REALITY CHECK:
“-The biggest challenge is getting people in the company to be active”- [].
Common pitfalls of enterprise prediction markets: participants who lack relevant information, too few participants, and too little trading.
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Tag Archives: Collective Forecasting
The prediction market links that Robin Hanson does not want you to read
Robin Hanson cant ignore Paul Hewitt anymore.
Spot the comments in the following posts:
– Robin Hanson to Paul Hewitt –- #1
– Robin Hanson to Paul Hewitt –- #2
The Robin Hanson manipulation papers make unrealistic assumptions, but its not like prediction markets are a bad idea…!!…
In terms of unrealistic assumptions in Robin Hanson’-s series of papers on manipulation, the major ones have been out there since at least 2004.
Despite some limited evidence, the insistence on traders needing to know the direction of manipulation isn’-t too compelling since the direction will be manifest insofar as the price is “-wrong.”- “-Noise trader”- is a politically loaded and misleading term. Misleading because it suggests that the mean effect will be zero, when in reality “-noise trading”- usually takes the form of feedback trading. Lack of feedback trading is a significant assumption in the Hanson manipulation papers. Fortunately, prediction markets have objective settlements at specified times, unlike traditional assets where the meaning of prices is open to interpretation, making them more prone to feedback trading and irrational booms and busts.
With prediction markets, conditions for manipulation are more favorable when the settlement is far off in time, and when there are subjective inputs to the settlement, e.g. in politics. A distant settlement simultaneously makes it less clear what the real price should be, and delays manipulator losses because there is less incentive to correct price. At the limit, a manipulator could introduce a price distortion when a contract is launched, only to reverse position for small liquidity-related loss immediately before settlement, thereby destroying the markets “-integral”- of error over time.
Another big assumption, also identified by Paul Hewitt, is that traders have equal account sizes. But maybe this isn’-t a huge problem if settlement is forthcoming, and maybe the issue could be mitigated with additional exchange disclosures, such as the standard deviation of position sizes in a given market. While this could discourage liquidity as large traders would become paranoid about their positions, it is essentially a “-soft”- position limit, and traders would be forewarned of one-sided markets (which could of course be the result of someone well-informed, but I –- the google-anonymous* writer –- would bet that more concentration comes with more error on average…- this can be tested by someone with the data, of course maintaining trader anonymity)
Even accounting for long-term settlement, feedback trading, semi-subjective settlements, and account size imbalances, it seems one would have to abide to an overly rigid tenet of “-do no harm”- to hold that prediction markets are, on net, a bad idea. (Do no harm is of course abhorrent to libertarians, and even doctors don’-t actually follow such a rule.) Moreover, some pathologies like political self-fulfilling prophecy will only happen if prediction markets have already demonstrated their value and have become more popular. But even if one believes in their long term success, single pathologies can damage one’-s reputation permanently…- if one plans to die at a reasonable age.
[*Given the political climate, many firms have issued directives to employees to not engage in even the slightest appearance of impropriety, which might include blogging on manipulation.]
Paul Hewitt doubts.
Finally, we need to know if this was only possible, because it was a fairly simple experimental model. Will the same decision-maker’s ability exist in extremely complex public policy markets?
Prediction Markets @ Singularity University
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Lets boycott the $400 vendor conference on prediction markets.
I renew my boycott on the $400 vendor conference on prediction markets.
As I said many times, do not pay anything (not even $4) to listen to vendors’- marketing message —-and to illuminated academics bought by these vendors.
They all exaggerate the usefulness of the prediction markets.
And beware that phone-booth conference organizer who hides under a female account and a “-legal assistant”- account on that e-mailing list.
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iPredict New Zealand = good predictor
Who has the best analysis for Chicagos failed bid for the Olympics?
I think the a€?small, secretive committeea€? explanation is weak [].
Bradbury does an excellent job sifting through the shifting coalitions revealed in the three rounds of IOC voting. Neither Madrid nor Toyko showed any significant ability to attract votes as the rounds proceeded. It was going to be Rio or Chicago all along, but Chicago was weakest in the four-way vote and lost early, leaving the games to go to Brazil.
Based on Bradburya€™s [analysis], Ia€™m convinced that the decision was pretty much a toss up between Chicago and Rio. That conclusion was also implied in the prediction market prices just before the decision. Sure, the prediction markets favored Chicago, slightly, over Rio- I dona€™t think you can call it a miss given the closeness of the decision.
Well:
- The voting mechanism of the IOC regarding the 2016 Olympics venue was known to the news media and the prediction market traders (like Ben Shannon) well before the vote.
- The prediction market traders gave a surreal boost to the Chicago probability.
- The reality check is that Chicago was the weakest candidate.
- Hence, the prediction market traders were not informed enough about the basic facts regarding the IOC voting, for the reason that the International Olympic Committee is governed by secrecy, politics, and pork.
Previously: Chicago wona€™t have the Olympics in 2016.
ADDENDUM:
– BetFair’-s event derivative prices:
– InTrade’-s event derivative prices:
– HubDub’-s event derivative prices:
If Michael Giberson is wrong, then that means that Chris Masse is right.
I donta€™ know that you could say Chicago was the a€?weakest linka€?, just because it got dropped first in the voting. The political process caused it to go early. However, Michael Giberson is wrong to imply that the prediction was accurate on the basis that Chicago and Rio were fairly close. Leta€™s keep in mind that the options are about as discrete as they come. Even if Chicago were to have come in a close second, it would have been a complete miss by the market.
If one needed to make a decision that depended on whether Chicago would win the bid, the prior choice would have been completely wrong, once the true outcome was revealed.
I have to agree with Chris. The market participants did not possess a sufficient level of information completeness to arrive at the correct prediction. Furthermore, the discrete nature of the outcomes made it a risky prediction. Finally, Ia€™m guessing that few, if any, of the IOC voting members were involved in the prediction markets, leading one to conclude that all (or almost all) of the market participants were a€?noisea€? traders.
Elsewhere, another commentator claimed that, because the prediction market started to show Chicagoa€™s share falling during the morning of the vote, this was evidence that prediction markets work. Hardly. It does show that prediction markets rarely provide accurate predictions sufficiently in advance of the outcome, in order for useful decisions to be made.
The prediction market industry really needs to investigate the determinants of success and which types of markets (issues) have the potential to provide consistently accurate predictions. Way too much time and effort is being spent arguing about meaningless markets, trivial questions, and false accuracy claims.
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Previously: Chicago wona€™t have the Olympics in 2016.
ADDENDUM:
– BetFair’-s event derivative prices:
– InTrade’-s event derivative prices:
– HubDub’-s event derivative prices: