Subsidizing Prediction Markets

Epistemic Status: Sadly Not Yet Subsidized

Robin Hanson linked to my previous post on prediction markets with the following note:

I did briefly mention subsidization, as an option for satisfying the fifth requirement: Sources of Disagreement and Interest, also known as Suckers At The Table. The ultimate sucker is an explicit, intentional one. It can serve that roll quite well, and a sufficiently large subsidy can make up for a lot. Any sufficiently large sucker can do that – give people enough profit to chase, and suddenly not being so well-defined or so quick or probable to resolve, or even being not safe from key insider information, starts to sound like it is worth the risk.

Suppose one wants to create a subsidized prediction market. Your goal presumably is to get a good estimate for the probability distribution of an event, and to do so without paying more than necessary. Secondary goals might include building up interest and a marketplace for this and future prediction markets, and getting a transparently robust result, so others or even the media are more likely to take the outcome seriously. What is the best way to go about doing this?

Before looking at implementation details, I’ll look at the five things a prediction market needs.


Well Defined

The most cost-efficient subsidy for a market is to ensure that the market is well defined. Someone has to make sure everyone understands exactly what happens under every scenario, and that someone is you. Careful wording and consideration of corner cases is vital. Taking the time to do this right is a lot more efficient than throwing money at the problem, especially trying to build a system and brand over time.

If you’re going to subsidize a market, step one is to write good careful rules, make sure people understand them, and to commit to making it right for everyone if something goes wrong, if necessary by paying multiple sides as if they had won. This is potentially quite expensive even if it rarely happens, so it’s hard to budget for it, and it feels bad in the moment so often people don’t pull the trigger. Plus, if you do it sometimes, people will argue for it all the time.

But if you’re in it to win it, this is where you start.


Quick Resolution

Once you’ve got your definitions settled, your next job is to pay the winners quickly once the event happens. People care about this more than you can possibly imagine. The difference between paying out five seconds after the final play, and five minutes after the final play, is a big game to many. Make them wait an hour and they’ll be furiously complaining on forums. When the outcome is certain, even if it hasn’t actually happened yet, it’s often a great move to pay people in advance. People love it. Of course, occasionally someone does something like pay out on bets on Hillary Clinton two weeks early, in which case you end up paying both sides. But great publicity, and good subsidy!

Another key service is to make sure your system recognizes when a profit has been locked in, or risk has been hedged, and does not needlessly tie up capital.

This one is otherwise tough to work around. If you want to know what happens twenty years from now, nothing is going to make resolving the question happen quickly. You can help a lot by ensuring that the market is liquid. If I buy in at 50% now, then a year from now the market is at 75% and is liquid enough, I can take my profits in one year rather than twenty. That’s still a year, and it’s still unlikely the price will ‘catch up’ fully to my new opinion by that time. It helps a lot, though.


Probable Resolution

It is a large feel bad, and a real expense, when capital is tied up and odds look good but then the event doesn’t happen, and funds are returned. It hurts most when you’ve pulled off an arbitrage, and you win money on any result.

If, when this happens, you subsidize people for their time and capital, they’d be much more excited to participate. I think this would have a stronger effect than a similar subsidy to the market itself, once you get enough liquidity to jump start trading. Make sure that if money gets tied up for months or years, that it won’t be for nothing.


Limited Hidden Information

If your goal is to buy the hidden information, you might be all right with others not being interested in your market, as long as the subsidy brings the insiders to the table to mop up the free money. That approach is quite expensive. If the regular traders are still driven away, you’ll end up paying a lot to get the insiders to show their hand, because they can’t make money off anyone else. Even insiders start to worry that others have more and better inside information than they do, which could put them at a disadvantage. So it’s still important to bring in the outsiders.

One approach is to make the inside information public. Do your own investigations, require disclosure from those participating in the events themselves, work to keep everyone informed. That helps but when what you want to get at is the inside information it only goes so far.

That means that when this is your problem, and you can’t fix it directly through action and disclosure, you’re going to have to spend a lot of money. The key is to give that money to the outsiders as much as possible. They are the ones you need at the table, to get yourself a good market. The insiders can then prey on the outsiders, but that’s much better than preying on you directly.

The counterargument, especially if you don’t need to show liquidity or volume, is that if you buy the information directly there’s less noise, so perhaps you want to design the system to get a small number of highly informed traders and let everyone else get driven away. In cases where the outsiders would be pure noise, where the insiders outright know the answer, and where getting outsiders to be suckers that take a loss isn’t practical, that can be best.


Disagreement and Interest

This one’s easy. You are paying a subsidy, so you’re the sucker. Be loud about it so everyone knows you’re the sucker, and then they can fight to cash in. Excellent.

The other half, disagreement, is still important. Many people, whose analysis and participation you want, still benefit from a story that explains why they are being paid to express an opinion, rather than fighting to be slightly more efficient at capturing the subsidy. And of course, if no one disagrees about the answer, then your subsidy was wasted, since you already knew the answer!

In light of those issues, what are the best ways to subsidize the market?

Option 0: Cover Your Basics

Solve the issues noted above. Choose a market people want to participate in to begin with. Ensure there are carefully written rules with no ambiguity, that any problems there are covered. Make sure you’ll get things resolved and paid quickly, that capital won’t be tied up one minute longer than necessary. When possible, disclose all the relevant information, on all levels. If things don’t resolve, it would be great if you could compensate people for their time and capital.

And also, make sure everyone is confident the winners will be paid! Nothing kills a market like worrying you can’t collect if you win. That’s often as or more important even than providing strong, reliable liquidity.

If you can improve your interface, usability, accessibility, user’s tax liability or anything like that, definitely do that. If your market design is poor, such as having the wrong tick size, make sure to fix that. Tick sizes that are too small discourage the providing of liquidity to the market, and are in my experience a bigger and more common mistake than ticks that are too big.

Finally, waive the fees. All of them. Deposit fees, withdraw fees, trading fees, you name it. At most, there should be a fee when taking liquidity that is paid entirely to the trader providing liquidity. People hate paying fees a lot more than they like getting subsidies. They won’t cancel out.

With that out of the way, what are your options for the main subsidy?

Option 1: Be a Market Maker and Provide Liquidity Directly

As the subsidizer of the market, commit to being the market maker with well-defined rules.

The standard principle is, let everyone know that there will always be $X of liquidity available on both sides, and at a fixed cost of Y% price difference between your bid and your offer. So for example, you might agree to offer $1,000 on each side with a difference of 5% at all times, starting with a 48% bid and a 53% offer. You’d then adjust as you did trades.

A simple rule to protect yourself from unlimited downside is if you do a trade for some percent of your liquidity, you adjust your price that percentage of its width. So in this example, if someone took 40% of your offer, you’d adjust by 40% of 5%, which is 2%, and now have a 50% bid and a 55% offer. If you follow such a rule, your maximum loss is what it takes to move the odds to 0% or 100% (and if you let people keep trading until the event is done, you will take that loss). People trading against you in opposite directions can make you money, but can’t cost you money.

For convenience, you can post additional bids and offers so that if someone wants to move the odds a lot, they can see what liquidity they would get from you, and have the option to take it all at once. You’ll lose money every time the fair probability changes, but that’s why they call it a subsidy, an this encourages people to show their information quickly and efficiently.

There are ways to make that smarter, so you can lose less (or make more!) money while offering better liquidity, which will be left as an exercise to the reader. Generally they sacrifice simplicity and transparency in order to make the subsidy ‘more efficient.’ The danger is that if the subsidy is attempting to ensure a sucker is at the table, it does not do that if it stops being the sucker, or it becomes too hard to tell if it is one or not.

Then again, the dream is to offer a subsidy that doesn’t cost you anything, or even makes you money! Market making can be highly profitable when done skillfully, while also building up a marketplace.

Option 2: Take Liquidity

If you provide liquidity, others will take advantage, but in some ways you make it harder to provide liquidity. If you take liquidity, you make it more profitable to provide it, at the risk of making the market look less liquid.

It also loses money. The more clear you are about what you are up to, the better.

There are a few fun variants of this, if you’re all right with the expense.

One strategy is to take periodically liquidity in both directions. At either fixed or random intervals, examine the order books in the market. If they meet required conditions (e.g. there is at least $X on the bid and offer within Y% of each other) then you hit the bid and lift the offer for $Z.

This costs you money, since your trades net out at a loss. If someone else was both the best bid and best offer, they made money.

That’s the idea. You’re directly subsidizing people to aggressively provide liquidity.

Traders compete to be on the bid and offer to trade with you, the virtual customer, which in turn gives those with an opinion a liquid market to trade against. Sometimes people get far too aggressive providing in such situations, and those trying to capture the subsidy end up losing money because they make bad trades against others, especially if they don’t then hedge.

You can also do this in a more random or unbalanced fashion. If you flip a coin each day and decide whether to be a buyer or a seller, that will cause the price to temporarily become ‘unfair’ to satisfy your demand – you’ll get a bad price. But that creates a trading opportunity for others. It can also make the results hard to interpret, which is a risk.

Option 3: Subsidize Trading / Give Free Money

Often you’ll see crypto exchanges do this as a promotion, offering a prize to whoever trades the most of some coin. By paying for trades, you’re encouraging exactly what you want.

Except that you’re probably not doing that. Remember Goodhart’s Law.

The problem is ‘wash’ trading, where people trade with each other or themselves without taking on positions. This is bad on every level. It misleads everyone about the volume and price, and doesn’t help at all with finding out the answer to the question the market is trying to answer. The last thing you want to do is encourage it!

For that reason, subsidizing trading itself is a dangerous game. But it can be done, if you’re careful with the design.

Many online sites have tried this in the form of the classic ‘deposit bonus’ or even the free play. Anyone can sign up and get Free Money in exchange for engaging in a minimum amount of trading activity. And of course, most of the time, a deposit to match, if the offer is more than a small ‘free play.’ In for-profit markets the goal is to have the required activity make up for the subsidy, then hopefully hook the customer to keep them trading. There are always those looking to game these offerings if you leave them vulnerable.

That can work for you. Getting those same people, who are often quite creative and clever, thinking about how to come out ahead in your system can be a big win if your end goal isn’t profit! So long as you make it sufficiently difficult to do wash trading or sign up for tons of copies of the bonuses, you can give them a puzzle worth maximizing (from their perspective) and effectively rent their labor to see what they think of the situation.

Option 4: Subsidize Market Making

You can also subsidize market making activity, as an alternative to doing the job yourself and butchering it. That’s activity you can’t fake, provided you set the rules carefully. Paying people who provide rather than take liquidity is good, and often paying for real two-sided market making activity is better. As always, make sure you’re not vulnerable to wash trading or other forms of collusion.

Option 5: Advertising

People can’t trade what they aren’t thinking about or don’t know about.

Putting It All Together

Which of these strategies is most efficient and what circumstances change that answer?

It’s expensive to change or clarify your rules and conditions once trading has begun, so invest in doing that first. Other quality of life improvements are great, but take a back seat to establishing good liquidity.

I list Option 0 first because it’s things you definitely should do if you’re taking the operation seriously, but that doesn’t mean you always do all of them first before the direct subsidy. It’s great if you can, but often you need to establish liquidity first.

If ‘no liquidity’ is the pain point and bad experience, there isn’t much that will overcome that. There’s no market. So if you don’t have liquidity yet, providing at least a reasonable amount, or paying someone else to do it, is the best thing you can do. Just throw something out there and see what happens. This makes intuitive sense all around – as an easy intuition pump, if you want to know if something is more likely than not, offering someone a 50/50 bet on it is a great way to get their real opinion.

Once liquidity isn’t a full deal breaker, it’s time to go with Option 0, then return to increasing the subsidy and spreading the word.

What form should the direct subsidy take?

I’d advise to continue to take away bad experiences and barriers first.

The best subsidy is paying to produce reliable, safe and easy to use software, getting ironclad rules in place, being ready to handle deposits, withdraws, evaluation of results and other hassles. Make sure people can find your markets and set up the markets people want to find.

Next best is to avoid fees. People hate fees more than they love subsidies. Yes, you can trick people with deposit bonuses and then charge them a lot on their trades, but the best way to get away with that is bake the fees into the trade prices, so it doesn’t look like a fee.

At a minimum, you shouldn’t be charging fees for deposits or withdraws, or for providing liquidity in the market.

Next up, make trades cost net zero fees. Either charge nothing to provide or to take liquidity, or charge a fee to take liquidity but pay it to those who provide.

After that, my opinions are less confident, but here’s my best guess.

If that’s still not good enough, provide liquidity. Either pay someone else to be a market maker, or provide the service yourself. I like the idea of a ‘dumb’ market maker everyone knows is dumb, and that operates with known rules that hamstring it. If you’re looking to provide a subsidy, this is a great way to do that. A smarter market maker is cheaper, and can provide better liquidity, but is less obviously a target. As the market matures, you’ll want to transition to something smarter. Thin markets want obviously dumb providers.

Once you’ve done a healthy amount of that, then you’ll want to give away Free Money. Give people some cash in exchange for participating in the market at all, or trading a minimum amount. Or give people bonuses on deposited funds so long as they use them to trade, or similar.

You have to watch for abuse. If you can respond to abuse by changing the system, it’s fine to be vulnerable to abuse in theory, and even allow small amounts of it. If you’re going to release a cryptographic protocol you can’t alter, you’ll need to be game theoretically robust, so this won’t be an option, and you’ll have to retreat to taking liquidity.

Taking liquidity seems less likely to motivate the average potential participant, and costs you weirdness points, but does provide a strong incentive for the right type of trader. The best reason I can think of to use such a strategy is that it is robust to abuse. That’s a big game if you can’t respond dynamically to unfriendly players.

At the end of the day, your biggest barriers are that people’s attention is limited, complexity is bad, opportunity cost is high and people don’t do things. I keep meaning to get around to bothering with HyperMind and/or PredictIt, and keep not doing it, and I’m guessing I am far from alone in that. Subsidy can get people excited and make markets work that wouldn’t otherwise get off the ground. What I think they can’t do at reasonable cost is fix fundamental problems. If you don’t have a great product behind the subsidy, it’s going to be orders of magnitude more expensive to motivate participation.




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Tidying One’s Room

Previously (Compass Rose): Culture, Interpretive Labor, and Tidying One’s Room

Epistemic Status: A bit messy

She’s tidied up and I can’t find anything! All my tubes and wires, careful notes!” – Thomas Dolby, She Blinded Me With Science

From Compass Rose:

Why would tidying my room involve interpretive labor? 

It turns out, every item in my room is a sort of crystallized intention, generally past-me. (We’ve all heard the stories of researchers with messy rooms who somehow knew where everything was, and lost track of everything when someone else committed the violent act of reorganizing the room, thus deindexing it from its owner’s mind.) As I decided what to do with an item, I wanted to make sure I didn’t lose that information. So, I tried to Aumann with my past self – the true way, the way that filters back into deep models, so that I could pass my past self’s ideological turing test. And that’s cognitively expensive.

It’s generally too aggressive to tidy someone’s room without their permission, unless they’re in physical danger because of it. But to be unwilling to tidy my own room without getting very clear explicit permission from my past self for every action – or at least checking in – is pathologically nonaggressive.

From my wife, upon seeing the draft up to this point:

You know, in the time it takes you to write this, you could actually tidy your room.

Proof that the subject of cleaning, and cleaning that which does not belong to you, can escalate quickly in aggressiveness!

There are a few dynamics I’d like to talk about here. I won’t (today) be relating them back to Ben’s larger questions of how generally to deal with the intentions of the environment, instead choosing a more narrow scope.



Your past self left you an ideological Turing test, of a sort, by leaving items in seemingly random locations.

Good news! I have the cheat sheet.

Close, but wrong: “I’ll remember that it’s there and I’m too lazy to optimize its location further.”

Usually correct: “I’m done with this, I should put it somewhere. This is somewhere.”

Don’t give your past self too much credit.

Most things are (hopefully) where they are because you put them there on purpose. That’s where they ‘live’. If they’re not in a permanent location, they’re probably in an arbitrary location.

One should think about intention behind the current location of a thing if and only if the location was clearly chosen intentionally. 

If the location doesn’t seem random, this is probably why: “I predicted I’d look here for this item in the future. This is where I seemed to have indexed it.”

Ben worried he needed to pass an ITT against his past self before he could alter his past self’s wishes.

I think that’s backwards. Past you’s work is done. The key ITT is against your future self!



Whether or not a location was chosen carefully, it has the great advantage of memory. If you put something somewhere, there’s a good chance that’s where you will look for it. If you put it there regularly, that chance is better still.

This is why ‘tidying’ someone’s room for them is an act of aggression. 

If I’m the one who put a thing somewhere, I could figure out where it is by remembering where I put it, or asking where I had it last (which my family called ‘The Papa Josh method’ as if it wasn’t universal, but specific names are still useful, and Papa Josh was apparently kind of an annoying jerk about it). I could also pass the ideological Turing test of my past self and figure out where I would have chosen to put it.  Since, philosophical objections aside, I am me, my chances are often very good.

If I have a strong indexing of an item to a location, I’ll instinctively put it back in the same location, confident I can find it in the future. My ability to automatically look in the right place, and find it now, is good evidence of that. If it was hard to find, I should probably move it. Over time, this improves indexing.

If someone else puts the object somewhere, I now have to figure out where someone else would place the object. Over time, if they keep doing this, I’ll figure out where they put it, but when a new person starts cleaning a location, chaos reigns. What is logical to them is not what is logical to you.

An especially nasty trap is when you’re not sure if you know where an object is, so you check, it is where you look for it, then put it back in a different location. Oh good, you think, I have it, I’ll now put it over here. Classic mistake. If an object is in the first place you look, and you need to find it soon, put it back exactly where you found it! If an object isn’t in the first place you look, put it in the first place you looked! You’ll look there again.

Otherwise, what you are doing is systematically taking things you can find, and moving them to locations where you might not find them. Whereas if you fail to find them, you won’t move them, and they’ll stay not found. This is why you can’t find the remote – it keeps moving randomly until it finds a place where you can’t find it, then stays there until you figure that one out. Repeat.

It took way too many times when the only thing I needed was reliably in the wrong pocket for me to figure out how this works.



As a child in the days before the internet, I would keep stacks of sports and gaming magazines in my room. In order to quickly locate the one I wanted, I’d spread them out so part of the cover was visible on each copy, allowing a quick visual scan.

Then someone would, against my will, come in and ‘clean’ the room, stacking them all into one pile with no way to tell which one was which.

So the moment I came back, I’d undo the pile and spread them back out again, since the pile was almost optimizing for lack of legibility.

I’d complain about this all the time, and make my wishes clear, and the stack would reassemble twice a week anyway.

Space, especially visual space, is a resource. Using it draws things to your attention. That’s good if you want to find them! It also threatens to distract. It gives the appearance of clutter, and threatens to clutter the mind.



It is tempting to ‘tidy’ one’s room, to give appearance of tidiness, or to clear necessary space, by accumulating debt. You shove things aside or into closets, rather than putting them in a place that is helpful. Even sorting things into seemingly organized piles is still debt, if you don’t know the indexing and won’t be able to find them. At some point you’ll be paying search costs.

If you are not careful, this debt will accumulate, and interest on it will add up. It is hard to get motivated to pay down such debts, even when returns are good.

It is also tempting to ‘tidy’ that which does not need to be fixed, or to let this task distract you as a way to procrastinate other tasks.

My solution is simple. Any time you look for something, you give yourself a reasonable amount of time to find it. If after that time you cannot find it, but you are confident it is there to be found, you stop looking for the item and instead clean the room (at least) until you find the item. This inevitably finds the item and creates equilibrium – the more you need to clean, the more likely you are to do so. If you can always find everything, then everything is fine.



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Prediction Markets: When Do They Work?

Epistemic Status: Resident Expert

I’m a little late on this, which was an old promise to Robin Hanson (not that he asked for it). I was motivated to deal with this again by the launch of Augur (REP), the crypto prediction market token. And by the crypto prediction market token, I mean the empty shell of a potential future prediction market token; what they have now is pretty terrible but in crypto world that is occasionally good for a $300 million market cap. This is, for now, one of those occasions.

The biggest market there, by far, is on whether Ether will trade above $500 at the end of the year. This is an interesting market because Augur bets are made in Ether. So even though the market (as of last time I checked) says it’s 74% percent to be trading above $500 and it’s currently $480 (it’s currently Thursday, July 26, and I’m not going to go back and keep updating these numbers). When I first saw this the market was at 63%, which seemed to me like a complete steal. Now it’s at 74%, which seems more reasonable, which means the first ‘official DWATV trading tip’ will have to wait. A shame!

A better way to ask this question, given how close the price is to $500 now, is what the ratio of ‘given Ether is above $500 what does it cost’ to ‘given Ether is below $500 what does it cost’ should be. A three to one ratio seems plausible?

The weakness (or twist) on markets this implies applies to prediction markets generally. If you bet on an event that is correlated with the currency you’re betting in, the fair price can be very different from the true probability. It doesn’t have to be price based – think about betting on an election between a hard money candidate and one who will print money, or a prediction on a nuclear war.

If I bet on a nuclear war, and win, how exactly am I getting paid?

Robin Hanson, Eliezer Yudkowsky and Scott Sumner are big advocates of prediction markets. In theory, so am I. Prediction markets are a wonderful thing. If you’re not familiar with them or want to read more, a good place to start is this wiki. By giving people a monetary incentive to solve problems and share information, we can learn probabilities (what will GDP be next year?) and conditional probabilities (what will GDP be next year if we pass this tax cut bill?) and use the answers to make the best decision. This method of making decisions is called futarchy.

Formally, a prediction market allows participants to buy and sell contracts. Those contracts then pay out a variable amount of money. Typically this is either binary (will Donald Trump be elected president?), paying out 100 if the event happens and 0 if it doesn’t, or they are continuous (how many electoral college votes will Donald Trump get?) and pay proportionally to the answer. Sometimes there are special cases where the market is void and all transactions are undone, at other times strange cases have special logic to determine the payout level.

There are three types of prediction markets that have gotten non-zero traction.

The first is politics. There are markets at PredictIt and BetFair and Pinnacle Sports, and there used to be relatively deep markets at InTrade. These markets matter enough to get talked about and attract some money when they involve major events like presidential elections, but tend to be quite pathetic for anything less than that.

The second is economics. There are lots of stocks and futures and options and other such products available for purchase. Futures markets in particular are prediction markets. They don’t call themselves prediction markets, but that is one of the things they are, and the information they reveal is invaluable. It’s even sometimes used to make decisions.

The third is sports. Most televised sporting events have bookmakers offering odds and taking bets. They use their own terminology for many things, but these are the closest thing to true prediction markets out there.

What makes a successful prediction market? What makes an unsuccessful prediction market? When are they efficient? What gets people involved?

To get a thriving market, you need (at least) these five things.



If you can’t exactly define the outcome, you can’t have a prediction market. Even highly unlikely corner cases must be resolved. Thus, if you want a market on “Donald Trump is elected president of the United States in 2020” you need to know exactly what happens if he dies after the election but before inauguration, or if there is a revolt in the electoral college, or if the election is fraudulent or cancelled, or if he loses to a different person also named “Donald Trump.”  That’s not because Trump makes such issues more likely. If you were betting on Obama vs. Romney, you’d need to do all the same stuff.

In sports markets, this means writing up a multi-page document detailing what happens when a game is rained out, disputed, postponed, tied, you name it, along with all the other rules. If there’s an angle left ambiguous, you can bet (well you can’t, but if you could, you’d have good odds) that someone will try to take advantage of it eventually. That leaves everyone mad and ruins good business relationships. It’s important to have clear rules and stick to them.

One of the first markets on Augur asked, “Will England defeat Croatia in the World Cup?” Which I immediately recognized as a really bad wording, because it was ambiguous. If the game had gone to overtime, or even to penalty kicks, and England had advanced, what happens? In a real sportsbook, generally bets default to regulation time only, so the match would be ruled a draw, and the answer would be “No” even if England later won.

That’s not acceptable. All it takes is one corner case to get people yelling at each other, and drive them away. When they do happen, the sportsbook is wise to eat the loss, even if that means paying both sides, and then fix its procedures. That’s one benefit of having a central authority to blame.

I’m also including in this the requirement that you can be confident you can collect if you win. As one sportsbook’s slogan puts it, sweat the game, not the payout. Bets with people who might not pay you require huge edges. They’re about accepting the risk to land the whale. They don’t do much for price discovery, and are for professionals only.


Quick Resolution

The faster the market pays out, the more interest you’ll get. Markets that tie up money for weeks or months, let alone years, see decisive declines in participation. Before the season, there are markets for which teams will win the championship, or how many wins each team will have. This seems great, since if you’re right you can have a huge advantage, and it gives you an interest in games for much or all of the season.

Despite that, you will see less money wagered on any given season win total than for a random game played by that team. Usually by an order of magnitude. That’s how valuable quick resolution is. Even in March Madness, where everyone fills out office brackets, the bulk of the real wagering goes game by game. There are lots of propositions, and there’s value there, but only for small amounts. Betting your bracket before the tournament, despite brackets being something presidents do to show they’re in touch, isn’t a thing for serious money.

Thus, markets on small events like individual games are bigger, and more efficient, than markets on bigger and more interesting things like entire seasons or even a playoff series. The primary purpose of the long-term markets isn’t to make money; it’s to provide a service so people can see what odds have been assigned to various outcomes.

Major events like presidential elections have enough inherent interest to still see solid markets, but only barely. There’s a lot of interest in what the odds are, but the volumes traded are quite thin, so much so that it is in the interest of partisans to trade in order to move the price and thus change the political narrative.

Economic markets are the only place longer-term markets prosper.

Note that if the market is sufficiently liquid, it can act as if it is short term, provided the prices will move quickly enough, since participants can then exit their positions.


Probable Resolution

Trading in a prediction market ties up capital, creates risk and requires optimization pressure. I need to pay attention to the market, both to decide what fair value is and then to go about maximizing and making good trades.

If that market is conditional, and trades were only valid if those conditions were met, we have a problem: I’ve wasted my time, money and risk capacity, and gotten nothing in return.

One of the markets I liked a lot as a gambler was called the Home/Away line in MLB. The idea was, you added up all the runs scored by the home teams and compared them to all the runs scored by the away teams, and bet on which would be higher, or on the sum of all runs scored that day, which was called The Grand Salami. There was lots of value in these lines because people were using very simple heuristics, and if you did first-level statistics on runs scored in games and how distributions add up, you could get a big edge.

What was continuously frustrating was that often one game would get rained out, cancelling all your wagers. Often I’d have locked in large profits, and they’d be lost.

This wasn’t enough to keep me from betting, because I got my money back within a day and the edge was huge. But when funds were tight, it shifted those funds towards other things, and every time I thought about looking at the Home/Away line, my brain fired back ‘are you sure you want to bother?’ so I only cared when my edge was large.

Gamblers actively prefer betting on odds that can’t tie, e.g. betting on a football team -3.5 or -2.5 rather than -3.0, because the -3 line ties 10% of the time. The bookmaker agrees!

If you are instead tying up your money for weeks, months or even years, and instead of a 10% chance of rain somewhere there’s a 50% or even 90% chance the event doesn’t fire, that’s much worse. If your’e dealing with a hyper-complex Hansonian death trap of a conditional market where it’s 99%+ to not happen, even with good risk measurement tools that don’t tie up more money than necessary, no one is going to want to put in the work and tie up the funds.


Limited Hidden Information

Insider trading of securities is illegal. This seems at odds with price discovery. If I know something you don’t know, then my not trading on it makes the price less accurate. One might suggest that allowing insiders to trade would make the price more efficient.

The problem is that it drives people away.

If other people know something important I don’t, then my trades are giving them a way to pick my pocket. When I look at the price and see it is wrong, my prior is ‘there is something I don’t know’ rather than ‘there is something they don’t know or understand’. I’m the one making the mistake. I’m the sucker. So I walk away.

Thus, while the individual trades of insiders make the market more efficient, they punish others trying to share their information and analysis with the market. This is bad. Bad enough to kill outright markets with too much risk of insider information.

The first season of Survivor, there was a market on who would win. The production crew found out. Then there was no market.

Another important case: If a person with a large role in choosing the outcome can bet in the market, you might not want to risk betting against him. Or bet at all.

When there is a big injury risk in a game, the market dies until the issue is resolved. When the issue is resolved, trading picks back up no matter the outcome.

Even reduction of uncertainty as such can be important. Before important events like elections often money will ‘sit on the sideline’ until the outcome is known. This can even result in bad outcomes driving prices up. We may not like the new boss, but at least we now know who he is and can go about our business.

In my experience with prediction markets, important hidden information other traders could know acts as an outright veto on the market. It might not do that if the market had enough ‘natural’ trading volume, but that’s a high bar to clear.


Sources of Disagreement and Interest

Also known as, Suckers at the Table.

Any market, like a poker table, requires sources of disagreements and profits. Without a sucker at the table, why participate in the market? Remember, if you can’t spot the sucker in your first half hour at the table, then you are the sucker.

Ideally, you want either a direct subsidy to the market, or natural buyers and sellers.

If someone has a reason to trade even at a not so great price, for example airlines or countries hedging against moves in oil prices, then everyone can compete to make money off of that. The same would go if someone wanted to hedge against a political event, or to bet for or against their favorite team on principle – either to make it interesting, or to get what I used to half-jokingly call ‘compensation for our pain’ when the Mets inevitably lost again.

Another class of ‘natural’ traders are gamblers or noise traders, who demand liquidity for no particular reason. They too can be the sucker.

If people who want to learn fair probabilities subsidize the market, like donors subsidize the NGDP futures markets Scott Sumner helped create, that also works.

And of course there’s always the people who think they know something, and are sadly mistaken.

What traders need, more than anything else, is the ability to tell a story for why their trade is a good idea. To do that, they need to know why they have the opportunity to make this trade. What do they know that others don’t know? What mistake do they think people are making?

For sports, politics and economics, everyone has an opinion, so it is easy for them to get the idea that they have the advantage.

The genius of a binary bet on Ether prices that trades in Ether is that there are a lot of angles where one can think you know something the market doesn’t fully know, and lots of mistakes you can think other people are making. It’s easy to think of many different angles and approaches one could take. One can trade short term, or trade long term, do arbitrage or use it for leverage. Another could be doing it as a form of speech, or an experiment, and the group that can reach the market is doubtless quite biased.

It’s easy to make that leap to ‘I know why he’s willing to give me this trade’ and even to ‘I know exactly what mistake he is making.’ It’s a great choice for a big initial market.


Summary and Conclusion

Prediction markets rely on attracting a variety of participants, including both ‘losers’ who have natural reasons to participate, and ‘winners’ who will be attracted by that value.

Any critical issue can kill a prediction market dead, or even an entire prediction market ecosystem.

If your market isn’t well-defined, arguments over price become arguments over the rules, which turn into very angry participants. If this happens even a small percentage of the time, it drives everyone away.

If your market doesn’t resolve quickly, and quickly is on the order of days or at most a few weeks, it needs to be massively liquid and refer to real world questions people have natural exposures to, to create participation. It ties up cash and doesn’t offer the rush of a good gamble. No one wants to bet on an obscure outcome years from now.

If your market is unlikely to resolve, participants will find other uses for their time and money. The chance here has to be small, well under 50%, and much lower if time to resolution isn’t quick. Years-long markets that are unlikely to trigger are going to have severe issues.

If your market has potential hidden information, that is a tax on everyone who participates, who are prey to adverse selection. Everyone must worry that the market knows what they don’t know, and that them liking one side of a trade means the person on the other side has a secret; there are even traders in such markets that follow a strategy of ‘find the naively correct bet, and bet the other way,’ which is known (or should be known) as The Constanza.

If your market doesn’t draw natural interest and offer sources of disagreement, to create a foundation of participation and liquidity, or at least bribe the participants with explicit subsidies, there’s nothing to build on and no interest.

In addition to these threats, such markets face regulatory and legal hurdles, and face various ethical concerns. If you offer one market that seems to mimic a regulated trade, such as an option on a stock, or that sounds distasteful, such as the so-called ‘assassination markets,’ that can be all anyone will see when they look at your offerings. Even though such concerns, frankly, are mostly quite stupid, they’re real and people care about this a lot. They’ve gotten basically every past attempt at prediction markets (other than bookmakers and professional economic trading platforms) shut down.

Active curation is necessary to deal with many of these issues, and to provide simple ease of use and ease of finding what one is looking for and would be interested in.

Surgical use of prediction markets for key information points remains a great idea, and in many cases people love a good bet. But we shouldn’t get too ambitious, and keep an eye on the practical needs of participants.











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Who Wants The Job?

Over this last month, my wife and I searched for and hired a new nanny, as ours had decided to learn programming and move to The Bay.

We ended up with a wonderful woman we found through a personal recommendation on a local mailing list. Her previous employer posted saying, hire this woman, she’s fantastic, and this was indeed the case.

Before we realized we’d found her, various people encouraged us to post a job listing on a website, so we did so on Sittercity.

That went… less well.

We got over one hundred applications.

The majority of them had profiles that did not match the requirements of the job. About half were only available for a few months or for part time work. Many others wouldn’t work with multiple children, or had higher salary requirements than we listed.

The vast majority of them had major spelling and/or grammar errors in their profiles. Also in their messages to us.

From the remaining profiles, we reached out to a few dozen applicants.

The majority of them did not respond to us at all. 

Of those who did respond, several did not answer the phone for the initial interview and provided no explanation.

Of those who did answer, several were actively rude on the phone. 

Of those who were not rude on the phone, several did not engage with the questions being asked or show any interest in the job,

Of those who passed that screening and were asked to come for an in person interview, fully half of them failed to show up, most with no warning or cancellation. In all such cases, they didn’t contact us again. In other cases, they cancelled, but failed to make any real effort to reschedule.

As a result of all this, we only ended up doing two in person interviews. Because it turns out that getting people to show up, at all, is super hard. One of them seemed acceptable in a punch. The other we hired.

The vast majority of people who were on a job site, seeking a job, were not capable of tasks like: Write a profile page in English without major mistakes. Respond promptly when an employer contacts you to respond to your application. Talk politely on the phone and sound like you are listening and care about the job. Show up to your interview.

Yes. Standards are that low. 

You are much more employable than you think.

If you’re wondering why employers say it’s so hard to hire people despite getting a hundred applications for every position? That’s why.

If you’re wondering why many people can’t find work? I can’t help but wonder if it’s because they can’t do the very basic things even at high leverage points like the interview process. Things like showing up and responding to emails. Being on time. Being polite. Making sure the profile you show the world doesn’t have major errors and matches the jobs you’re applying to. Acting like you actually want the job.

Those are standards that 98% or so of applications we got failed to live up to. Presumably the same people, failing to live up to them, apply again and again, failing those standards again and again, wondering why they can’t get hired.



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Simplicio and Sophisticus

Previously (Slate Star Codex): The Whole City is Center

Epistemic Status:

Image result for two spiderman meme

Note that after writing a lot of this, I checked and Sniffnoy anticipated a lot of this in the comments, but I think both takes are necessary.

There are many useful points to Scott’s philosophical dialogue, The Whole City is Center, between Simplicio and Sophisticus. I want to point out an extra one I think is important.

Here’s a short summary of some key points of disagreement they have.

Simplicio claims that there are words people use to describe concepts, and we should use those words to describe those concepts, even if those words have unfortunate implicaitons. Say true things about the world. Larry is lazy.

Sophisticus says no, if those words have unfortunate implications we shouldn’t use them. And in many cases, where the unfortunate implications are inevitable because people have those implications about the concept being described, we shouldn’t use any word at all to describe the concept. Larry can be counted on not to do things. But we shouldn’t treat lazy as a thing, because people think being lazy is bad and there’s no utility in thinking Larry is bad.

Simplicio says we should use whatever techniques work, regardless of whether they are negative reinforcement, positive reinforcement, before the act, after the act, too big, too small, you name it, if that’s the system that works. And if people’s natural instincts are to do things that work best as a system, but are sometimes ‘overkill’ or have unfortunate side effects in a particular case, you should accept that.

Sophisticus says no. Studies show negative reinforcement reinforcement doesn’t work, so don’t do it. Studies show harsher prisons don’t deter people so don’t use them. You should only use exactly what is needed to cause a direct effect in each situation. Or, if you need to use deterrence, what the evidence says will actually deter people.

Sophisticus says, we should look upon motivations like ‘I want this person to suffer’ with horror, and assume something has gone horribly wrong. (He makes no comment on feeling ‘I want this particular person to be happy’, which doesn’t come up.)

Simplicio says, if having seemingly unreasonable desires in some situations, including potential future situations, is the way persons and groups get better results, stop looking at it as some crazy or horrible thing. People’s motivations are messy, they have lots of weird side effects like loving kittens (I would note, so much so that I am punished for not loving them, basically because not having bad side effects of a thing is evidence of not having the thing itself). Going all ‘these instincts seem superficially nice so we’re going to approve, and these instincts seem superficially not nice so we’re going to disapprove’ seems wrong.

Sophisticus says, that by refusing to use concepts like lazy, he has a value disagreement with Simplicio and those who do use the lazy concept. Because those people embrace the implications.

Simplicio says no, this isn’t about value disagreement.

But then, near the end, Sophisticus catches Simplicio by saying he’s refusing in context to use the term ‘value difference’ because he doesn’t like its implications, and insisting only upon some Platonic ideal version of value difference. Which, Sophisticus says, makes him a hypocrite! Rather than point out either that no, it doesn’t, or maybe it does and you get non-zero points for noticing but asking for people not to ever be a hypocrite is not a valid move, he instead gets so embarrassed he flees town, and only redeems himself ten years later by pointing out what happens if you reject the unfortunate implications of the term ‘city center.

The most important lesson is, as Sniffnoy observes, the characters have the wrong names. Simplicio should be Sophisticus. Sophisticus should be Simplicio.

(I will continue to refer to them by Scott’s names here.)

Sophisticus wants to solve the world by getting rid of all the things he doesn’t like, and all the things he can’t properly quantify. He only accepts actions that are based on fully described and measured reasons. He will accept second or third order causes and consequences, but only and exactly those with well-described and quantified causal pathways.

Then he says that such actions are intelligent, sophisticated and advanced. They reject the irrational, the non-scientific. So they denigrate people who think otherwise with labels like Simplicio, and pretend that word doesn’t have unfortunate implications. Because it’s never all right to label people, in ways that have unfortunate and false implications (e.g. that a person is simple or stupid) unless you catch someone labeling people.

Simplicio accepts that the world is complex, and that our systems for dealing with it are approximations and sets of rules and values that won’t always do the locally optimal thing, and that doesn’t mean they’re wrong. Simplicio is comfortable with the idea that correlations and associations exist even when we don’t like them.

Sophisticus is what Nassim Taleb calls the Intellectual, Yet Idiot (IYI). By doing things that are more abstract, and discarding most of the valid and useful information and relationships, they fool themselves and others into thinking that they are smarter and more sophisticated. Simplicio is advocating for Taleb’s typical grandmother, who has learned what actually works and survives, even if she doesn’t understand all the reasons or implications.

Sophisticus is vastly simplifying the world.

He simplifies the world by cutting out the parts he does not like, and the parts he does not understand.

This allows him to create a model of the world. That’s great! That’s super useful! I love me some models, and you can’t have models without throwing a lot of stuff out. Often the model gives much better answers despite this, and allows us to learn much and make better decisions.  What makes a model great is that when you get rid of all the fuzziness, you get rid of a lot of noise, and you can manipulate and do math to what is left. Over time, you can add more stuff back into the model, and make it more sophisticated.

When you start thinking in models, or like a rationalist, or an economist, either in general or about a particular thing, that kind of thinking starts out deeply, deeply stupid. You must count on your other ways of thinking to contain the damage and point out the mistakes, to avoid taking these stupid conclusions too seriously, rather than as additional perspectives, as points of departure and future development, and places to learn. It goes way beyond Knowing About Biases Can Hurt People.

Drop stuff from your model, and you fail to understand or optimize for those things. If you then optimize based on your model, the things you left out of the model will be left out, and sacrificed, because they’re using optimization pressure and atoms that can be used for something else. The results might or might not be an improvement. As the optimizations get more extreme, we should expect bigger disruptions and sacrifices of key excluded elements, so that had better be worth it.

One danger is that many people who develop the models either do so because they are really bad at navigating without models, or because they realized how bad everyone is at navigating without models. This provides motivation to work on the models even if they aren’t yet any good, but it also increases temptation to forget that the model is a map and not the territory.

I think this is related to how those who found a business are as a group completely delusional about their chances of success, but also that founding a business is a generally very good idea. Motivating the long term investment and endurance of high costs only works in such cases, even if many more people would be better off in the long run if they did it.

The struggle is, how does one combine these two approaches. Build up one’s models and toolboxes, to allow systematic thinking, while not losing the power of what you’re ignoring, and slowly incorporating that stuff into your systematic thinking. Otherwise, no matter how simplistic the average person might be, you risk being even more so.





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Why Destructive Value Capture?

Previously: Front Row Center

I got a lot of push-back from suggesting that there was a way for theaters to improve their customer experience and value proposition at low cost (get rid of the seats that are so close to the screen they cause neck strain), and that theaters should do that.

The push-back didn’t argue that the method wouldn’t improve the customer experience at low cost. There were a few who suggested an alternate high-cost solution that improves the experience more (use high-quality and assigned seating at a substantially higher price point), and which some places have implemented. No one argued that, where the higher-cost solution didn’t make sense. my incremental suggestion wouldn’t improve the customer experience versus status quo, at relatively low cost.

They also didn’t raise the reasonable argument that getting people to do things at all, especially slightly non-standard things that might look bad on superficial metrics during the pitch meeting, is hard. People don’t think about things, they don’t do things, they don’t optimize, and so on. One could reasonably argue this isn’t worth the effort.

Instead, everyone argued that, unless they were forced to do so, theaters shouldn’t implement the suggestion. Because it would cost them money – they couldn’t sell those few terrible seats, and forcing people to come early increases ad and concession revenue.

That’s interesting. And weird.

The proposition creates value. One comment from Quixote estimates $1.67 in customer time-value is saved in exchange for the loss of $0.10 in ad revenue.

The proposition improves the customer experience. It generates movie-going habits, loyalty and goodwill.

Not implementing the proposition is a destructive value capture. In order to get a little revenue, an order of magnitude more value is destroyed.

Destructive value capture is normal. In order to capture value, some value is typically destroyed. But when you’re destroying most of the value you withdraw from the system, you should be suspicious mistakes are being made. At a minimum, it’s worth asking on a deeper level why this is happening. What could justify it? What failure mode are we in? How does it come to be, why does it persist, is there a way we can solve it or minimize it? We shouldn’t shrug and mutter something about capitalism. We should treat this as a major failure, and brainstorm potential barriers even if they don’t apply in this case.

Can’t Raise the Price

If you’re charging $15 to see a movie, then destroying $1.50 in value to generate $0.15 in additional income, why aren’t you just not doing that, and instead charging $15.25 to see the movie?

What might stop this from being a good solution?

What if movie was free? Moving from free to not free is a huge change, even if the additional cost is small. This could drive people away and be hugely value destructive.

What if this introduced an additional collection point? You’d need to ask someone for money an additional time to make up the additional cost, and that could be value destructive.

What if this disrupted a standardized price or crosses a key threshold? Suppose everyone knows that movies cost $15, and there would be a strong reaction against a price of $15.05, because it’s different, or because it makes it hard to give exact change.

What if the market encouraged sorting purely by price? Imagine a world like with plane tickets, where you go to Kayak or Orbitz or what not, and there is strong default pressure to buy the cheapest tickets without noticing extra charges.

What if regulation prevented higher prices? That which is forbidden is not allowed. Price controls often cause perverse reactions.

Those would be good reasons. All clearly do not apply. Movies aren’t free (or if you have MoviePass, they would stay free). Movies have a collection point. Movies don’t have a standardized prices or a strong price-sorting search mechanism, and prices are rarely at a key threshold.

Other reasons might apply somewhat, but still seem weak.

What if this would be a price increase and that would be bad? Thus, the bad event of ‘prices went up’ could matter even if the new price isn’t much different from the old price, so you can’t do that often. A tiny increase might be impractical.

That’s fair. But the increase could be put into a later, larger increase, or if that’s too big a burden, one could wait on implementation until the next price increase.

What if higher prices decrease customer experience, so they’re more expensive than they look? 

I grant this is likely true for some, but the effect size should be small.

What if this is a pure bad when demand is low, such as at a matinee, and complexity cost prevents price discrimination? 

Again, this seems true but effect size is small. Some places price discriminate by time but the complexity cost stops the majority. So even though removing the seats costs nothing when demand is low, raising the price at those times is net bad.

Would a price increase send the wrong message? Would people then worry about the health of your company, or your industry? Would it thus push down stock prices or reduce your ability to raise money?

It might, indeed. It also might do the opposite. I don’t think this is what’s going on here.

All of that is seeking solutions to the easy out: raising prices. Or, if prices are already higher than they should be, lower them to where they should otherwise be, then raising them back.

Let’s take away that easy out, and say one of the good reasons applied. You can’t raise the price and demand exceeds supply.

This is pretty terrible even if you don’t then do value capture. Destructive value allocation is bad enough, via making people wait on lines or make commitments or virtue signal or what have you – anything where the auction involves incinerating rather than redistributing the bids, often all-pay auctions at that. One can think of this as balancing supply and demand by making quality of the supply sufficiently worse. 

Thus we have two mostly distinct problems. We need to pay for the creation and maintenance of nice things without destroying what makes them nice. And we need to do efficient allocation of those nice things, that balances supply and demand and gets the product to the right people.

Letting the price float is the best way to do both, but what happens when you can’t do it? Are we now stuck with terrible seating and massive deadweight loss? What about other situations where the price is stuck? A life lived under advertising’s increasingly long and intrusive shadow? Or worse, the evil bastard children of microtransactions and free to play games?

We seem to be headed that way. I think there are promising answers, which I hope to explore further. That starts with defaulting to price adjustment, and finding creative ways to do price adjustment, and viewing destruction of value as a failure rather than normality or ‘the way of business.’



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Front Row Center

Epistemic Status: Lightweight

Related: Choices are BadChoices Are Really Bad

Yesterday, my wife and I went out to see Ocean’s Eight (official review: as advertised). The first place we went was a massively overpriced theater (thanks MoviePass!) with assigned seating, but they were sold out (thanks MoviePass!) so we instead went to a different overpriced theater without assigned seating, and got tickets for a later show. We had some time, so we had a nice walk and came back for the show.

When we got back, there was nowhere for us to sit together outside of the first two rows. They’re too close, up where you have to strain your neck to see the screen. My wife took the last seat we could find a few rows behind that, and I got a seat in the second row. It was fine, but I’d have much preferred to sit together.

It was, of course, our fault for showing up on time rather than early to a sold out screening. I mention it because it’s a clean example of how offering less can provide more value.

The theater should, if they don’t want to do assigned seating, rip out the first two rows.

At first this seems crazy. Many people prefer sitting in the first two rows to being unable to attend the show, so the seats create value while increasing profits. What’s the harm?

The harm is introducing risk, and creating an expensive auction.

The risk is that if you go to the movies, especially the movies you most want to see, you’ll be stuck in the first two rows. So when you buy a ticket and go upstairs, you might get a bad experience. If the show is sold out, that might be better, as you can buy a different ticket or none at all.

The auction is worse. Seats are first come, first serve. So if it’s important to get served first, you need to come first. If it’s very important to not be last, to avoid awful seats, you need to come early, and so does everyone else, bidding up the price of not-last the same way you’d bid up being first.

With no awful seats, those who care a lot about better seats will still come early, but most people care a lot less. So everyone can come substantially earlier, and not feel pressure. Many will show at the last minute, and be totally fine.

The deadweight loss in time, of adding those forty extra seats, is massive, distributed throughout the theater. Everyone feels pressure to get there early even when they already have a ticket, so even if their seat is good, they stressed out about their seat, and not only burned time but feel bad about being pressured.

Avoiding time-based auctions and signals, or at least minimizing the value at stake in them, is an important and underappreciated problem.



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