challenges (jacobjacob/ozziegooen/Elizabeth/NunoSempere/bgold, 2019)
• [Part 2] Amplifying generalist research via forecasting – results from a preliminary exploration (jacobjacob/ozziegooen/Elizabeth/NunoSempere/bgold, 2019)
• Discussions
  • /r/slatestarcodex
  • LessWrong

Appendix A: Explanations for the Experiments I Chose

Over time, I'll put some explanations on why these specific experiments interest me. Not yet fully, though.

L-Theanine + Caffeine vs. Sugar → Meditative Absorption

My l-theanine experiment gave disappointing results, but people have (rightfully) pointed out that l-theanine is best taken together with caffeine: one gets energy and relaxation at the same time.

This points at a broader possibility: Why not set up markets for all possible combinations of nootropics? But alas, this runs into problems with combinatorial explosion.

Nicotine vs. Normal chewing gum → Meditative Absorption

• Gwern 2016

Modafinil vs. Sugar → Meditative Absorption

• Gwern 2018

Vitamin D vs. Sugar → Meditative Absorption

Vitamin D seems just generally great, so it's not super far out to suspect that supplementing it after waking up could have positive effects on wakefulness.

Vitamin B12 vs. Sugar → Meditative Absorption

LSD Microdosing vs. Water → Meditative Absorption

Inspired by Gwern 2019.

CBD Oil vs. Similar-Tasting Oil → Meditative Absorption and THC Oil vs. Similar-Tasting Oil → Meditative Absorption

My brother, in conversation, brought up that smoking weed is incredibly relaxing to him, and told me he imagines that this is what he thinks deep meditative states feel like. That intrigues me enough to consider it as intervention towards absorption, if not mindfulness (albeit one that has the danger of creating subtly dull states of mind).

L-Phenylalanine vs. Sugar → Meditative Absorption

• Diet Coke probably isn't a cognitive performance enhancer (dynomight, 2022)
• Contra Dynomight contra me, but also kinda contra me and in support of Dynomight, on Diet Coke (Aaron Bergman, 2022)

Bupropion vs. Sugar → Happiness

• My positive experience taking the antidepressant Wellbutrin/Bupropion, & why maybe you should try it too (Robert Wiblin, 2019)

Intermittent Fasting vs. Normal Diet → Happiness

Pomodoro Method vs. Nothing → Productivity

The Pomodoro technique also uses the concept of rhythm, breaking up the day into twenty-five-minute segments of work and five minutes of a break. Interestingly, though, I found no academic study that tested the technique.

—Gloria Mark, “Attention Span” p. 66, 2023

It'd be cool if I were the first person to actually test this widespread technique.

See also:

• Akrasia Tactics Review, 2, 3

Bright Light vs. Normal Light → Happiness

• My simple hack for increased alertness and improved cognitive functioning: very bright light (chaosmage, 2013)
• YOU NEED MORE LUMENS (David Chapman, 2015)
• How to build your own Lumenator (Eliezer Yudkowsky, 2016)
• MORE LUX: light bars for SAD (David Chapman, 2019)
• Your room can be as bright as the outdoors (Ben Kuhn, 2019)
• How to Build a Lumenator (Raemon 2018)
• Someone should probably start a bright home lighting company (lincolnquirk, 2019)
• Why indoor lighting is hard to get right and how to fix it (Richard Korzekwa, 2020)
• A new option for building lumenators (Alex Altair, 2021)
• How to Lumenate (UK Edition) (chanamessinger, 2022)
• Guide to rationalist interior decorating (mingyuan 2023)
• Shortform on cheap lumenator (Angelinahli 2023)
• YOU NEED MORE LUX (David Chapman, 2023)
• Seriously bright light vs. winter blahs (David Chapman, 2023)

Meditation vs. No Meditation → Sleep duration

• O'Hara et al. 2010