Robin Hanson and others have suggested that prediction markets be used to help shape the direction of public policy. The current hot issue is how to combat global warming and its effects on the environment.
Matt Yglesias has argued that big money can manipulate markets. So, we should not use prediction markets for this purpose. On paper, prediction markets provide monetary or ego-related rewards for truthfully revealing private information by trading. In this sense, prediction markets are said to “incentivize accuracy”. When the incentives for manipulating the market price are greater than the incentives for not doing so, it is obvious how traders will act. Matt argues that prediction markets that are prone to manipulation, such as climate change futures, will make inaccurate predictions, and any policy that is based on these will be inappropriate. I agree.
Robin Hanson, on the other hand, believes that big money manipulators can only improve the accuracy of prediction markets. He goes so far as to say that prediction markets are “especially incorruptible”. I need to read all of his papers on this subject in their entirety, however, based on his own summary of the findings, I will make a few comments, now. [I promise I will read them, fully, and update this post if necessary]
Robin (and others) argue that prediction market accuracy improves “as more big money powers are known to want to manipulate them.” Manipulators are in essence noise traders. Markets with more noise traders are more accurate, because informed traders are attracted to the possibility of profiting by trading with the noise traders.
He qualifies his conclusion by stating that “this isn’t an absolute guarantee.” Then, he suggests that we try it before we condemn it. However, before we do so, I suggest we look at the theory more closely. We may find that it works as well as the neoclassical economic framework in economics. It works fine in a hypothetical, assumption-simplified world, but fails miserably in practice.
Let’s look at some of the simplifying assumptions in Robin Hanson’s application of prediction market theory. One, the informed traders are more powerful than the manipulators, or noise traders. In Hanson’s experiments, the manipulators are able to affect the market price, but the informed traders quickly bring prices back to an accurate level.
What if informed traders aren’t wealthier (than the manipulators)?
In a typical prediction market, greater trader wealth is accumulated by being better informed than other traders and making trades that payoff more frequently. By virtue of their greater wealth, informed traders have more power to influence the market than uninformed traders. This is a necessary condition to mitigate against manipulative behaviour.
In a public, real money market, trader wealth may have nothing at all to do with knowledge about that, or any other, outcome. Manipulative traders can simply bring wealth to the market. Furthermore, if such wealth is known to other traders, it may send a false signal to all traders about the manipulator’s “expert” status. That is, rather than being viewed as a manipulator, the trader may be seen as an expert. This is especially likely in markets where it is difficult (or impossible) for any individual to have enough knowledge to make an “informed” trade. Even if you place restrictions on wealth that may be traded, so as to prevent a small group of traders from manipulating the market, if the stakes are high enough, the big money manipulator will simply finance a large number of other traders to carry out the manipulation. I think many big money players would find the incentives large enough in the global warming debate.
What if most (or all) of the traders are uninformed?
I would argue that as long as the collective information set is sufficiently complete, the market could obtain a reasonably accurate prediction. If this is not the case, we will likely see a very flat distribution of predictions, reflecting the high degree of uncertainty. Such a result would be practically useless for policy decision-making, other than to indicate that we need much more information about the subject. Unfortunately, for an extremely complex issue, like climate change, it is highly unlikely that the market participants will have a “complete” set of information. It is doubtful whether any of the participants would be able to properly weigh and assess all of the information, in order to make a truly accurate prediction of any climate change metric. There are simply no, known frameworks for making such assessments, which leads us to…
Another possibility is that if traders have very little personal information about the subject, they will instinctively look to the others (the market) for guidance. The prediction market principle of independence begins to break down. If the market price has been manipulated, there is a good chance that the non-manipulative traders (notice I didn’t say “informed”) may “read” information in the price that isn’t true and place their trades accordingly.
Public vs. Enterprise Prediction Market Manipulation
One of the reasons I haven’t looked into the issue of market manipulation is that it isn’t much of a problem in enterprise prediction markets. Generally, we expect EPMs to have a sufficient number of informed traders, who tend to be “wealthier” than manipulators. There are some noise traders, but not too many. I agree with Robin Hanson’s assessment that manipulation will be overcome in enterprise markets. Consequently, I’ve had little interest in looking at this issue.
However, prediction markets on public policy issues are different. Apart from the market participants, there are many groups that have vested interests in the implications that might flow from a public policy prediction market outcome, and they will seek to influence the market prediction, by trading or by other means. For example, big business may try to influence the information available to all traders to achieve the desired prediction. This may take the form of advertising, public announcements, privately funded research, and all forms of lobbying activity. Governments issue their own propaganda. This information may be corroborated with price changes in the prediction market, lending credibility to inaccurate information. Unless these prediction markets can be insulated from the manipulative influence of non-trading interest groups, they will not be able to prevent or eliminate manipulation of the market predictions.
How Manipulation is Nullified (or not)
Robin Hanson states that the informed traders must know that the noise traders want to manipulate the market. In order to profit from this knowledge, they also need to know which way they wish to manipulate the market price.
In a global warming market, big business, carbon emitters would likely exert downward pressure on any metric that shows adverse effects from their activities, so that legislators would be less likely to impose costly laws to prevent such activities or to compensate others for the effects. On the other hand, “tree-hugging” organizations may wish to increase the market price, so that such legislation is more likely to be enacted. In both cases, the truly informed trader must know who the trader is and the trader’s motive for trading. Since there is no way to prevent a trader from diguising his identity, it is impossible to properly match the motive with the trader. It also begs the following question.
How might the informed trader distinguish between a manipulative trader and a misinformed honest trader? I don’t have that answer, but unless it can be answered, it may be impossible to ensure that attempts at manipulation will lead to more accurate predictions, at least in complex, public policy prediction markets.
In theory, it is a nice idea to try and accurately aggregate as much information as possible in order to determine the best course of action in public policy decisions. Most public policy decisions are remarkably complex with numerous tradeoffs among competing interests. All decision-making benefits from more information that is more accurate and more timely. Unfortunately, simply inserting a prediction market framework into the decision-making process does not eliminate the political biases that have been, and will always be, there.
While it may be possible to operate public policy prediction markets for some issues, their use in the climate change or global warming debate is questionable. Not only can there be no guarantee of manipulation-free markets, we wouldn’t even know if market predictions had been manipulated. If actual public policy were to depend on false readings from such markets, the potential for significant misallocation of resources is immense. It is simply too great a risk to consider at this time, in my opinion.