Political prediction markets react (with a small delay) to political polls &#8212-just like the political experts and the mass media do, too. Hence, in order to discover their true social utility, the prediction markets (which are tools of intelligence) should not be compared to the polls (which are just facts) but to the similar meta intelligence mechanisms (the averaged probabilistic predictions from a large panel of experts, or the averaged probabilistic predictions from the political reporters in the mass media, or else). My bet is that, in complicated situations (such as the 2008 Democratic primary), the prediction markets beat the mass media (in terms of velocity) &#8212-even though the prediction markets are not omniscient and not completely objective (but who is?).

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You might remember the research article that I have blogged about:

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Learning in Investment Decisions: Evidence from Prediction Markets and Polls – (PDF file) – David S. Lee and Enrico Moretti – 2008-12-XX

In this paper, we explore how polls and prediction markets interact in the context of the 2008 U.S. Presidential election. We begin by presenting some evidence on the relative predictive power of polls and prediction markers. If almost all of the information that is relevant for predicting electoral outcomes is not captured in polling, then there is little reason to believe that prediction market prices should co-move with contemporaneous polling. If, at the other extreme, there is no useful information beyond what is already summarized by the current polls, then market prices should react to new polling information in a particular way. Using both a random walk and a simple autoregressive model, we find that the latter view appears more consistent with the data. Rather than anticipating significant changes in voter sentiment, the market price appears to be reacting to the release of the polling information.

We then outline and test a more formal model of investor learning. In the model, investors have a prior on the probability of victory of each candidate, and in each period they update this probability after receiving a noisy signal in the form of a poll. This Bayesian model indicates that the market price should be a function of the prior and each of the available signals, with weights reflecting their relative precision. It also indicates that more precise polls (i.e. polls with larger sample size) and earlier polls should have more effect on market prices, everything else constant. The empirical evidence is generally, although not completely, supportive of the predictions of the Bayesian model.

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You might also have watched Emile Servan-Schreiber&#8217-s videos. Emile is a smart man, and those videos are truly instructive.

1. In the first part (the lecture), our good doctor Emile Servan-Schreiber sold the usual log lines about the prediction markets &#8212-blah blah blah blah blah.
2. In the second part, Emile Servan-Schreiber took questions from the audience in the room. &#8220-Aren&#8217-t political prediction markets just following the polls?&#8221-, asked one guy. Emile&#8217-s answer was long and confused. However, in my view, Emile actually did answer that question (before it was ever asked) in his preceding lecture when, at one point, he made the point that the media were slower than the prediction markets to integrate all the facts about the 2008 Democratic primary, around May 2008. That is the right answer to give to a conference attendee who enquires about prediction markets &#8220-following&#8221- the polls. Both the mass media and the prediction markets do follow the polls (since the polls are facts that can&#8217-t be ignored), during political campaigns. Let&#8217-s compare the prediction markets with the mass media, instead, and let&#8217-s see who&#8217-s quicker to deliver the right intelligence..

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Lance Fortnow gives a good insight about the relationship between polls and prediction markets (see his last paragraph).

Yesterday the Electoral College delegates voted, 365 for Barack Obama and 173 for John McCain. How did the markets do?

To compare, here is my map the night before the election and the final results. The leaning category had Obama at 364. The markets leaned the wrong way for Missouri and Indiana, their 11 electoral votes canceling each other out. The extra vote for Obama came from a quirk in Nebraska that the Intrade markets didn&#8217-t cover: Nebraska splits their votes based on congressional delegations, one of which went to Obama.

Indiana and Missouri were the most likely Republican and Democratic states to switch sides according to the markets, which mean the markets did very well this year again. Had every state leaned the right way (again), one would wonder if the probabilities in each state had any meaning beyond being above or below 50%.

Many argue the markets just followed the predictions based on polls like Nate Silver&#8217-s fivethirtyeight.com. True to a point, Silver did amazingly well and the markets smartly trusted him. But the markets also did very well in 2004 without Silver. [Chris Masse’s remark: In 2004, Electoral-Vote.com (another poll aggregator) was all the rage.] One can aggregate polls and other information using hours upon hours of analysis or one can just trust the markets to get essentially equally good results with little effort.

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The polls are facts. Prediction markets are meta to facts. Prediction markets are intelligence tools. Let&#8217-s compare them with similar intelligence tools.

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Lance Fortnow&#8217-s post attracted an interesting comment from one of his readers:

to provide an exciting collection of political and other prediction markets.

These markets are as much a &#8220-prediction&#8221- tool as a wind vane or outdoor thermometer are. They moved up and down according to the daily trends, with very little insight of the longer place phenomena underlying them.

When the weather was hot (Palin&#8217-s nomination announcement) the market swinged widely towards McCain, while ignoring the cold front on the way here (the economic recession + Palin inexperience).

The value of weather forecast is in telling us things we didn&#8217-t know. We don&#8217-t need to trade securities to believe that if McCain is closing on the polls then his chances of wining are higher (duh!), which is what the markets did. We need sophisticated prediction mechanisms to tell us how the worsening economic conditions, the war in Iraq and Palin ineptitude (which in pre-Couric days wasn&#8217-t as well established) will impact this election, today poll&#8217-s be damned.

Looking at the actions by the republican teams, who were trying to read past the daily trend all the way to November 4th, it is clear that they thought all along they were losing by a fair margin. Because of this is they choose moderate, maverick McCain, went for the Palin hail mary fumble^H^H^H^H^H pass and the put-the-campaign-on-hold move.

A full two weeks before the election the McCain team concluded the election was unwinnable, while the electoral college market was still giving 25-35% odds to McCain.

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As highlighted in bold, the commenter says two things:

1. The prediction markets are just following the polls.
2. The prediction markets have a minimal societal value.

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My replies to his/her points:

1. That&#8217-s not the whole truth. The polls are just a set of facts, whereas the prediction markets are intelligence tools that aggregate both facts and expertise. The commenter picks up a simple situation (the 2008 US presidential election) where, indeed, anybody reading the latest polls (highly favorable to Barack Obama) could figure out by himself/herself what the outcome would be (provided the polls wouldn&#8217-t screw it).
2. That&#8217-s true in simple situations, but that&#8217-s wrong in complicated situations (such as the 2008 Democratic primary).

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The emergence of the social utility of the prediction markets will come more clearly to people once we:

1. Highlight the complicated situations-
2. Code the mass media&#8217-s analysis of those complicated situations, and compare that with the prediction markets.

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APPENDIX:

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