In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.
This is the ninth post in my series on Anthropics. The previous one is The Solution to Sleeping Beauty.
There are some quite pervasive misconceptions about betting in regards to the Sleeping Beauty problem.
One is that you need to switch between halfer and thirder stances based on the betting scheme proposed. As if learning about a betting scheme is supposed to affect your credence in an event.
Another is that halfers should bet at thirders odds and, therefore, thirdism is vindicated on the grounds of betting. What do halfers even mean by probability of Heads being 1/2 if they bet as if it's 1/3?
In this post we are going to correct them. We will understand how to arrive to correct betting odds from both thirdist and halfist positions, and...
No, I mean the Beauty awakes, sees Blue, gets a proposal to bet on Red with 1:1 odds, and you recommend accepting this bet?
Today a trend broke in Formula One. Max Verstappen didn't win a Grand Prix. Of the last 35 Formula One Grand Prix, Max Verstappen has won all but 5. Last season he had something like 86% dominance.
For context I believe that I am overall pessimistic when asked to give a probability range about something "working out". And since sports tend to vary in results if using a sport like Formula One would be a good source of data to make and compare predictions against?
Everything from estimating the range a pole position time, or the difference between pole and the last qualifier, from to a fastest lap in a race or what lap a driver will pit for fresh tyres.
What is the best way of doing it?
Tracking your predictions and improving your calibration over time is good. So is practicing making outside-view estimates based on related numerical data. But I think diversity is good.
If you start going back through historical F1 data as prediction exercises, I expect the main thing that will happen is you'll learn a lot about the history of F1. Secondarily, you'll get better at avoiding your own biases, but in a way that's concentrated on your biases relevant to F1 predictions.
If you already want to learn more about the history of F1, then go for it, it...
Human interactions are full of little “negotiations”. My friend and I have different preferences about where to go for dinner. My boss and I have different preferences about how soon I should deliver the report. My spouse and I are both enjoying this chat, but we inevitably have slightly different (unstated) preferences about whose turn it is to speak, whether to change the subject, etc.
None of these are arguments. Everyone is having a lovely time. But they involve conflicting preferences, however mild, and these conflicts need to somehow get resolved.
These ubiquitous everyday “negotiations” have some funny properties. At the surface level, both people may put on an elaborate pretense that there is no conflict at all. (“Oh, it’s no problem, it would be my pleasure!”) Meanwhile, below...
that thing about affine transformations
If the purpose of a utility function is to provide evidence about the behavior of the group, we can preprocess the data structure into that form: Suppose Alice may update the distribution over group decisions by ε. Then she'll push in the direction of her utility function, and the constraints "add up to 100%" and "size ε" cancel out the "affine transformation" degrees of freedom. Now such directions can be added up.
On 16 March 2024, I sat down to chat with New York Times technology reporter Cade Metz! In part of our conversation, transcribed below, we discussed his February 2021 article "Silicon Valley's Safe Space", covering Scott Alexander's Slate Star Codex blog and the surrounding community.
The transcript has been significantly edited for clarity. (It turns out that real-time conversation transcribed completely verbatim is full of filler words, false starts, crosstalk, "uh huh"s, "yeah"s, pauses while one party picks up their coffee order, &c. that do not seem particularly substantive.)
ZMD: I actually have some questions for you.
CM: Great, let's start with that.
ZMD: They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your...
So despite it being "hard to substantiate", or to "find Scott saying" it, you think it's so certainly true that a journalist is justified in essentially lying in order to convey it to his audience?
About 15 years ago, I read Malcolm Gladwell's Outliers. He profiled Chris Langan, an extremely high-IQ person, claiming that he had only mediocre accomplishments despite his high IQ. Chris Langan's theory of everything, the Cognitive Theoretic Model of the Universe, was mentioned. I considered that it might be worth checking out someday.
Well, someday has happened, and I looked into CTMU, prompted by Alex Zhu (who also paid me for reviewing the work). The main CTMU paper is "The Cognitive-Theoretic Model of the Universe: A New Kind of Reality Theory".
CTMU has a high-IQ mystique about it: if you don't get it, maybe it's because your IQ is too low. The paper itself is dense with insights, especially the first part. It uses quite a lot of nonstandard terminology (partially...
(This post is intended for my personal blog. Thank you.)
One of the dominant thoughts in my head when I build datasets for my training runs: what our ancestors 'did' over their lifespan likely played a key role in the creation of language and human values.[1]
I imagine a tribe whose members had an approximate of twenty to thirty-five years to accumulate knowledge—such as food preparation, hunting strategies, tool-making, social skills, and avoiding predators. To transmit this knowledge, they likely devised a system of sounds associated with animals, locations, actions, objects, etc.
Sounds related to survival would have been prioritized. These had immediate, life-and-death consequences, creating powerful associations (or neurochemical activity?) in the brain. "Danger" or "food" would have been far more potent than navigational instructions. I...
This is the eighth post in my series on Anthropics. The previous one is Lessons from Failed Attempts to Model Sleeping Beauty Problem. The next one is Beauty and the Bets.
Suppose we take the insights from the previous post, and directly try to construct a model for the Sleeping Beauty problem based on them.
We expect a halfer model, so
On the other hand, in order not repeat Lewis' Model's mistakes:
But both of these statements can only be true if
And, therefore, apparently, has to be zero, which sounds obviously wrong. Surely the Beauty can be awaken on Tuesday!
At this point, I think, you wouldn't be surprised, if I tell you that there are philosophers who are eager to bite this bullet and claim that the Beauty should, indeed, reason as...
The Two Coin version is about what happens on one day.
Let it be not two different days but two different half-hour intervals. Or even two milliseconds - this doesn't change the core of the issue that sequential events are not mutually exclusive.
observation of a state, when that observation bears no connection to any other, as independent of any other.
It very much bears a connection. If you are observing state TH it necessary means that either you've already observed or will observe state TT.
What law was broken?
The definition of a sample space - it's suppos...
In January, I defended my PhD thesis, which I called Algorithmic Bayesian Epistemology. From the preface:
For me as for most students, college was a time of exploration. I took many classes, read many academic and non-academic works, and tried my hand at a few research projects. Early in graduate school, I noticed a strong commonality among the questions that I had found particularly fascinating: most of them involved reasoning about knowledge, information, or uncertainty under constraints. I decided that this cluster of problems would be my primary academic focus. I settled on calling the cluster algorithmic Bayesian epistemology: all of the questions I was thinking about involved applying the "algorithmic lens" of theoretical computer science to problems of Bayesian epistemology.
Although my interest in mathematical reasoning about uncertainty...
Congratulations! I wish we could have collaborated while I was in school, but I don't think we were researching at the same time. I haven't read your actual papers, so feel free to answer "you should check out the paper" to my comments.
For chapter 4: From the high level summary here it sounds like you're offloading the task of aggregation to the forecasters themselves. It's odd to me that you're describing this as arbitrage. Also, I have frequently seen the scoring rule be used with some intermediary function to determine monetary rewards. For example, whe...
That sounds fun, feel free to message me with an invite. :)
stream-entry might be relatively easy and helpful
Worth noting that stream entry isn't necessarily a net positive either:
...However, if you’ve ever seen me answer the question “What is stream entry like,” you know that my answer is always “Stream entry is like the American invasion of Iraq.” It’s taking a dictatorship that is pretty clearly bad and overthrowing it (where the “ego,” a word necessarily left undefined, serves as dictator). While in theory this would cause, over time, a better government t