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Scott Sumner argues that Nominal GDP expectations are critical to macroeconomic health, and I find his arguments pretty convincing. Low NGDP expectations (relative to trend) imply that the money supply is low relative to the demand for money. Unfortunately it is non-trivial to estimate NGDP expectations because there are no NGDP futures markets. The absence of a good measure of NGDP expectations makes assessing the performance of the central bank more difficult.

Fortunately, as I recently learned, InTrade will list basically any contract you like on their exchange for a fee of $100. Since this is pretty cheap, I will create a set of NGDP prediction markets for several future dates. Hopefully these markets will be active and produce meaningful estimates of NGDP expectations. If these markets are active, I think it should be easy to convince InTrade to continue to create new contracts as old ones expire.

I think it’s important to design these contracts well so their prices are both accurate and useful. The InTrade Real GDP markets (under Financial->US Eco Numbers) are a good anti-example; the various Real GDP markets are either not very informative or not very useful. The contracts tied to the Advance Real GDP figures are totally inactive; all of the markets I saw had 0 trades. The markets that rely on Final Figures are not terribly illiquid, but since they are binary markets that indicate only the probability that Real GDP will be positive/negative they are not terribly useful.

I think it is clear that a single market with a continuous payout is preferable to a set of binary markets as a single market will be much simpler for traders to trade in.

As I see it, the critical contract design questions are:

  1. How far apart should the contracts be spaced? 3 months, 6 months, 12 months?
  2. Should the contract payout be proportional the level of NGDP or the rate of change of NGDP in some period?
  3. How far out should the contracts start? What are the most important expectations? Expectations about NGDP 6 months out? 12? 24?
  4. What BEA data should the contracts be based on? Advance, Primary, Secondary or Final NGDP estimates?

Finally, there is the more difficult question of how to generate interest in the markets if regular InTrade traders are not inherently interested in the contracts. The most direct method that I know of is to subsidize trading by employing some type of automated market maker that is willing to lose some money. Peter McCluskey used this method to subsidize conditional prediction markets during the last election (see here). Unfortunately, he doesn’t seem to think his experiment was very successful. However since the last election there has been some research done on improved automated market makers.

I think it might be a good idea to attach a discussion forum to every prediction market contract. I think it might allow traders to make money off of private information faster and with less risk than they can now and might increase the amount of information generated by some markets.

If each prediction market had a dedicated forum attached to it, market participants who have information not currently incorporated into the contract price could make fast and relatively low risk profits by first buying all the contracts they want, revealing their information, and then selling their shares at a profit after the contract price adjusts. This would allow people who bring new information to cash in on their information quickly and with minimal risk, and would have the added benefit of making such information or analysis public.

Attaching forums would be very easy; a link on the contract information page would be sufficient. Moderation would not be problem because traders would have to have accounts with the prediction market exchange, so real punishments could be given to those who post fraudulently.

Discussion forums would also give people who are interested in the meaning of the contract price to ask questions about the logic behind it, though I don’t know if traders would answer.

I think it is odd that Google put $20 million behind the Google Lunar X Prize, but it does not appear to have subsidized a prediction market about the outcome, even though Google is a notable user of prediction markets. The intrade contract is already reasonably liquid (10% spread), but it would be interesting to see a more liquid market. A more liquid market would give people who think about the future of space exploration an easy way to see how hard or easy it is to go back to the moon (right now the probability is 20%-30%).

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 (, 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

One of the most interesting concepts I learned from Michael Abramowicz’s Predictocracy (which I have mentioned before) is the idea of a “normative prediction market”.

The essential distinction between normal conditional prediction markets (about prediction markets in general) and normative prediction markets is that for a normal prediction market, the contract payout depends on some objective criteria, like whether Barrack Obama wins the Democratic nomination or what the unemployment rate is in 2013, but for a normative prediction market, the contract payouts depend on the the subjective judgment of a person or organization. The subjective judgment could be that acquiring that small startup was a bad idea for Google or that introducing needle exchange programs was a good idea.

The major benefit of using subjective criteria to decide prediction market contract payouts is that the market predictions are more useful because they more precisely target the questions of interest. Normative prediction markets are less biased than normal prediction markets because they do not omit important but hard to quantify effects (which seem commonplace). Normative prediction markets can also do a good job of  aggregating preferences in some cases.

Uses of normative prediction markets

Normative prediction markets can potential help companies make good decisions. A manufacturing company might use a normative prediction market to predict whether some major internal change, like moving one of their manufacturing divisions overseas, would be regarded as a good idea by a judging committee in the future. The company would commit to setting up a committee in the future which would report on whether the change was a good idea. The committee would have a relatively open ended criteria but would likely consist mostly of cost benefit analysis, and prediction market participants would have to predict what factors the committee would think were important.

Normative prediction markets also have a lot of potential in public policy. For example, an executive agency or an interested non-profit could sponsor a normative prediction market to help evaluate whether it would be a good idea to implement some new type of poverty relief program. Prediction market contracts would pay out based on whether a decision judge, a randomly selected agency employee or non-profit board member, would announce that it was a good idea or announce that it was a bad idea to implement the program.

Payout rules

Different rules can be used to select the body responsible for deciding the variables that determine prediction market contract payouts. One method is to determine the payout based on a judgment made by an individual randomly selected from a defined pool. This method would be relatively cheap and the resulting market prediction should be quite stable because prediction market participants must average the expected judgments of the pool of decision makers. This averaging can be useful if one is interested in making decisions partially based on diverse preferences, not just diverse analysis.

Another method is to base the payout on a judgment made by a committee. Committees would generally give payouts with less variance than randomly selected individuals, which would make participating in the market more attractive because it would lower the risk involved. In some cases committees might also produce higher quality judgments; for example, when back and forth argumentation is important for producing good judgments. Of course, committees would also be more expensive since they involve more people and have other potential problems.

Potential problems with normative prediction markets

Shirking by decision judges is an important potential problem for normative prediction markets, because decision judges do not directly influence the final decision (since it has already been made). Thus, they will have lower incentives to make informed and well thought through decisions than traditional decision makers. This would reduce the quality of the predictions made by prediction markets.

Norms about not shirking and monitoring of decision judges could reduce shirking. However, decision judges should in general be harder than monitoring traditional decision makers because market judges do not directly change real outcomes, which eliminates one method of assessing decision quality.

One normative prediction market feature that could be either harmful or beneficial is that decision judges are likely to give much higher weight to higher order principles and ideology than traditional decision makers. Because decision judges who determine the contract payouts do not determine the decision that was actually made, if they care about other prediction market outcomes in the future, they have an incentive influence how market participants perceive the average decision judgment in order to shift that average in the direction they favor. This may include giving more more extreme judgments than they really prefer.

Consider a situation where the government uses a normative prediction market to decide how much it should spend on a new social programs. The prediction market decision judges are randomly drawn from body of liberals and conservatives. For a particular decision, if a liberal decision judge (a judge who’s spending judgments are generally higher than the mean of the group’s judgments) is selected, then they have an incentive to try to move the group’s mean judgment upward as much as they can by announcing a very high spending amount, even if that amount is higher than the amount they would choose if they were the actual decision maker. The reverse would be true when conservative decision judges are selected.

Strong norms pushing people towards giving genuine preferences would mitigate this effect somewhat.

The beneficial aspect potential of this effect is that decisions judges would generally be less tempted than traditional decision makers to trade off reduced rule of law or other high order principles for better immediate outcomes.

Abramowicz emphasizes this second potential, but on net, I think this effect would be more harmful than beneficial.

Over all, I think normative prediction markets are a very cool idea. They definitely deserve experimentation, and I suspect that they will eventually be used to make many types of decisions.

Links: Abramowiczs description of normative prediction markets

I am reading Predictocracy: Market Mechanisms for Public and Private Decision by Michael Abramowicz, about how prediction markets can be used for decision making in various places. Since I talk about governance mechanisms a lot on this blog, I intend to review the book later on.

In the mean time, here is a series of posts that Abramowicz did for The Volokh Conspiracy on prediction markets which I found pretty interesting. It includes some good-back-and-forth with Robin Hanson about Futarchy vs. Predictocracy (which I have discussed before).

Intrade is apparently starting to include contracts on the future of aggregate economic measures (e.g. GDP growth rate or unemployment rate changes) conditional on political outcomes (e.g. Hillary gets elected) at the suggestion of a research initiative of the Westminster Business School; such conditional prediction market contracts are central to Futarchy. Now, perhaps, we will be able to see if prediction markets can make good predictions about these topics, though they may be too thin to reveal much information.

Looking at the contract prices and volumes on Intrade (Markets->Politics->Impact of Next Pres.) right now, the markets look pretty thin, but perhaps eventually they will have significant impacts on elections.

A month or two ago, I was talking to Chris, and he brought up the idea of a government based on prediction markets. At the time, I dismissed the idea as unworkable, but now I have come across Robin Hanson’s more in depth development of the idea, which he calls ‘Futarchy,’ so I have given the idea some more thought. Hanson summarizes his idea,

In futarchy, democracy would continue to say what we want, but betting markets would now say how to get it. That is, elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare. The basic rule of government would be:

When a betting market clearly estimates that a proposed policy would increase expected national welfare, that proposal becomes law.

Futarchy is intended to be ideologically neutral; it could result in anything from an extreme socialism to an extreme minarchy, depending on what voters say they want, and on what speculators think would get it for them.

Subsidized betting markets would trade idea futures to determine what the results of certain policies will be. I envision the political body used to define the overall measure of welfare as a lot like the Electoral Council I have discussed for Professional Voting, a proportionally elected body which relies on logrolling to make create efficient outcomes. Proportional elections are important in both Futarchy and Professional Voting because the values and perspective of the median voter are unlikely to be the same values and perspective generated by logrolling between parties representing all voters.

I have not finished reading and digesting Hanson’s in-depth paper on Futarchy, but I wanted to offer my initial thoughts. While, I should first say that I am probably biased against this idea because, the way Hanson proposes it, it would be a substitute for my own idea for making democracy produce better outcomes, Professional Voting.

Here are my initial concerns with Futarchy:

  1. Some goals are procedural, and therefore not measurable, i.e. the Rule of Law.
  2. I suspect that it would be very difficult to create an overall measure of welfare that includes basically everything that people care about, no doubt there could be good numerical measures of a lot of what people care about but not everything or close to everything that people care about.
  3. Even though my own values are utilitarian, I strongly doubt that most peoples values are; I suspect that most people’s values are based on ‘fairness’ and other process based concepts, and policies which try to maximize an overall measure of welfare (which would presumably be a state function) would not be very good at furthering those values.

Ideas futures seem most promising to me as an aid to Professional Voting; good policy-futures markets would provide a lot of valuable information to professional voters and elected officials responsible to them about which policies are most likely to further specific goals. Such information could improve the decision making process tremendously, even if the decision of which policies to implement and how were left up to the discretion of the professionally elected legislative body. But perhaps this is just my bias for favoring my own idea.

Still, I would like to see both Futarchy and Professional Voting tried in experimental and small scale governance settings. I think Futarchy, in particular, would work well for making corporate governance decisions because there is essentially one dimension (stock price) on which to evaluate the quality of decisions. I think Futarchy could largely solve principal-agent problems corporate governance experience due to rationally ignorant stock holders. Internal corporate ideas futures could ask, for example, whether replacing the current CEO would boost the stock price, which would give the CEO a very strong incentive to be a good agent for stock holders.

Such experiments would let us evaluate whether and under what circumstances Professional Voting and Futarchy generate better outcomes that more traditional democracy.