trentmkelly/LessWrong-43k huggingface dataset:
Using Betting Markets to Make Decisions
by pku — 2021-02-19T19:39:53.287Z

Epistemic status: This seems sufficiently basic that it seems like someone would already have developed a general theory, but I haven’t managed to find anything. If you know of anything like this please let me know. This post copied over from here.

Say you want to make some really good choices – you want your business to succeed, your marriage to flourish, and to beat your long-term rival in a chess game. Problem is, you’re not very good at making your own decisions.

The obvious thing is to ask some experts for help. But there’s two problems with this: First, who even are the relevant experts? In chess this is simple enough – just call up the top ten or so players by ELO rating – but who will you call for your business and marriage? MBAs and marriage counsellors claim to have relevant expertise, but there’s no good rigorous data to check how good they are. Second, what do we do when the experts disagree? And how do we get them to help us in the first place?

The classic solution for this is to set up a betting market. The good experts will gain influence, money, and confidence proportionate to how good they actually are, and the Efficient Market Hypothesis implies this will give us the best possible decisions (assuming there’s enough money in the market to make it worth the competent experts’ time). This solves the last two problems, but “just set up a betting market” is doing a lot of work on what we actually need to do here.

The classic example people give of a betting market is the stock market, but in practice they’re pretty different things. In the stock market, investors bet on how well publicly traded companies will do[1], and make money if they’re right[2][3]. In theory the thing that backs this all up is that companies have the ability to give dividends when they make profits, and that if you buy enough of a company you get the power to control it, but in practice this is all a

Keynesian beauty contest

. The values of betting markets, on the other hand, are backed by the fact that they pay out directly if you’re right.

But this distinction doesn’t really matter – there’s other things, like commodities, where trading pretty closely resembles direct bets on a belief. The bigger issue with the equivalence is that there’s

only about 3500 publicly-traded stocks

in the entire US market. For comparison, there’s about 10^40 possible chess positions, which is far m...
Forecasting Prize Results
by NunoSempere — 2021-02-19T19:07:09.420Z

The "Forecasting Innovations Prize" was announced on the 15th of November of 2020 on the

Effective Altruism Forum

and on

LessWrong

, with the goal of incentivizing valuable research around forecasting. We received 10 submissions.

Judges—AlexRJL, Eric Neyman, Tamay Besiroglu, Linch Zhang, Ozzie Gooen and myself— recommended a quantity of money to be awarded to each submission. The next section is a short summary of each entry, the prize they were assigned, and the reasons the judges gave. This is followed by a brief discussion of the judging process and takeaways.

We will be contacting authors soon.

Crowd-forecasting COVID-19

The post describes the results of a COVID-19 crowd-forecasting project created during the author's PhD. The judges didn’t know of any other app in which human forecasters could conveniently forecast different points in a time series, with confidence intervals. The project’s forecasts were submitted to the German and Polish Forecast Hub, and they did surprisingly well in comparison with other groups.

Judges brought up the issue that R/shiny is probably the suboptimal technology for a web-app. Further, as of the time the post was published neither the post under consideration nor other submissions to the German and Polish Forecast Hub were able to outperform a model that simply predicts constant cases on a four-week horizon.

This post receives a prize of $250.

Incentivizing forecasting via social media

The post explores the implications of integrating forecasting functionality with social media platforms. They consider several important potential issues in some length, and possible solutions to these, as well as indications for next steps. The scenario they consider— if it were to occur—could possibly have a large impact on the 'information economy'.

However, as the author’s note, the feasibility of the proposal is very unclear (<1%, though note that

Twitch

recently added some prediction functionality). Further, the authors were not aware of Facebook’s Forecast at the time they wrote the post.

This post receives a prize of $250.

Central Limit Theorem investigation

The post visualizes how quickly the central limit theorem works in practice, i.e., how many distributions of different types one has to sum (or convolve) to approximate a Gaussian distribution in practice. The visualizations are excellent, and give the readers intuitions ...
The Glory System:  A Model For Moral Currency And Distributed Self-Moderation
by Darklight — 2021-02-19T16:42:48.980Z

One of the most important changes in our civilized modern society over the past historical tendencies has been the state's monopoly on the use of violence. Most everyone today aside from the various strains of anarchist agree that this is a necessary and important requirement for modern life, but perhaps a problematic side effect of this change is that the use of force by individuals as a negative reinforcement tool to shape behaviour is lost. Whereas in the distant past, a slight against one's honour could be challenged by the threat of a duel, today, positive reinforcement through transfers of money are the main way human behaviour is shaped.

Markets rely on these positive reinforcement signals to aggregate information about people's preferences. But in some sense, traditional currencies are one-sided. In effect, you can only vote Yea or Abstain with your dollar. What if, you could also vote Nay?

Certainly the government can enforce fines as a way to provide negative reinforcement to bad market actors. This punishment of negative externalities is one of the strongest justifications for government intervention. But government interventions are often both heavy handed and slow to respond. What if individuals could spend money to destroy someone else's money?

This somewhat radical and odd idea forms the basis of what I propose to be the glory system. In the glory system, individuals can both transfer glory to others as a form of positive reinforcement, and spend glory to destroy other people's glory as a form of negative reinforcement. It essentially functions as a system of distributed reward and punishment, as a true method of social credit.

Previously I had considered if there was a way to implement this idea in the form of a cryptocurrency. But realistically, I have doubts that an actual currency that can disappear or be destroyed by others legally would have much interest as a store of value. So what can this idea perhaps be more reasonably applied to?

One obvious comparison to be made with the the glory system is the karma system used on Reddit and here on Less Wrong. Both allow both upvotes and downvotes in a sense. The glory system as implemented in a forum reputation system would have some interesting characteristics.

Let's assume that glory is accumulated by upvotes on you...
Bedtime reminiscences
by Kaj_Sotala — 2021-02-19T11:50:05.271Z

Things that I imagine would be cool to do with my kids (if I manage to have some): taking bedtime as a moment to reminisce about the day together.

Recalling enjoyable moments is by itself enjoyable. So ask, what parts of the day did you like? What were some good moments? What about it was enjoyable?

At first, it’ll be just me mentioning things I noticed on that day: “You seemed to really like playing with those toys today.” “You looked happy being with uncle X.”

Hopefully soon the kids will notice that this is enjoyable and start paying more conscious attention to the good moments throughout the day – and start bringing up things on their own. (And feel like that was their own idea to also contribute, rather than it just being their dad telling them these things.)

Later, start also covering the moments when they were unhappy or upset. Are they feeling okay now, anything about it that they still want or need to discuss? Even if they’re fine now, make sure to take those moments and reframe them in an explicitly accepting light (all emotions are fine, including negative ones):

“That really was upsetting for you but now you’re okay, all bad feelings pass eventually.”

“You held yourself together back there even though you were really unhappy about it, that was great. You could have chosen not to even try, but you did do it.”

“We both got a little mad at each other earlier but that’s okay, kids need to be mad at dad sometimes and sometimes dad gets mad back. I try not to, but that’s on me, and I love you no matter what.”

Then maybe recall some happy moments that we discussed when it was bedtime on earlier days, and which they might have forgotten otherwise. Keep those unhappy moments firmly sandwiched between good ones.

Also tell them about all the moments today when they made me and mom happy and how we love them. Then a bedtime story and wishing good night.

Hopefully the conversations should keep getting more sophisticated as the kids get older. Get into topics like the value of negative emotions, and what unmet need their unhappiness in that moment was a signal of. Help them brainstorm ways they could meet that need better from now on.

Then one day when they’re adults, hopefully they’ll be so firmly in the habit of going through the good moments and the lessons-in-the-bad-moments that they won’t need me for it anymore, and it has just become automatic.

And if it hasn...
Oliver Sipple
by KatjaGrace — 2021-02-19T07:00:18.788Z

The other day I read Wikipedia arguably too much, and consequently came to know the story of Oliver Sipple. Here’s my summary of the story according to these two Wikipedia pages and this page:

In the September of 1975, Oliver (‘Billy’) Sipple was an ex-marine of thirty-three, injured in Vietnam and living in San Francisco. He was in and out of the veteran’s hospital, six years into civilian life.

One afternoon, he stood in a crowd of thousands of people to see the visiting President Gerald Ford leave a San Francisco hotel from across the street. Ford stopped to wave. Suddenly, a shot sounded, and Oliver saw a woman nearby adjusting the aim of her revolver. He lunged and grabbed her arm, sending the second bullet into the hotel, injuring a man inside.

Oliver was thanked for saving the president, and celebrated as a hero by the media. A heroic veteran.

Soon the media learned that he was in fact a heroic gay veteran.

Oliver had shared his sexual orientation with with the San Francisco gay community—or at least he had worked at a gay bar, paraded for gay pride, demonstrated for gay rights, helped in the (LGBT) Imperial Court System, and worked on the campaign to elect openly gay board of supervisors candidate Harvey Milk. But he hadn’t shared it with his family in Detroit, who had more old-fashioned impressions about the morality of homosexuality. He also hadn’t shared it with the world at large, who after all, lived at a time when evidence of a gay person being a public hero was considered fascinating news.

How did the media learn about this? Perhaps there were many sources, or would have been eventually. But the morning after the shooting, two prominent gay activists each outed Oliver to the San Francisco Chronicle. One was Reverend Ray Broshears, leader of the ‘Lavender Panthers’. The other was Oliver’s own friend, Harvey Milk.

!

Harvey is reported to have explained privately to a friend, “It’s too good an opportunity. For once we can show that gays do heroic things, not just all that caca about molesting children and hanging out in bathrooms.”

The next day, Herb Caen, the San Francisco Chronicle reporter who received these messages, reported to the world that Oliver was gay. He added that Oliver was friends with Harvey Milk, and speculated that President Ford hadn’t invited him to the White House because of his sexual orientation.

Somewhere in here, Oliver asked that ...
The Ultimatum Game
by thetwelve — 2021-02-18T18:36:53.789Z

l

The Ultimatum Game is a simple game in which two players attempts to split a $100 reward. They can communicate with each other for 10 minutes, after which:

Player 1 proposes an integer split (e.g. $75 for Player 1, $25 for Player 2)
Player 2 may Accept or Reject this split. If Player 2 rejects, both players receive nothing. Otherwise, the money is distributed according to the agreed-upon split.

At first glance, the mathematical analysis is simple: Player 2 should always accept (since anything is better than nothing), so Player 1 should offer a 99-to-1 split to maximize their winnings.

Much of the commentary around this game revolves around the fact that when you play this game with humans, Player 2's sense of "fairness" will cause them to "irrationally" reject sufficiently imbalanced splits.

But this post isn't about people's feelings. It's about rational agents attempting to maximize wealth. (I don't doubt that all these ideas have been discussed before, though in most LW posts I found with this game in it, the game itself is not discussed for more than a paragraph or two).

A Veto is a Powerful Bargaining Chip

If you're Player 2 and you want to walk away with more than $1, what do you do?

It's pretty simple, actually - all you need to do is to immediately communicate to Player 1 that you've sworn an Unbreakable Vow that you will reject anything other than a 99-1 split in your favor. (Or, more practically, give Player 1 a cryptographic proof of a cryptographic contract that destroys $1000 if you accept anything other than 99-1.) And just like that, the tables are turned. Player 1 now gets to decide between walking away with $1 or walking away with nothing.

This style of play involves reducing your options and committing to throwing away money in a wide variety of scenarios. But against a Player 1 who's as naive as the original analysis's Player 2, it works. It's the madman theory of geopolitics - sometimes the best move is to declare yourself crazy.

Examples

This game corresponds fairly directly to the idea of economic surplus: in a positive-sum transaction, both sides want the transaction to go through, but there remains the lingering question of how to split the surplus.

I unfortunately read the news a lot, so I see a lot of big companies and governments getting into fights with this shape.

Apple vs Epic Game

...
Utility Maximization = Description Length Minimization
by johnswentworth — 2021-02-18T18:04:23.365Z

There’s a useful intuitive notion of “optimization” as pushing the world into a small set of states, starting from any of a large number of states. Visually:

Yudkowsky

and

Flint

both have notable formalizations of this “optimization as compression” idea.

This post presents a formalization of optimization-as-compression grounded in information theory. Specifically: to “optimize” a system is to reduce the number of bits required to represent the system state using a particular encoding. In other words, “optimizing” a system means making it compressible (in the information-theoretic sense) by a particular model.

This formalization turns out to be equivalent to expected utility maximization, and allows us to interpret any expected utility maximizer as “trying to make the world look like a particular model”.

Conceptual Example: Building A House

Before diving into the formalism, we’ll walk through a conceptual example, taken directly from Flint’s

Ground of Optimization

: building a house. Here’s Flint’s diagram:

The key idea here is that there’s a wide variety of initial states (piles of lumber, etc) which all end up in the same target configuration set (finished house). The “perturbation” indicates that the initial state could change to some other state - e.g. someone could move all the lumber ten feet to the left - and we’d still end up with the house.

In terms of information-theoretic compression: we could imagine a model which says there is probably a house. Efficiently encoding samples from this model will mean using shorter bit-strings for world-states with a house, and longer bit-strings for world-states without a house. World-states with piles of lumber will therefore generally require more bits than world-states with a house. By turning the piles of lumber into a house, we reduce the number of bits required to represent the world-state using this particular encoding/model.

If that seems kind of trivial and obvious, then you’ve probably understood the idea; later sections will talk about how it ties into other things. If not, then the next section is probably for you.

Background Concepts From Information Theory

The basic motivating idea of information theory is that we can represent information using fewer bits, on average, if we use shorter representations for states which occur more often. For instance, Morse code uses only a single bit (“.”) to represent the lette...
Formal Solution to the Inner Alignment Problem
by michaelcohen — 2021-02-18T14:51:14.117Z

We've written a paper on online imitation learning, and our construction allows us to bound the extent to which mesa-optimizers could accomplish anything. This is not to say it will definitely be easy to eliminate mesa-optimizers in practice, but investigations into how to do so could look here as a starting point. The way to avoid outputting predictions that may have been corrupted by a mesa-optimizer is to ask for help when plausible stochastic models disagree about probabilities.

Here is the abstract:

In imitation learning, imitators and demonstrators are policies for picking actions given past interactions with the environment. If we run an imitator, we probably want events to unfold similarly to the way they would have if the demonstrator had been acting the whole time. No existing work provides formal guidance in how this might be accomplished, instead restricting focus to environments that restart, making learning unusually easy, and conveniently limiting the significance of any mistake. We address a fully general setting, in which the (stochastic) environment and demonstrator never reset, not even for training purposes. Our new conservative Bayesian imitation learner underestimates the probabilities of each available action, and queries for more data with the remaining probability. Our main result: if an event would have been unlikely had the demonstrator acted the whole time, that event's likelihood can be bounded above when running the (initially totally ignorant) imitator instead. Meanwhile, queries to the demonstrator rapidly diminish in frequency.

The second-last sentence refers to the bound on what a mesa-optimizer could accomplish. We assume a realizable setting (positive prior weight on the true demonstrator-model). There are none of the usual embedding problems here—the imitator can just be bigger than the demonstrator that it's modeling.

(As a side note, even if the imitator had to model the whole world, it wouldn't be a big problem theoretically. If the walls of the computer don't in fact break during the operation of the agent, then "the actual world" and "the actual world outside the computer conditioned on the walls of the computer not breaking" both have equal claim to being "the true world-model", in the formal sense that is relevant to a Bayesian agent. And the latter formulation doesn't require the agent to fit inside world that it's mo...
Covid 2/18: Vaccines Still Work
by Zvi — 2021-02-18T12:30:00.789Z

This week the CDC released new guidelines for schools. I’ve spun my analysis of that off into its own post. Scott Alexander also shared some good thoughts on Covid-19 in two new posts, and I discuss both of those, and how our models and predictions differ.

Also, as some combination of retaliation and its continued crusade against the evils of the tech industry and the notion of freedom of speech, the New York Times finally published its hit piece on Scott Alexander. I devote a brief section to it, the upside of which is that the paper is now permanently banned from this space.

Oh, right, you came here to learn about Covid-19 news. The short news is great. Infections and deaths are down much faster than anticipated. The vaccine news is less great. The process continues to accelerate, but that acceleration was slower than expected, and there’s still lots of FUD about what vaccinated people can and can’t ‘safely’ do and little movement yet towards saner policy in other ways, although there are signs of moving in that direction. The new strains aren’t here in force yet, but it seems likely that will happen soon.

One potentially big thing we learned is the study from infected NBA players, which suggests that the English strain causes infections to play out over a longer period of time than the classic strain, with all the implications of that.

Another was that it got even clearer than it already was that the Pfizer and Moderna vaccines are highly effective after only one dose, and that First Doses First would be a vast improvement while we continue to have much less vaccine than we need.

The Numbers

Predictions

Results last week: 6.4% positive rate on 11.3 million tests, and an average of 2,752 deaths after adjusting for Indiana.

Prediction: 5.7% positive rate and an average of 2,450 deaths. Things should still continue to improve.

Result: 5.2% positive test rate on 10.4 million tests, and an average of 2,089 deaths. This is after removing Ohio’s adjustments that add additional deaths from previous months, similar to Indiana’s adjustments from last week.

I’m straight up adjusting the results rather than listing both answers, because the adjustment makes my prediction less accurate. When adjustments like this make predictions more accurate, it seems necessary to list both results to keep oneself honest.

This is an extraordinarily good result. After several ...
Neck abacus
by KatjaGrace — 2021-02-18T07:40:13.066Z

My points for anxiety system continued to help, but was encumbered by the friction of getting my phone out to mark points. Thus I have turned to wearable abaci.

I made this necklace according to very roughly this picture, using knitting wool and beads I bought at one point to use as virtual in-house currency and now found in a box in my room. It works well! The beads don’t shift unless I move them, which is easy and pleasing. It seems clearly more convenient than my phone. (Plus, I can show off to those in the know that I have 4 or 6 or 24 or 26 of something!) I am also for now reminded when I look in a mirror to consider whether I can get a point, which is currently a plus.

You can buy bracelet versions in a very small number of places online, and also keychain or general hanging clip-on versions, but I don’t think I saw necklaces anywhere. This seems striking, given the clear superiority to a phone counter for me so far, and the likely scale of phone counter usage in the world.