I was really impressed with how successful RC is at maintaining an environment where people can learn and grow. Part of that is certainly selection effects- the point of center is self directed growth around programming, and there's an interview process that I assume filters especially hostile people.
But I think the social rules do a lot too, and have been trying to pay attention to the effects on others when someone breaks them at work. No Feigned Surprise is a particularly important one around people who are trying to learn and already a little insecure. It's great when they've learned a new thing, and you want to celebrate that, not meet it with denigration!
How do prediction markets account for interest rates? I feel like I should be willing to pay no more than ~96 cents for a contract that will definitely resolve to a dollar in a year. Who puts up the other 4 cents?
I'm sincerely unclear- is the analysis here that smaller samples (fewer ballots processed by a tabulator) have a higher relative standard deviation than larger samples?
Occasionally but pretty rarely. By the time a trade has become actually risk free, as in "I can instantly buy X here and sell X there and make a profit", someone will have realized that and done the trade when it was almost risk free- the that the price is likely but not guaranteed to go up there- and made the trade first.
Almost always outside of crypto, the market makers and exchanges are different entities. Exchanges maintain order books- who is willing to buy or sell what, at what prices, plus a lot of rules about tie breaking, order visibility, "implied" prices (e.g. sometimes the combination of two products is logically equivalent to a third), etc. When orders "cross"- that is, someone is offering to buy at a price at least as good as someone is willing to sell for, the exchanges matches those participants and they are considered to have traded (though for a mix of technical and regulatory reasons, the trade actually settles two days later)
Market makers generally maintain offers to both buy and sell a product, generally ~all the time the market is open. For example, they might offer to buy up to 30 X for $0.99 or sell up to 70 for $1.01. If small buy and sell orders come in more or less randomly, the market maker will sell about as many X as they buy, for (1.01 - 0.99) a profit of 2c for each set of orders. The trick for a market maker is to offer the best price, so that they get any orders at all, while accounting for the risk that the person buying or selling from them (the liquidity taker) isn't just a random order, but is either market moving or correctly predicting the market is about to move- e.g. a market maker offering to buy a million shares of a X at $0.99 will lose a lot of money to someone who correctly predicted X is about to go to $0.70, and took them up on the full offer.
My hunch is that they aren't tailored toward ridiculous images exactly, but if they demonstrated "a woman sitting in a chair reading", it would be really hard to tell if the result was a small modification of an image in the training data. If they demonstrate "A snake made out of corn", I have less concern about the model having a very close training example.
I'm going through Understanding Software Dynamics by Richard Sites now, and it's the first book I've read that covers the practical performance implications of some of these new features, even if briefly.
I was really impressed with how successful RC is at maintaining an environment where people can learn and grow. Part of that is certainly selection effects- the point of center is self directed growth around programming, and there's an interview process that I assume filters especially hostile people.
But I think the social rules do a lot too, and have been trying to pay attention to the effects on others when someone breaks them at work. No Feigned Surprise is a particularly important one around people who are trying to learn and already a little insecure. It's great when they've learned a new thing, and you want to celebrate that, not meet it with denigration!