APRIL FOOLS DAY: This year, again, CNET makes fun of the wisdom of crowds.

No GravatarCNET: Edit wars come to spy agencies&#8217- Intellipedia.

Intellipedia

The full listing of all the April Fool&#8217-s Day jokes&#8230-

Previously: The 2007 CNET joke&#8230-

Previously: The 2008 Midas Oracle joke&#8230-

Previous blog posts by Chris F. Masse:

  • Thanks to enterprise prediction markets, senior management can move faster to deal with problems or exploit opportunities.
  • NOTE TO SELF: Set up customized e-mail alerts for brand-new, hot Midas Oracle stuff.
  • DAYS OF RECKONING, PART TWO: Matt Drudge features the prediction markets. + Reuters has the right terminology (“traders”, “prediction exchanges”) but ignores BetFair.
  • DAYS OF RECKONING: The New York Times is telling the business world that enterprise prediction markets are an essential management tool.
  • HubDub will soon distribute a continuously-updating chart widget displaying the state of their prediction markets.

Last years best April Fools Day joke had something to do with the wisdom of crowds.

No Gravatar2007’s April Fool’s Day

CNET:

Wikipedia founder&#8217-s bold experiment

Diagnosed with cataracts, Jimmy Wales invites first 100 people who show up at his home to perform surgery. &#8220-There may be some trial and error, but I&#8217-m confident the community will make the right decisions,&#8221- Wales said.

MIDAS ORACLE&#8217-S 2008 APRIL FOOL&#8217-S DAY JOKE: BetFair-TradeFair hire Bo Cowgill in an attempt to improve their ranking in Google web search results.

Previous blog posts by Chris F. Masse:

  • Play-money prediction exchange HubDub is a phenomenal success.
  • BetFair Australia’s spin doctor tells all about their payments to the horse race industry.
  • Meet Jeffrey Ma (at right on the photo), the ProTrade co-founder, and whose gambling life is the basis of the upcoming movie, 21.
  • Independent production company seeks deep throats to spill beans on online poker industry and BetFair Poker.
  • BetFair-TradeFair hire Bo Cowgill in an attempt to improve their ranking in Google web search results.

New Hampshire fiasco blamed on lack of InTrade traders diversity

On A Limb

Mister Kirtland:

[&#8230-] I’m going to go out on a limb here and say that the traders on InTrade may not be the most diverse group of people we could assemble. I would bet, for example, that not many people from New Hampshire – who would have more direct knowledge of the situation “on the ground” – bet on InTrade. [&#8230-]

As I already wrote here, I don&#8217-t buy the argument, but let&#8217-s put that aside.

In public prediction exchanges, traders are self selected. Nothing can be done to change that. Am I correct?

Read the previous blog posts by Chris. F. Masse:

  • Davos – World Economic Forum
  • CME Group = Chicago Mercantile Exchange + Chicago Board Of Trade
  • Democratic and Republican caucuses in Nevada + Republican primary in South Carolina
  • The BetFair blog is not a serious publication.
  • MICHIGAN PRIMARY @ BETFAIR: Niall O’Connor asks the very pertinent question.
  • One thing John Delaney and his Irish employees at InTrade-TradeSports can learn from the BetFair-TradeFair folks at HammerSmith.
  • BetFair compound chart on the Michigan primary

GIGO and prophets, tears and markets

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Prediction markets failed to accurately predict the unexpected effect a few tears had on the New Hampshire primaries- and some analysts rushed to blame the tool and undermine its reliability and applicability. Let me restate some fundamentals and my view, in a snapshot:

  • Markets are not prophets, prophets do not exist.
  • A mechanism&#8217-s forecastability should not be judged against a virtual fool-proof prophet- we&#8217-d better compare it with other existing or widely-used mechanisms and -to my partial and context-bound knowledge- markets outperform all those.
  • Markets are the only tool that intrinsically suggests their probability of failure. If Obama&#8217-s stock is traded at 70 cents, this suggests that there is a 30% probability of Obama losing- I&#8217-d say markets are by character modest and no fanfare has any place in describing their suggestions.
  • Markets are primarily an aggregation/meta mechanism- as such, garbage-in-garbage-out effects are expected to happen, so we&#8217-d need to keep focus on minimizing garbage rather than blaming the market/compiler.
  • Maturity of the mechanism and its use, as long as trading volume (in real-money intrade for example), have not yet reached a fully efficient level (more on this to come soon), but these result in significant profit opportunities, so I expect things to just keep getting better.

cross-posted from my blog

Who did best in explaining the prediction markets to the lynching crowd?

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After the New Hampshire fiasco, 16 18 people came to defend the prediction markets, so far. So far, the best takes are from:

  1. George Tziralis
  2. Robin Hanson
  3. Jonathan Kennedy
  4. and I&#8217-ll give the 4th spot to a combo, mixing takes from John Tierney, Adam Siegel (surprisingly pertinent &#8211-I bet he is on a fish diet, post Christmas :-D ), and Steve Roman.
  5. UPDATE: &#8220-Thrutch&#8220-, Emile Servan-Schreiber and Panos Ipeirotis.

AWOLs (so far): PMIA, AEI-Brookings, InTrade, TradeSports, BetFair, TradeFair, NewsFutures, Emile Servan-Schreiber, Jed Christiansen, Koleman Strumpf, Bo Cowgill, Richard Borghesi, Chris Hibbert, David Perry, Ken Kittlitz, Paul Tetlock, David Pennock, Mike Linksvayer, Brent Stinsky, David Yu, Mark Davis, David Jack, James Surowiecki, Tyler Cowen, Greg Mankiw, Donald Luskin, John Delaney [*], etc.

[*] Steve Bass tells us that John Delaney&#8217-s pre-NH CNBC appearance was awesome. I was up that day, waiting for that CNBC segment, but failed to spot it. If somebody sends me the YouTube link, I&#8217-ll publish it here.

THE SILICON ALLEY BLOG COMES TO THE RESCUE OF THE PREDICTION MARKETS.

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Silicon Alley&#8217-s Jonathan Kennedy:

[…] In denouncing prediction markets as &#8220-wrong,&#8221- however, many pundits miss the point. Prediction markets do not provide accurate predictions of the future. (How could they? They simply represent the consensus guess of a group of people who aren&#8217-t prophets). They merely provide the most-informed guess as to what that future is likely to be.

As numerous &#8220-collective wisdom&#8221- studies have shown, the consensus guess is always better than the majority of the individual guesses that are factored into it (not sometimes&#8211-always). The collective wisdom, moreover, is often more accurate than that of ANY individual. Why? Because the market collectively incorporates far more information than is available to any one individual.

Like the stock market, prediction markets don&#8217-t get it right every time. They do, however, provide a useful window into the collective expectations of others&#8211-one that is often the best available estimate of the future. And they do sometimes get it right. Just as they did with Mr. McCain.

Bravo, mister Jonathan Kennedy.

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Take that, Barry Ritholtz. :-D

In an upcoming post, we will review the strengths and weaknesses of these thinly traded prediction markets&#8230-

We are holding our breath, Barry. Hurry up.

Prediction Markets = the greatest time-saving invention of this century

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John Tierney (and again this morning):

[…] Keeping up with a presidential campaign used to require at least an hour a day of wading through punditry, and much more time during the peak primary season. But now, with a few clicks on Intrade, you can see the accumulated expertise of thousands of people betting on the campaign. […]

That&#8217-s what I mean by &#8220-Prediction markets are forecasting tools of convenience that feed on advanced indicators&#8220-.

I will have another post on John Tierney&#8230- if Barry Ritholtz delivers on his promise to write up on prediction markets.

Please, make WordPress a bit like Wikipedia.

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Folks, here is my proposal to the WordPress developers:

Assign a great number of editors to some specific pages

Right now, if you are an editor in WordPress, you can edit any posts and pages. Hence, the administrator of a big group blog would not have many editors &#8212-because the blog posters would not like the idea that their colleagues can edit their posts.

But it would be great to be able to have a great number of editors for some specific pages. That way, any group blog powered by WordPress would be able to tap in the &#8220-wisdom of crowds&#8221- (see James Surowiecki book by the same name) &#8212-the same way Wikipedia does. For more on Wikipedia, see these two posts.

Collective intelligence (a.k.a. wisdom of crowds) is a mechanism at the heart of Google PageRank, Wikipedia, open-source software, prediction markets, etc. It is very powerful. WordPress could tap into that very easily, by allowing a page-by-page editing role.

The WP admin would set who are the editor(s) of a particular page &#8212-one registered person, two, a bunch of blog authors&#8230- or any internet citizens like in Wikipedia.

Thanks a lot for your attention. Contact me for more info, or leave a comment below.

NEXT: WordPress is a bit like WikiMedia (the software powering Wikipedia), now.

Previous blog posts by Chris F. Masse:

  • The definitive proof that FOR-PROFIT prediction exchanges (like BetFair and InTrade) are the best organizers of socially valuable prediction markets (like those on global warming and climate change).
  • Fairness Doctrine prediction markets
  • 2 MILLION TRADES LATER: Inkling’s play-money prediction markets are accurate —too.
  • Web Forums on Prediction Markets
  • Jason Ruspini will answer SOME of these CFTC questions. — 12 days left, Jason.
  • QUIZZ OF THE DAY: Which blog is the most open minded?
  • Prediction Markets TV — Will the controversial but indispensable Max Keiser (ex-HSX) stay true to his purpose, or will he f*** it up?

Amateur Journalists (Bloggers) Vs. Professional Journalists (Media) Vs. Wisdom Of Crowds & Collective Intelligence (Wikipedia)

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And the wisdom of crowds won, of course. That&#8217-s the conclusion I draw from reading Rogers Cadenhead at WorkBench, who assessed what would be the settlement of the LongBets wager on:

In a Google search of five keywords or phrases representing the top five news stories of 2007, weblogs will rank higher than the New York Times&#8217- Web site.

AGREE
Dave Winer

Stakes
$2,000
($1,000 each)

DISAGREE
Martin Nisenholtz

For Rogers Cadenhead, Dave Winer will win the bet. But he also says that the overall winner is&#8230- WIKIPEDIA.

[…] So Winer wins the bet 3-2, but his premise of blog triumphalism is challenged by the fact that on all five stories, a major U.S. media outlet ranks above the leading weblog in Google search. Also, the results for the top story of the year reflect poorly on both sides. In the five years since the bet was made, a clear winner did emerge, but it was neither blogs nor the Times. Wikipedia, which was only one year old in 2002, ranks higher today on four of the five news stories: 12th for Chinese exports, fifth for oil prices, first for the Iraq war, fourth for the mortgage crisis and first for the Virginia Tech killings. Winer predicted a news environment &#8220-changed so thoroughly that informed people will look to amateurs they trust for the information they want.&#8221- Nisenholtz expected the professional media to remain the authoritative source for &#8220-unbiased, accurate, and coherent&#8221- information. Instead, our most trusted source on the biggest news stories of 2007 is a horde of nameless, faceless amateurs who are not required to prove expertise in the subjects they cover.

So the real winner is Wikipedia &#8212-a news and knowledge aggregator&#8230- using anonymous volunteers. But Wikipedia is only an information aggregator&#8230- it feeds on both media and blogs to gather the facts. Wikipedia is the common denominator of knowledge &#8212-not the primary source of reporting. Just like prediction markets feed on polls and other advanced indicators.

External Link: See a previous assessment of the bet by Jason Kottke.

NEXT: Amateur Experts (Yahoo! Answers) Vs. Wisdom Of Crowds &amp- Collective Intelligence (Wikipedia)

UPDATE: An empty comment from Read &#038- Write Web.

James Surowieckis The Wisdom Of Crowds… still stands.

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James Surowiecki&#8217-s 4 comments at Overcoming Bias (in October 2007), responding to accusations that he got it all wrong about Francis Galton:

&#8212-

James Surowiecki&#8217-s 1st comment:

&#8220-Galton did not even bother to calculate a mean, as he saw his data was clearly not normally distributed. He used the median (of 1207), which was much further off than the mean, but by modern standards clearly the better estimator. It was Karl Pearson in 1924 who calculated the mean.&#8221-

Robin [Hanson], before repeating falsehoods, you might want to go back to the original sources &#8212- or, in this case, to the footnotes to my book. Galton did, in fact, calculate the mean, long before Karl Pearson did. Galton&#8217-s calculation appeared in Nature, Vol. 35, No. 1952 (3/28/07), in a response to letters regarding his original article. One of the correspondents had gone ahead and calculated a mean from the data that Galton had provided in his original piece, and had come up with the number 1196. Galton writes, &#8220-he makes it [the mean] 1196 lb. . . . whereas it should have been 1197 lb.&#8221-

I find the fact that Levy and Peart wrote an entire article about Galton (and, to a lesser extent, about my use of him), and never went back and checked the original sources is astounding in its own right. (They actually wonder in the paper, &#8220-However the new estimate of location came to be part of Surowieki’s account,&#8221- as if the answer isn&#8217-t listed right there in the footnotes.) What makes it even more astounding, though, is that they&#8217-ve written an entire paper about the diffusion of errors by experts who &#8220-pass along false information (wittingly or unwittingly)&#8221- while passing along false information themselves.

It also seems bizarre that Levy and Peart caution, &#8220-The expectation of being careful seems to substitute for actually being careful,&#8221- and yet they were somehow unable to figure out how to spell &#8220-Surowiecki&#8221- correctly. The article is a parody of itself.

I&#8217-m happy to enter into a discussion of whether the median or the mean should be used in aggregating the wisdom of crowds. But whether Galton himself thought the mean or the median was better was and is irrelevant to the argument of my book. I was interested in the story of the ox-weighing competition because it captures, in a single example, just how powerful group judgments can be. Galton did calculate the mean. It was 1197 lbs., and it was 1 lb. away from the actual weight of the ox. The only &#8220-falsehood&#8221- being perpetrated here are the ones Levy and Peart are putting out there, and the ones that you uncritically reprinted.

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James Surowiecki&#8217-s 2nd comment:

Here are the links for the letter from Galton, where he reports the mean:

http://galton.org/cgi-bin/search/images/galton/search/essays/pages/galton-1907-ballot-box_1.htm
http://galton.org/cgi-bin/search/images/galton/search/essays/pages/galton-1907-ballot-box_2.htm

There&#8217-s no reason for debate here. Levy and Peart say &#8220-Pearson’s retelling of the ox judging tale apparently served as a starting point for the 2004 popular account of the modern economics of information aggregation, James Surowieki’s Wisdom of Crowds.&#8221- It wasn&#8217-t the starting point. The starting point was Galton&#8217-s own experiment, and his own reporting of the mean in &#8220-The Ballot Box.&#8221- Robin writes: &#8220-Galton did not even bother to calculate a mean.&#8221- He did calculate it, and he did report it. This fact shouldn&#8217-t be listed as an &#8220-addendum&#8221- to the original post. The original post should be rewritten completely &#8212- perhaps along the lines of &#8220-Surowiecki and Galton disagree about which estimate is a better representation of group judgment&#8221- rather than &#8220-Author Misreads Expert&#8221- &#8212- or else scrapped.

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James Surowiecki&#8217-s 3rd comment:

I appreciate Levy and Peart admitting their mistake. But they seem not to recognize that their mistake undermines the critique that&#8217-s at the center of their paper. Their paper, they write, is about the misconstruing of Galton&#8217-s experiment. &#8220-A key question,&#8221- they write, &#8220-is whether the tale was changed deliberately (falsified) or whether, not knowing the truth, the retold (and different) tale was passed on unwittingly.&#8221- But the account of Galton&#8217-s experiment was not changed deliberately and was not falsified. It was recounted accurately. Levy and Peart want to use my retelling of the Galton story as evidence of how &#8220-experts pass along false information (wittingly or unwittingly) [and] become part of a process by which errors are diffused.&#8221- But there&#8217-s no false information here, and no diffusion of errors, which rather demolishes their thesis. If they really want to write a paper about how &#8220-experts&#8221- pass along false information, they&#8217-d be better off using themselves as Exhibit A, and tell the story of how they managed to publish such incredibly shoddy work and have prominent economists uncritically link to it.

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James Surowiecki&#8217-s 4th comment:

To finish, Levy and Peart insist that their really important point still stands, which is that &#8220-When people quote Galton through Surowiecki, they tell Surowiecki&#8217-s tale, not Galton&#8217-s,&#8221- and that this is a problem because Galton&#8217-s thinking is being misrepresented. But as I said earlier, &#8220-The Wisdom of Crowds&#8221- was not intended to be a discussion of Francis Galton&#8217-s opinions on what&#8217-s the best method to capture group judgment, nor, as far as I know, has anyone who&#8217-s &#8220-Surowiecki&#8217-s tale&#8221- used the Galton example since used it to analyze Galton&#8217-s opinions. People aren&#8217-t quoting the Galton story because they&#8217-re interested in what Galton himself thought about the median vs. mean. They&#8217-re quoting it because they&#8217-re interested in the bigger idea, which is that group judgments (and this is true whether you use the median, the mean, or a method like parimutuel markets) are often exceptionally accurate. Levy and Peart have constructed a straw man &#8212- and, in this case, a straw man based on a falsehood &#8212- and then tried to knock it down.

Robin [Hanson] writes: &#8220-it is ironic that Galton made quite an effort to emphasize and prefer the median, in part because the data did not look like a bell curve, while your retelling focuses on him calculating a mean after checking for a bell curve.&#8221- What&#8217-s ironic about this? He did check for a bell curve, and he did calculate the mean. It&#8217-s the data themselves, not Galton&#8217-s interpretation of them, that I was writing about. (If he hadn&#8217-t calculated the mean, I would have happily told the story with the median, since it was also remarkably accurate, and demonstrated the same point about the wisdom of crowds.)

Finally, on the substantive question, Robin (and Levy and Peart) seem to think that because the distribution of guesses wasn&#8217-t normal, that makes using the mean a mistake. But this is precisely what&#8217-s so interesting: if the group is large enough, even if the distribution isn&#8217-t normal, the mean of a group&#8217-s guesses is nonetheless often exceptionally good.