Michigan, U.S.A. —- Tuesday, January 15, 2008
—-
The Democrats
The Hillary Clinton event derivative was expired to 100.
—-
The Republicans
The Mitt Romney event derivative was expired to 100.
Source: InTrade
No, no, no…- —-not that:
That, rather:
NewsFutures – NewsFutures blog
Read the previous blog posts by Chris. F. Masse:
Tom Davenport:
- Confidence that executives have valuable roles to play even if they don’t always have the right answer-
- A high level of trust in the intelligence and capabilities of employees-
- The willingness to follow numbers and analysis wherever they lead (as long as they are more-or-less consistent with common sense)-
- A pretty strong analytical and financial orientation (since futures markets aren’t something that every Joe or Jane understands).
Read the previous blog posts by Chris. F. Masse:
David Leonhart in his New York Times blog, last week:
The political prediction markets just went through their version of the dot-com bubble. […]
Intrade’s odds have had a very good forecasting record over the last few years, having correctly called every Senate race in 2006, every state in the 2004 presidential election and all but one state in the 2004 Senate races. The odds also correctly called New Hampshire for John McCain this week and now make him the favorite for the Republican nomination- he is given a 38 percent chance, while Rudolph W. Giuliani is given a 29 percent chance.
Intrade’s executives, as well as the academic researchers who study the site, are careful to point out that its contracts provide only odds, not certainties. An outcome that’s given a 20 percent chance of happening should happen 20 percent of the time — not never. […]
The question I asked yesterday was: What would happen if that warning label were to be sticked on InTrade before each election, as opposed to after each predictive debacle? My bet is that, if you suppress the mention of InTrade’-s magical touch, the Irish real-money prediction markets will be far less appealing to people. They want magic. All of the sudden, InTrade is not a psychic anymore, but simply a forecasting tool of convenience for busy people who don’-t want to check the polls in details. This issue is crucial if we want to be able to define what is the “-prediction market approach”- —-as opposed to the “-betting exchange approach”-.
Give me one reason why the political analysts should follow the US primaries thru the prism of the InTrade prediction markets instead of thru the polls. [My question is still unanswered, you will notice. Which shows to you the embarrassment of the prediction market luminaries (or so they think they are).]
Once the true nature of the prediction markets appears more clearly, it becomes evident that they are not tools for the experts, but tools for the ignorants, rather. Which is great, provided that this is said clearly from the start.
Sean Park (after a long, boring introduction to the subject):
[…] The ‘failure’ of New Hampshire was the result of primarily two factors:
- It wasn’t a failure. No market is always right. More importantly markets reflect the information available to and the interests of their participants. Basically markets are very efficient mechanisms (I would claim the most efficient) for processing information. No more, no less.
- In this particular instance, the probability of the market producing an erroneous forecast was high due to the lack of liquidity. This is a problem of all political markets in the US. Show me a market on the New Hampshire primaries with tens of thousands of participants and millions of dollars traded and I will show you a market that creates more valuable information. BUT it would still on occasion be ’surprised.’
Basically I guess what I’m trying to say is the expectations seem to be set all wrong by many inside the community. I think “prediction markets” – creating markets in information and outcomes is a wonderfully important and valuable thing to do. Equally however I think that anyone that represents such markets as being able to predict the future is a charlatan. What they can do is collect and synthesize powerfully and efficiently all the dispersed available information – using money as the relevance filter. This is very valuable in its own right and is defensible. Promoting prediction markets to true sceptics (ie mainstream American politicians) on the basis that they are a Delphic Oracle is surely a path to certain tears and ultimately is almost guaranteed to fail. [*]
Markets don’t compute unknown unknowns. That doesn’t mean they are useless, just that they have to be understood in context.
[*] How to promote the prediction markets, then? As information collecting tools? Who should use these tools, then? Experts or ignorants? Sean Park does not elaborate further. None of the questions I have asked are answered.
In “-private beta”-.
So I can’-t say anything.
Or, next thing, I’-m a dead blogger.
As soon as you catch this post, RUSH THERE AND TAKE A VIRTUAL TOUR. Awesome. Nigel Eccles is the man. John Delaney, David Jack, Adam Siegel and Emile Servan-Schreiber can return to the locker room. Robin Hanson and Justin Wolfers are history artifacts, starting today. The whole world will look completely different after the HubDub launch.
How come nobody got that idea (news aggregation + prediction exchange) before HubDub???
Justin Wolfers:
So we decided to move beyond asking, “Do prediction markets work?” and instead use them as a tool for better understanding how information flows within a (very cool) corporation.
I am more interested in the accuracy of the enterprise prediction markets than in corporate micro-geography issues.
Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)
Robin Hanson in a comment on Marginal Revolution:
This is important work for organizational sociology, but not for prediction markets, as this does little to help us find and field high value markets.
Finally, somebody who speaks the truth.
See also the comment of economist Michael Giberson.
Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)
Robin Hanson:
Yes prediction markets are cool, Google is cool, and it is cool that Google had location data to show how location influences trading. But cool need not be useful. People are not asking the hard questions here: what value exactly is Google getting out of these markets, aside from helping them look cool?
Robin Hanson is a modern-day hero. Speaks the truth. Has a clear vision. Doesn’-t mind to act as a contrarian, now and then. Like Winston Churchill. Is a real leader.
Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)
Panos Ipeirotis in a comment here:
[W]e should try to separate two things: Market efficiency and market accuracy. Efficiency is the rate in which the market incorporates new information and prevents any arbitrage opportunities. Accuracy is the probability in which the market predicts the correct outcome of an event. The main claim to fame for the [prediction] markets is that they self-report their accuracy, and that “the prices are probabilities”.
We can measure the effectiveness of the market by following the outline discussed above. One axis is the price of the contract at time t before the expiration of the contract and the other axis is the rate in which this event happens. (…60% of the cases the event that trades at 0.6 happens, 30% of the cases the event that trades at 0.3 happens, and so on…). A perfectly accurate market should have a straight line as an outcome when time t gets close to 0. Any deviation of the experimental results indicates an accuracy bias. There are many papers that indicate the favorite-longshot biases in the market (underprice the favorite, overprice the longshots) so there is no need to really repeat this here. An interesting thing is to see how big it can be and still have reasonable accuracy. Furthermore, if we have systematic and robust biases, then we can use a calibration function that will adjust the market prices, compensating for the biases, to reflect real-life probabilities.
Measuring efficiency is a trickier concept. The general definition of efficiency is that “the market immediately incorporates all available information”. Being able to predict price movements indicates inefficiency. Having prices for an event summing up to anything other than 1, indicates inefficiency. However, it is difficult to have a definite proof that the market is efficient. We can only say that “we were not able to spot inefficiencies”. It is very difficult to prove that “the market is efficient”.
The two metrics are, of course, highly connected close to the expiration of the contract. If the market is not efficient, then it will not be accurate, as it will not have had incorporated all the available information, if any material information becomes available just before the expiration of the contract.
Panos Ipeirotis