It’s the summer, so I’m an intern again. Like last year, I’m working as an engineer at a large industrial plant. At my current job and jobs before it, I have heard people mention that people view environmental concerns differently than other concerns, and that this comes from the fact that environmental compliance is usually handled by a specialized group.

With direct environmental regulations, compliance usually has to be handled by specialized group that understands the relevant regulations. For example, during the school year, when I had a large design project class, we had a specialized group that handled the environmental impacts, they had to know the regulations which the rest of us knew little about.

In industry, there seems to be a tendency for people outside of the environmetnal group tend to view the environmental group as meddling with their activities. This probably makes compliance with environmental regulations more difficult than it could be. However, as environmental policy moves towards market based solutions, industrial plant engineers and operators will take over those areas that use markets and incorporate those prices into their decision making; instead of saying “the environmental department is messing with my design!”, they will say “oh, the cost of water is really high right now.” This will reduce wasted efforts, and help the environment more cheaply.

I have been waiting hopefully for Diggs recommendation engine for some time now. Now it’s here (in beta):

The Recommendation Engine uses your past digging activity to identify what we call Diggers Like You (who you can see on the right hand nav) to suggest stories you might like.

Awesome.

I submitted a comment to the CFTC about their “Concept Release on the Appropriate Regulatory Treatment of Event Contracts.” Specifically, I addressed the American Enterprise Institute’s call for a ban on for-profit prediction market exchanges as well as restricting fees charged by such exchanges to “modest” ones (link). This is what I said:

Recently, the American Enterprise Institute and others have asked the Commodities and Futures Trading Commission to prohibit for-profit prediction market exchanges, and only allow prediction markets to charge “modest fees”. I will make the case here that both for-profit exchanges and more than “modest” may both be important for getting the most benefits from prediction markets.

One of the major benefits of prediction is that people and companies can use prediction markets’ relatively accurate and well-calibrated predictions to improve their planning. Market predictions reduce the calculation work that people and companies have to do in order to come up with predictions because they can outsource the work to prediction markets.

It would be a mistake to unnecessarily limit the areas which prediction markets are used to predict, because it is difficult to predict what areas may help people and companies improve their planning. For profit exchanges will have incentives to find as many places where such more accurate and better calibrated predictions are useful, especially in industry. Thus it would be a mistake to prohibit either for-profit exchanges or limit the fees that exchanges can charge.

Consider the following scenario:
A number of companies in some industry are interested in the information about the future price of certain products, the future of industry relevant technologies or in future demand for certain products or any number of things of that might be predicted using prediction markets.

In response to this interest, a for-profit company creates a prediction market exchange for contracts about the information that those companies are interested in, and then sells access to the exchange to companies in relevant industries. The exchange company uses the revenue generated from exchange subscriptions to subsidize contracts in order to generate more accurate predictions. Employees from the companies who subscribe to the exchanges would be the market participants.

Such exchanges would be even more attractive to companies than internal prediction market exchanges because contract subsidies and the pooling of market participants in multiple companies into one market would improve the usefulness of prices significantly.

Allowing a for profit company to create such exchanges means that it will have strong incentives to make its exchange contracts the more useful to its subscribers, whereas non-profit companies will have weaker incentives to do so. Now, perhaps someone would step up and create a non-profit exchange to fill this role, but perhaps none would. This is especially likely in markets where there is little camaraderie and collusion. Non-profit exchanges will probably also get created and develop slower than for-profit exchanges. This would very bad in cases where subscriber needs change frequently, because non-profits would have trouble keeping up.

Limiting the fees that exchanges can charge is also a bad idea, because the amount by which the exchange company would need to subsidize a contract in order to achieve the desired accuracy could be large in some cases. When developing models, and collecting an analyzing data is costly, large subsidies would be needed to get people to make accurate predictions.

I do not doubt that non-profit prediction market exchanges are likely to be very valuable, especially in public policy arenas, but it would be a serious mistake limit prediction market exchanges to non-profits.

I want to argue that prediction market contracts about future prices may be quite useful for planners, even in markets where a robust futures market already exists (though not where robust options markets exist).

Prediction market contracts about prices differ from futures and options mostly in their purpose. The purpose of prediction market contracts about prices is to reveal information whereas the purpose of futures and options markets to allow arbitrage and hedging. Because the purpose of prediction market contracts is to reveal information, such markets will often exist in areas where futures and options markets do not exist, and they will frequently be subsidized.

The most general type of prediction market about prices is a price probability distribution market, which actually consists of number of different contracts. Each contract predicts the probability that the price of a certain good will fall in a certain range on a certain date. The rangers are continuous, so the prices of all the markets can be interpreted as a probability distribution for the price (as shown in the graph).

In markets where futures markets do not exist, such probability distributions are useful to planners because they capture all public knowledge and some private knowledge about future prices. Additionally, unlike expert predictions, market probabilities are generally well calibrated (events which are predicted to happen with a probability of p=.50 happen 50% of the time, and so on). This makes them much easier to rely upon than expert predictions which tend to be overconfident or underconfident. Markets also make it much easier to share information between people, even competitors.

Why might a future price distribution be useful even when there is already a futures market? The general answer is that many agents do not have utilities that are linear with the price of some goods. The futures price of a commodity should be it’s expectation (mean) price, but this not very useful for buyers who have elastic demand curves or suppliers who have elastic supply curves.

For example, consider a company considering investing in wind power in the future. They are considering buying marginal land for wind turbines which would be profitable in energy prices rise beyond a certain price. How much they should be willing to pay for such land depends on the probability that energy prices rise above that cutoff price and the expectation price given that it is above the cutoff. Thus, the land may be quite valuable, even when futures price of energy is below the cutoff but there is much uncertainty about the future price. In this case, a probability distribution over future prices is helpful because these both the probability and the expectation are easily determined from a price probability distribution (actually, I think most energy markets have active options prices from which you can get this type of information right now, so this may not be a great example).

Another example is a private bus company figuring out how big their buses should be in a city with congestion pricing. If the city’s congestion charges are very large in the future, the the company will compensate by using larger buses. Since the company’s profit is not constant with the price (the company can mitigate the impact of the price to some extent) it is useful for the company to know the probability distribution of future congestion charges.

I should have more to say about prediction market contracts about prices in the future.

Bryan Caplan does not like the Austrian concept of “radical uncertainty”. After thinking about this for a bit (I’ve been interested in uncertainty and judgment for a few months; my reading list so far is here), I have come to a view similar to Arnold Kling’s. I do think the concept of “radical uncertainty” (at least as I understand it; I have not read any Austrians on this topic) is useful, but I also think that the subjective (Bayesian) probability perspective is useful.

As I understand it, “radical uncertainty” is closely related to the idea of “unknown unknown.” An Unknown Unknown is an event that occurs but was totally outside of a person’s event space of possible outcomes. For example, if you make the decision to steal to 2nd base in a softball game and fall into a sinkhole under second base, falling into a sinkhole was an unknown unknown event. Falling into a sinkhole in that circumstance is an event which you would probably not have considered even if you had given the decision a lot of thought; it was totally out of your event space. Radical uncertainty is the uncertainty that comes from having nonexhaustive event space.

Part of the planning process is comming up with possible events. I don’t really know how this works, but our brains certainly don’t come up with exhaustive event spaces in most circumstances, and in many cases we seem to miss important potential events. Our brains seem to use useful but flawed heuristics to generate event spaces, much the same way that our brains generate subjective probabilities.

Events outside of a person’s subjective event space are not given a probability of zero, they are simply outside of the event space. If an event was previously outside of a good Bayesian’s subjective event space, and something else suggests the possibility of the event to them, the they would then give the event a nonzero probability. From a Bayesian perspective, it doesn’t make sense to say that the person gives the event a probability of zero because all probabilities are subjective, and a person cannot give a probability to an event that has not even crossed their minds.

Even a good Bayesian, who uses evidence to update all their subjective probabilities and who is well calibrated, can still be radically uncertain for planning purposes, because they can’t always generate a relatively complete subjective event space. They can even still be radically uncertain, even if they know they are radically uncertain. When you are radically uncertain, you have to make a judgment about how complete your event space is and what the expected utility is of events outside your event space, this is often very difficult, which explains why radical uncertainty poses such a challenge. However, recognizing you are radically uncertain is the first step towards generating a more complete event space, therebye turning radical uncertainty into normal uncertainty.

My brain has a limited capacity to store knowledge. In the past, I frequently needed to think about a particular topic and remembered finding valuable resources for it, but didn’t remember enough to find them again. The internet alleviate this problem. It is easy to write stuff down on the internet, and then find it again later.

Right now I use Google Notebook. When I find information on a topic I am or might be interested in, I write it down or link to it and describe it. If I have a preexisting note I add the information there; if I don’t, I start a new one. That way, when I decide to revisit the topic, I can search and find sources that I have already come across. When I find a paper that I might want to read later, I write a note to myself, so that I can find it later if I decide to read it.

Here’s a typical note, from my “Investment” notebook:

Online banking, higher rates than capital one
offering 4.1% when capital one was at 2.96%
offshore bank maybe a little sketchy

also high CD rates for large sums 100k - 7.5%
not FDIC insured key
http://www.mlnbank.com/EN/services/saccounts.htm

some skepticism about it
http://bankdeals.blogspot.com/2005/10/research-into-millennium-bank-and-775.htm

I found some interesting information online on banking, that I thought might be useful in the future, so I wrote it down for the next time I have to make a related decision. I wish I could do this with my school-real life note taking and store it on a computer where it is searchable.

I also occasionally look though my notebooks without a specific goal in mind. I do this for a couple reasons; 1) I can correct misspellings and errors, 2) sometimes it inspires me to look to go and read something or spend time thinking about an idea I had, and 3) leafing through it helps me remember what resources I actually have in there, which is important if I don’t want to start from scratch next time

I suspect that I will stop using Google Notebook soon because Google has seemingly stopped updating the application. Google has removed the application from their menu bar in Gmail. The search feature is also somewhat limited; for example, if I search for ‘auction’ it doesn’t do the regular Google things like also searching searching for ‘auctions’.

I don’t know what software I should use. I do know that Zoho has a Notebook (though it doesn’t seem to have a search feature!), and Second Brain seems interesting (though it doesn’t seem focused around note taking), but I haven’t played around with any software for an extended period.

Do other people do this sort of thing too?

There are many ideas floating around for reforming existing institutions so that they produce significantly better outcomes. The three ideas for reforming governing institutions that I know of are Predictocracy, Futarchy, and Professional Voting; I am sure there are more, and I know there are a lot more for reforming other institutions. However, there is a noticeable lack of experimentation with type of idea. Does it make sense not to experiment? Let’s do a few back-of-the-envelope net present value calculations to see if it does or not.

Let’s say that we judge Futarchy to have a 1% chance of working. Let’s also assume that if we experimented with it now it would take 30 years for the benefits to appear, that it would save everyone in the country $100/year (a conservative guesstimate, remember this is contingent on Futarchy “working”), and that the total investment would be about $100 million (surely a massive over guesstimation). Using a discount rate of 10%/year, the Net Present Value of a $100/year stream appearing after 30 years is $57 (I did the calculations). There are 300 million people in the country so the total Net Present Value is ( 1% x $57 x 300 million ) - $100 million = $70 million. So even under these calculations, the expected benefit of researching Futarchy is massively positive. Not experimenting is huge loss.

The thing that makes this investment so good is the fact that there are a lot of people in the country. Finding an institutional reform that makes peoples lives better provides a massive public good, but costs a fixed amount to find. In these calculations I have only included the people in the country, the expected benefit is an of magnitude larger if we consider the benefits to everyone in the whole world.

I thought that Cass Sunstein’s EconTalk podcast on worse case scenarios had an interesting discussion of discount rates (this section starts at about minute 55 and it is about 9 minutes long). He argue’s that there’s a difference between discounting lives and discounting money because money has opportunity cost; you can invest money and make it grow, whereas you cannot do the same thing with lives. Eliezer Yudkowsky has argued similarly that we should not have a discount rate in our pure preferences (Yudkowsky makes this distinction clearer than Sunstein). However, Robin Hanson dissented.

I tend to side side against discount rates in our pure preferences. Even if people act like they have discount rates in their pure preferences (note the lack of charities trying to help the future), as Hanson points out, perhaps they would if they understood how easy it is to help the future.

I have noticed something about myself in the last year or so, I fear being proven wrong. When I see some study on a topic about which I have a prior opinion, I get a light dose of fear. To give a more specific example, after I finish writing a post for this blog, I consider reading it over in order to revise it. My immediate reaction to that suggestion is to fear that my belief that I have written an eloquent post is wrong. Ironically actually discovering errors does not cause me much pain.

This seems like a clear case of confirmation bias, and being biased is bad. Bias leads to costly mistakes, like writing bad posts and making unwise investments. Luckily, I have come to recognize this feeling of fear relatively easily, and recognizing it helps me to identify when I might be prone to confirmation bias and to lookout for possible disconfirming evidence. This feeling of fear is my own special marker for confirmation bias.

When I recognize the feeling, I know that I should think carefully about my choices about what evidence to seek. I make sure not to avoid looking at evidence simply because it might force me to change my beliefs, and I often try to actively seek possible disconfirming evidence.

I am sure I don’t always notice my fear, and overcoming even light fear can be difficult, but I am glad that confirmation bias has a relatively clear marker. I don’t know if other people get this same fear emotion, but I wouldn’t be surprised if it is a general phenomena.

The Commodity Futures Trading Commission is calling for public comments on possible regulation of “Event Contracts.” As I understand it, this is good news for prediction markets; there is a lot of uncertainty about the regulatory status of prediction markets, and this seems like it would clear that up. A clear regulatory framework would pave the way for real-money prediction markets. At least, as long as event contracts are not regulated out of existence. It is my impression that this is a very good thing, so I am very excited.

I will be submitting comments via e-mail (secretary@cftc.gov), and I encourage others to do the same.

The gist of my comments is going to be that I see the potential for prediction markets to help regular people, who don’t have access to a lot of data and analysis, make decisions. For example, prediction markets about the future of renewable resource technologies could help engineering students determine whether they should invest resources in learning about those technologies. Prediction markets about the economic future of specific geographic areas could help young workers decide  where to move to get good jobs. Therefore, I would like to see the prediction markets relatively unlimited.

via Midas Oracle