Will_Howard
Could you share what the questions were in the application form? I’m thinking of applying next year but I see there’s no longer a way to get to the application form from the site.
right now I might buy, then explain, in the hope that other people will believe me, also buy, and I can sell high
I think this would still work, as long you don’t explain before your buy order goes through 🙃
if trades are public like manifold right now, this might result in trading strategies like “mimic the most successful traders” becoming plausible
I think this is right, because you could copy a successful trader in the same transaction they are buying in, so they would lose their first mover advantage (previously people could copy, but only after they picked up shares at the best price). This would be a good reason to not have individual orders public, at least before a batch trade goes through.
Interestingly real stock markets don’t have public orders for call auctions, they report an Indicative Match Price, and some kind of aggregated information about buy/sell imbalance (details vary). Whereas for continuous trading orders are public. Presumably this is for a similar reason.
Some counters on why you might want HFT: https://blog.headlandstech.com
Thanks! In reading about this, I saw that that batch auctions had been proposed and tried in various financial markets, but they hadn’t caught on, so it’s interesting to see some of the pushback.
I’m not super convinced by the arguments in that post. I think a lot of them are begging the question wrt whether to prefer continuous or batched trading. I.e. given that trading is continuous, HFT does provide a lot of utility in various ways, e.g. reducing spreads between distant markets. But the reason those spreads exist is because continuous trading creates the possibility for something to happen in New York while your order is on its way from Chicago. You could equally solve that problem (at least partially) by imposing some kind of delay or using batched auctions with a shared logical time.
The one point which may be a crux is “Feedback Loops in Global Supply and Demand Negotiation”. If it is the case that there is meaningful price discovery done by back-and-forth negotiation over hundreds or thousands of “market ticks”, then this is a point against batched auctions.
Also I think the interesting case on prediction markets now is on relatively thinly traded markets, where reactions still tend to be on “human” timescales (someone doomscrolling twitter for news vs someone doing a monthly forecast, rather than two bots competing against each other). I think a lot of the logic about HFT doesn’t really apply in this case.
if you are desperate to trade I am here to trade with you right now … you just need to pay a fee by crossing the spread
Part of my claim is that this use case doesn’t generate a lot of utility in prediction markets, and that serving it comes a cost to the majority[1] who wouldn’t mind waiting.
(I may come back and respond to more later)
- ^
In some information-weighted sense
- ^
Some of the points apply more obviously to high volume markets, but I actually think this would be a bigger improvement on thinly traded markets. Mainly because of the aggregation of liquidity and “better ergonomics”. With 3-5 people, the issue of whether any of the participants are even checking the market at any given time is more of a problem, so there is a bigger benefit to having an agreed-upon time at which to expect everyone to have their orders ready.
Expiration dates on limit orders achieve something in this direction, but it’s not quite the same thing that batched trading would achieve.
Consider the case where you place an order based on your current forecast, and then some event happens while you’re not watching the market:
Under the current system, supposing you set an expiration time of 1 day: If the event happens within the day, you will still get scooped. If it happens after 1 day, then you can’t get scooped but you also lose the opportunity to make the trade at your endorsed price. For thinly traded markets it may well take more than 1 day for sufficient liquidity to show up, so you have to make this tradeoff between waiting for liquidity and risking getting caught with your trousers down.
Under a batched auction system, with a batch interval of 1 day: If the event happens within the first day, as long as you notice it before the auction executes everything is fine. If it happens after the first day, it’s still fine as long as you check the market on that day, so you might set expiration dates on your limit orders according to the rule “as long as I can commit to checking on this once per day”.
If you do fail to react before the auction executes: You are still likely to have your order taken up at a more favourable price, because the auction executes at a single price. Looking at the first image in the post, suppose you are the buyer with a limit order at $14. In continuous trading, your order would have been taken at around $14, as soon as the marginal traders showed up to act on the new info. But under batched trading, more sellers would show up before the auction takes place, pushing the price lower. So you are still safer leaving your order sitting there in the market, as long as you trust that the bulk of orders at the time the auction executes are from “live” traders (who are actively reacting to information)[1].
- ^
Now I think about this this could actually push in the direction of lower accuracy, because people can afford to be more blasé about leaving low info orders in the market
Yeah I have also had to stop using it recently for this reason (at great cost to my productivity :( ), mainly because the non-AI part of the app became insanely slow. I’ve yet to find a good alternative (that has one-off tasks + robust verification + usable mobile interface), please let me know if you do find one, I’m currently leaning towards building a very basic one myself
Possibly of interest: the fastest rocket sled track uses a similar idea, they put a helium filled tube over the final section of the track:
Just as meteors are burned up by friction in the upper atmosphere, air friction can cause a high-speed sled to burn up, even if made of the toughest steel alloys. An engineering sleight-of-hand is used to increase those “burn-up” limits by reducing the density of the atmosphere around the track. To do this, one needs a safe, non-toxic, low-density gas such as helium. Helium is only one seventh the density of air, significantly reducing friction between the high speed sled and the atmosphere. Enter the “helium bag” concept.
No one person takes ownership of the idea, so it was probably a combination of brainstorming and inspiration. But like any elegant engineering solution, simplicity is at its heart. It involves enclosing a portion the track with a plastic sheet, not unlike the plastic drop cloth found at the hardware store. This tube is sealed off and pumped full of helium to force out the air. A helium-filled tube that can stretch for more than a mile then covers the track.Shows that the idea can basically work without any advanced technology. I think this is a video of it in action, where the white thing above the track is the polyethylene tunnel which is then destroyed as the sled goes through it:
That was when I took a week off work to do side projects, glad it at least shows up in this graph 😌
Thanks for the tip… deep down a part of me knows there are ways to get around Freedom, but they’re non-obvious enough that I haven’t cheated yet
Glad to be of help! I was almost put off by the overly rave reviews when I first tried it lol but now I can imagine myself writing one.
I haven’t tried Focusmate, although I do do timed work sessions (and did before I used the app) and now use the app to enforce a certain amount of time per day, so this achieves a similar effect.
I registered on forfeit and see that it has pomodoro forfeits in the app, so using it for focusmate may be redundant.
It might be redundant, although one thing that is really great about it is that because screenshots are the universal interface you can keep using the apps you already use and just use Forfeit to submit evidence. This is why I still use Habitica even though I could equally put the same tasks in Forfeit directly, there is just no particular reason to do so.
Also, if you don’t mind me asking, what is the commitment you most often fail? Is it time spent on twitter and youtube?
I’ve looked through at the ones I have failed and it looks like “reply to X person” and “publish blog post” are the categories that stand out. For replying to people I deliberately set the penalty low to begin with (and increase it if I fail the first time) so this is by design. For publishing blog posts, there aren’t that many instances but I have failed >50% of them, here I was generally underestimating the effort it takes.
I actually rarely fail the twitter one because the penalty is quite high (and I block it via Freedom for the rest of the day if I’m getting close to the limit).
Could you explain more about being coercive towards subagents? I’m not sure I’m picking up exactly what you mean.
Some thoughts related to what I am interpreting you as meaning: I have found that my problems have shifted from not being able to follow through on things, to being almost too able to follow through on things, and thus getting entrenched in a pre-committed plan when I should change it (for instance repeating a time consuming daily habit that isn’t doing much for me).
I see this as a good problem to have. When I was less able to execute on things my plans were just as bad, I just never found out because I never got as far as completing them. The problem of planning is still hard though, and isn’t solved by the app, although I have found that it helps a lot.
I think these apps work best if really aversive tasks are set to a estimated-bare-minimum commitment, which in reality is probably the median amount of what one can get done.
This seems right. One thing I would say is that kind of surprisingly it hasn’t been the most aversive tasks where the app has made the biggest difference, it’s the larger number of moderately aversive tasks. It makes expensive commitments cheap and cheap commitments even cheaper, and for me it has turned out that cheap commitments have made up most of the value.
It has made a difference to the most aversive ones, I just don’t think the category of extremely aversive things makes up that much of the value in my life.
This is possibly a way in which it’s better than Beeminder, which I understand is oriented towards at least medium term goals (where you make several increments of progress along the yellow brick road). By number most of the things I have put in Forfeit have been one-off things that would be too small to be worth Beeminding (unless you could group them into a higher level of abstraction).
giving him the Four-Hour Work-Week
...
he went from not hard-working at all (barely worked, had no direction) to more ambitious working levels (~50 hour weeks, big ambitions)
This made me chuckle
On the product categories vs quantity: I agree, I intended Schmartphones to be a new category in the post, apologies if that wasn’t clear[1].
I believe this actually is possible, and works in my example, though I accept that it’s not very realistic and the path would look more like the “Trajectory of a newly invented good” graph.
The point is that the GDP shares of the other categories can change while still growing at 0%, because growth is just based on the quantity of goods delivered. Going through the example again with more explicit numbers (and changing growth to 100% YoY to simplify it):
t = 0: There aren=9types of goods, 1000 units are sold of each, And they all have the same price of $1, so each gets 1/9th GDP share. New good has been invented but has 0% GDP share so far.t = 1: The new type of good sells 2 units, at $500 each, so it goes straight to GDP share parity with all the other goods, and each now have1/(9+1) = 1/10GDP share. But each of the other categories still sells 1000 units, so they are all at 0% growth, and per thet = 2: The new type of code sells 4 units, at $250. It maintains its1/10GDP share, grows by 100%, and adds 10% to overall GDP growth. The other types of goods still sell 1000 units each and contribute nothing to GDP growth.^perhaps an important point glossed over in the post is that the price of the new good has to go down by the inverse of the growth rate. And as mentioned briefly in the footnote, I think the simplest way to imagine this type of economy is that people work more hours to provide the new type of good, while otherwise changing nothing about what they do for the other goods.
Sidenote: This setup would keep nominal GDP constant, and the economy overall would be judged as being in steep deflation because you can buy more and more “product value” in the form of the new good, for the same nominal dollars.
I think this gets to the crux of it, and is the main point I’m trying to make: Using either fixed base year accounting or chain weighting, the value of a new product essentially gets baked into GDP at the [price when it is introduced] x [quantity when it is mature]. If the price at the time of introduction is proportional to GDP at that time (say, people always spend 1% of their income on the new gizmo, or 1% of people have a ton of disposable income and are price insensitive), then a linear flow of new goods will translate into an exponential increase in GDP.
Standard economics interprets this as an exponential increase in product value over time. This is reasonable, and I think this assumption is well understood by economists going through this calculation.
The main reason I think this is an interesting observation is that there is some laundering that happens between the calculation and the practical conception of GDP. People (including economists) often conflate exponential growth in GDP with exponential growth in ability to have impact on the world. For instance “if the economy keeps doubling every 30 years, we need to have tiled the light cone or hard plateaued within a few hundred years”. But the calculation supports this continued exponential doubling with only a linear stream of new goods, and these goods may not be of the type that let you tile the light cone.
Whether this mechanism explains a large or small fraction of measured GDP growth over the last ~100 years is an empirical question. I’m not claiming that the exponential growth comes from linear introduction of new goods, just that the accounting + a plausible trajectory for new goods permits this.
Also: I am aware that the way categories are counted practically means that a new good would probably fit into an existing category (e.g. the 71 the US uses) rather than creating a whole new one. I think this still works because within categories the same logic applies