ROBIN HANSON TELLS THE TRUTH ON GOOGLES ENTERPRISE PREDICTION MARKETS.

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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&#8217-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)

Robin Hanson is not convinced by the Google experiment with enterprise prediction markets -to say the least.

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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)

Have Googles enterprise prediction markets been accurate?

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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)

Googles enterprise prediction markets = a slavery instrument based on self-fulling prophecies?

Search engine expert Barry Schwartz:

[&#8230-] Why does Google encourage such activity from their employees? [&#8230-] It also helps stimulate an &#8220-optimism bias,&#8221- which in turn encourages Google employees to work harder to achieve a certain outcome they have predicted in the [prediction] market[s]. [&#8230-]

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)


Author Profile&nbsp-Editor and Publisher of Midas Oracle .ORG .NET .COM &#8212- Chris Masse&#8217-s mugshot &#8212- Contact Chris Masse &#8212- Chris Masse&#8217-s LinkedIn profile &#8212- Chris Masse&#8217-s FaceBook profile &#8212- Chris Masse&#8217-s Google profile &#8212- Sophia-Antipolis, France, E.U. Read more from this author&#8230-


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

  • Comments are now completely open on Midas Oracle.
  • Albert Einstein, Chairman of the Midas Oracle Advisory Board
  • Erratic –but not Stochastic– Charts
  • Barack Obama is the 44th US president.
  • We already have prediction markets in future tax rates. It’s called the municipal bond yield curve.
  • DELEGATES AND SUPERDELEGATES ACCOUNTANCY
  • O’Reilly – Money-Tech Conference

We don’t know whether Google approach to management, and in particular its approach to innovation, is a cause of its success or a product of its success.

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Good point.

[…] Many of the most innovative and successful of Google’s new ser­vices are, in fact, ones it has acquired rather than created. Those include the hugely popular video-sharing service YouTube, the Weblog publisher Blogger, the virtual globe Google Earth, the online word processor Writely (renamed Google Docs), the wiki developer JotSpot, the news syndication service Feedburner, and the Internet phone service GrandCentral. When it comes to innovation, Google is starting to look less like a sower than a harvester, less like an inventor than an exploiter. […]

There are signs that Google is coming to recognize this problem. Over the past year, its management has begun tightening the reins on its organization, imposing some restrictions on the company’s freewheeling and free-spending culture. Late in 2006, in what CEO Schmidt called “a big change in the way we run the company,” it ordered its innovation teams to focus on fewer initiatives and reduce the overall number of products under development by 20 percent. An exasperated Sergey Brin admitted that he “was getting lost in the sheer volume of the products that we were releasing.” And when the company announced disappointing earnings for the second quarter of 2007, Schmidt put the blame on overhiring and announced that the company would be more conservative in expanding its staff in the future. Google is hardly staid, but it is growing up. […]

InTrade-TradeSports and BetFair-TradeFair are barred from advertising on Google, Yahoo! and MicroSofts networks of websites.

Via Jason Ruspini and Daniel Horowitz, The Associated Press &amp- Reuters.

But I will remark that Google Ads serve both InTrade and TradeSports. [I don&#8217-t mind. Just a remark.]


Author Profile&nbsp-Editor and Publisher of Midas Oracle .ORG .NET .COM &#8212- Chris Masse&#8217-s mugshot &#8212- Contact Chris Masse &#8212- Chris Masse&#8217-s LinkedIn profile &#8212- Chris Masse&#8217-s FaceBook profile &#8212- Chris Masse&#8217-s Google profile &#8212- Sophia-Antipolis, France, E.U. Read more from this author&#8230-


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

  • Comments are now completely open on Midas Oracle.
  • Albert Einstein, Chairman of the Midas Oracle Advisory Board
  • Erratic –but not Stochastic– Charts
  • Barack Obama is the 44th US president.
  • We already have prediction markets in future tax rates. It’s called the municipal bond yield curve.
  • DELEGATES AND SUPERDELEGATES ACCOUNTANCY
  • O’Reilly – Money-Tech Conference

HISTORY: Prediction Markets Timeline

For an updated version of this document, see the &#8220-paged&#8221- Prediction Markets Timeline.

CHRONOLOGY &amp- HISTORY: Prediction Markets Timeline

Feel free to post a comment or contact me, and I&#8217-ll correct or add a factoid. Thanks.

#1. Historical Prediction Markets

According to Paul Rhode and Koleman Strumpf, prediction markets almost never got it wrong forecasting the 19 presidential elections that took place from 1868 to 1940. (PDF)

#2. The three Iowa Electronic Markets founders (Robert Forsythe, Forrest Nelson and George Neumann)

&#8220-We ran our first market in 1988. We didn’t have regulatory approval at that point so we were restricted solely to the University of Iowa community. We had under 200 traders and under $5,000.&#8221- &#8211- [Robert Forsythe – PDF file]

– [CFTC’s no-action letter to the IEM – 1992 – PDF file]

– [CFTC’s no-action letter to the IEM – 1993 – PDF file]

#3. Robin Hanson

a) Robin Hanson set up and ran a rudimentary prediction exchange (a market board, PPT file) in January 24, 1989. The outcome to predict was the name of the winner of a Poker party.

b) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a corporate prediction exchange &#8212-at Xanadu, Inc., in April 1989. See: A 1990 Corporate Prediction Market + Anonymity is important for employees trading on internal prediction markets.

Robin Hanson: &#8220-I started a market at Xanadu on cold fusion in April 1989. In May 1990, I started a market there on whether their product would be delivered before Deng died.&#8221-

c) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a bunch of imagination-based prediction markets. See the Murder Mystery Evening described by Barney Pell &#8212-circa June 8, 1989.

d) Until evidence of the contrary, it seems that Robin Hanson was the first to write a paper on prediction markets created and existing primarily because of the information in their prices (as opposed to markets created primarily for speculation and hedging).

Could Gambling Save Science? &#8211- (Reply to Comments) &#8211- by Robin Hanson &#8211- 1990-07-00
Market-Based Foresight: a Proposal &#8211- by Robin Hanson &#8211- 1990-10-30
Idea Futures: Encouraging an Honest Consensus &#8211- (PDF) &#8211- by Robin Hanson &#8211- 1992-11-00

e) Robin Hanson godfathered the Foresight Exchange (created in 1994) and NewsFutures (created in 2000).

f) Robin Hanson invented the concepts of decision markets (PDF) and decision-aid markets.

g) Robin Hanson invented a new market design (for the 2000-2003&#8242-s Policy Analysis Market), the Market Scoring Rules, a mix between CDA and Scoring Rules &#8212-now in use for most enterprise prediction markets and public, play-money prediction exchanges. Note that MSR is mainly used in a one-dimension version, but many researchers are interested in its combinatorial version.

#4. Other Pioneering Public Prediction Exchanges (Betting Exchanges, Event Derivative Exchanges) and Inventors/Innovators/Entrepreneurs

a) The Foresight Exchange was founded on September 22, 1994 by Ken Kittlitz, Sean Morgan, Mark James, Greg James, David McFadzean and Duane Hewitt. The Foresight Exchange is a play-money prediction exchange (betting exchange) managed by an open group of volunteers. It pioneered user-created and user-managed, play-money prediction markets. Any person can join the Foresight Exchange and interact with the rest of the Web-based organization. An independent judge (independent from the owner of the claim) should be appointed among the volunteers. [Thus, it’s not “DYI prediction markets”.]

b) The Hollywood Stock Exchange was founded on April 12, 1996, by Max Keiser and Michael Burns. See the patent for the Virtual Specialist. For more info, see: Is HSX the “longest continuously operating prediction market”??? &#8211- REDUX

c) BetFair was founded in 1999 by Andrew Black and Edward Wray, and was launched in England in June 2000. As of today, BetFair is the world&#8217-s biggest prediction exchange (betting exchange, event derivative exchange).

d) NewsFutures was founded in March 2000 and launched in September 2000 in France and in April 2001 in the US by Emile Servan-Shreiber and Maurice Balick. See: NewsFutures Timeline. NewsFutures was the first exchange to let people buy or sell contracts for each side of a binary-outcome event. The advantage of this design is that it avoids the need for &#8220-shorting&#8221-, a notion that tends to confuse novice traders. NewsFutures later extend that approach to deal with n-ary outcome events while implementing automatic arbitrage.

e) TradeSports was launched in Ireland in 2002 by John Delaney. InTrade was later purchased and became a non-sports prediction exchange (betting exchange). As of today, InTrade is the biggest betting exchange on the North-American market &#8212-where betting exchanges are still illegal. As for TradeSports, it closed at the end of 2008, alas.

#5. The Policy Analysis Market Brouhaha

a) Robin Hanson was the main economist behind the 2000–2003 US DoD&#8217-s DARPA&#8217-s IAO&#8217-s FutureMAP–Policy Analysis Market project. (For this project, Robin Hanson invented a new market design, the Market Scoring Rules.) On July 28, 2003, two Democratic US Senators called for the termination of PAM, the the big media gave airtime to their arguments, and the US DOD quickly ended the IAO&#8217-s FutureMAP program.

b) The second branch of the 2000–2003 US DoD&#8217-s DARPA&#8217-s IAO&#8217-s FutureMAP program was handled by the Iowa Electronic Markets and was intended to predict the SARS pandemic. (This project later gave birth to IEM&#8217-s Influenza Prediction Market.)

#6. James Surowiecki&#8217-s The Wisdom Of Crowds

a) James Surowiecki&#8217-s book, The Wisdom Of Crowds, was published in 2004.

b) Impact of The Wisdom Of Crowds.

#7. Recent Public Prediction Exchanges (Betting Exchanges, Event Derivative Exchanges) and Inventors/Innovators/Entrepreneurs

a) US-based and US-regulated HedgeStreet was launched in 2004 by John Nafeh, Russell Andersson, and Ursula Burger. A designated contract market (DCM) and a registered derivatives clearing organization (DCO), HedgeStreet is subject to regulatory oversight by the Commodity Futures Trading Commission (CFTC). In November 2006, IG Group bought HedgeStreet for $6 million.

b) Inkling Markets was launched in March 2006 and co-pioneered (with CrowdIQ, which later bellied up) the concept of DIY, play-money prediction markets.

c) In September 2006, TradeSports-InTrade was the first prediction exchange (betting exchange, event futures exchange) to apply Chris Masse&#8217-s concept of X Groups. See: TradeSports-InTrade prediction markets on Bush approval ratings.

d) HubDub was launched in early 2008 and is the second most popular play-money prediction exchange, behind HSX.

#8. Enterprise Prediction Markets

a) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a corporate prediction exchange &#8212-at Xanadu, Inc., in April 1989. See: A 1990 Corporate Prediction Market + Anonymity is important for employees trading on internal prediction markets.

b) In the 1996&#8211-1999 period, HP ran a series of internal prediction markets to forecast the sales of its printers.

c) Eli Lilly sponsored 10 public, industry-level prediction markets in April 2003 (on the NewsFutures prediction exchange).

d) Eli Lilly began using internal prediction markets in February 2004 (powered by NewsFutures).

e) Google&#8216-s Bo Cowgill published about their use of internal prediction markets in October 2005.

f) Since then, many companies selling software services for enterprise prediction markets have been created.

#9. Disputes Between Traders And Exchanges

a) The scandal of the North Korean Missile prediction market that erupted in July 2006 is, as of today, the biggest scandal that rocked the field of prediction markets.

Claude Allegre, Al Gore debunker

Claude Allegre debunks the first part of Al Gore&#8217-s documentary movie (the &#8220-catastrophism&#8221- part), and agrees with its second part (the &#8220-let&#8217-s use better technologies&#8221- part).

&#8220-Claude Allegre&#8221- at Google Search

Claude Allegre&#8217-s latest book (in French).

NEXT: An Inconvenient Truth &#8211- Al Gore’s movie

Predictocracy: Market Mechanisms for Public and Private Decisionmaking – THE MARKET WEB

Predictocracy: Market Mechanisms for Public and Private Decisionmaking &#8211- by Michael Abramowicz &#8211- 2007-xx-xx &#8211- (fall)

Chapter: The Market Web &#8211- (towards the end of the book)

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Michael Abramowicz:

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses.

Will trading play-money and/or real-money event derivative contracts become commonplace? It&#8217-s likely, at the contrary, that trading will remain an elite occupation and that prediction markets with appropriate liquidity will remain scarce. Unless Google, Yahoo! (with Yootopia) and/or MicroSoft has/have a secret plan to popularize betting exchanges &#8212-which could well be since Bo Cowgill, David Pennock and Todd Proebsting are ambitious guys.

&#8212-

Michael Abramowicz:

For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues.

Interest rate prediction markets would help, for sure. As for geographic forecasting, maybe non-trading mechanisms could help &#8212-for real estate, I&#8217-m thinking of Zillow, or some improved mechanisms derived on Zillow.

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Michael Abramowicz:

The Market Web

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses. For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues. That way, as the market predictions change, the spreadsheet&#8217-s bottom line would change as well. Predictions in many prediction markets may be interrelated, and so market participants in one prediction market will often have incentives to take into account developments in other prediction markets. Prediction markets thus can affect one another indirectly, as participants in one update their models based on developments in another.

Sometimes, however, it might be desirable to construct links among prediction markets so that changes in one automatically lead to changes in another. Consider, for example, the possibility of a market-based alternative to class action litigation. In Chapter 8, each adjudicated case represented a separate prediction market, but often there will be issues in common across cases. Many thousands of cases may depend in part on some common factual issues, as well as on some distinct issues. Legal issues also may be the same or different across cases. Someone who improves the analysis of any common factual or legal issue can thus profit on that only by changing predictions in a very large number of cases. A better system might allow someone to make a change across a single market and have that change propagate automatically to individual cases.

The critical step needed to facilitate creation of the market web is to allow a market participant to propose a mathematical formula to be used for some particular prediction market. Some of the variables in that formula could be references to other, sometimes new, prediction markets. For example, a market participant might propose in a market determining how much amages the plaintiff should receive a formula dependent on variables such as the probability that the plaintiff states a cause of action, the probability that the plaintiff was in fact injured, the probability given injury that the defendant caused the injury, the probability given a cause of action that the defendant is subject to strict liability, the probability given no strict liability that the defendant was negligent, and the damages that the plaintiff should be awarded if liability is proved. This formula, for example, presumably would allow for no damages where the plaintiff probably does not state a cause of action. Each of the components of this formula might be assessed with a separate prediction market. We can easily build the market web by combining three existing tools. The first tool is a text-authoring market. The relevant text would be the formula itself, including specifications of other prediction markets that would be used to calculate specific variables. As with any text-authoring market, a timing market would determine when a proposal to change the text should be resolved. Other markets might become live only once proposals to take them into account were approved. Ex post decisionmakers would assess the wisdom of these markets&#8217- recommendations in some fraction of cases to discipline the market&#8217-s functioning.

The second tool would be a simple normative prediction market corresponding to the text-authoring market. It might also be possible to have computer software that automatically parses the formula and consults various sources, but the market sponsor need not build this tool. Rather, ex post decisionmakers will assess the appropriate value for the normative prediction market based on the formula. An advantage of this approach is that it would make it easy to use complicated formulas, as well as formulas that depend in part on numbers from sources other than prediction markets, or from prediction markets of other types. In addition, this approach makes it easy to collapse a formula into a single prediction market, if that should prove desirable. The formula text simply would be changed to a description of the market to be created, such as &#8220-adjudication of plaintiff&#8217-s liability in a particular case.&#8221-

Finally, the third tool necessary is a mechanism for determining the market subsidy. A separate subsidy would be needed for the text-authoring market and the normative prediction market. Each of these subsidies could be determined by additional normative prediction markets, perhaps with fixed subsidies. The subsidy for the text-authoring market in turn would be distributed by the text-authoring market to individuals who have proposed particular amendments, and individuals who have participated in the assessment of particular amendments. The text-authoring market also could allocate a subsidy to the first individual who creates the market and proposes some text for it. When the text-authoring market produces a new formula reflecting additional prediction markets, the subsidy for the main prediction market would fall (since calculating a formula based on other prediction markets will often be relatively easy).

A single node in the market web would thus consist of a text-authoring market describing the node and providing a formula for calculating it, a normative prediction market, and a set of additional prediction markets for determining how to distribute a subsidy to the different components of the node. The nodes collectively create a web because the formulas link to other nodes- software, of course, could easily make these links clickable. At the same time, a mechanism is needed to determine what portion of the market subsidy each node should receive. A simple approach would be for a prediction market to be used for every link, to determine the portion of the subsidy for each node that should be allocated to each node linked to it. The total should add up to less than 1, leaving some portion of the subsidy for the node itself.

With these markets established, software could easily distribute a single subsidy for the market as a whole to market participants who have traded on individual nodes when the market closes. Market participants working on one portion of the web, meanwhile, would not have to assess the relative importance of one node to nodes that are only distantly related. It would also be straightforward to have a continuously open market, periodically collecting and distributing money in accordance with individual participants&#8217- success on the market.

This assumes that the market web would be arranged on a single server. It is possible, though, that a node on one market web might link to a node on another market web. If market sponsors allowed such links, it could promote competition among prediction market providers. It also partially answers one potential criticism of using prediction markets for decisionmaking, that a software engineer might hijack the government by faking some prediction market results. Market participants at least will have incentives to identify fake prediction markets and not link to them. In principle, it is possible to have government decisions based entirely on decentralized prediction markets. A caveat is that the government might want to subsidized individual market web providers, and it might use centralized prediction markets to accomplish that.

Whether or not the markets themselves are decentralized, they would allow market participants to make it easier to assess the basis for market predictions. Indeed, the market web is in some ways a substitute for deliberative prediction markets, because both provide means of helping observers understand the basis for the market&#8217-s predictions. An observer could look at any individual node of the market web and understand how it has been calculated, though inevitably there must be some &#8220-leaf&#8221- nodes that themselves do not contain any formulas. At the same time, software might allow an observer to find all of the nodes that link to a particular node. So a market participant addressing a factual issue relevant to many cases could link to all of the cases represented by that factual issue. As a particular issue becomes increasingly important, the subsidy for that node should rise, and market participants can profit on their analysis of the issues relevant to that node without worrying about details of individual cases.

[…]

Brainy stuff. I&#8217-ll mind this for a while. I&#8217-m sure that the Midas Oracle readers will find this idea original &#8212-and maybe, interesting.

Prediction Markets Definitions – REDUX REDUX

No GravatarI would like to comment on the post from the Hatena Diary blog. (By the way, please note that my URL has changed, because I corrected one word in the post title. Sorry for the inconvenience.)

#1. Speculation-oriented prediction markets/exchanges: TradeSports, BetFair.

#2. Hedging-oriented prediction markets/exchanges: HedgeStreet and all the Chicago exchanges that will do binary, European call options.

#3. Forecast-oriented prediction markets/exchanges: Iowa Electronic Markets, AS CLAIMED BY THESE SCHOLARS WHOSE TASK WAS TO CONVINCE THE CFTC TO GRANT THEM A NO-ACTION LETTER. (They would have not gotten it, had they emphasized &#8220-speculation&#8221-. And, of course, &#8220-hedging&#8221- was out of question.) It&#8217-s a &#8220-claim&#8221- that might be discussed, since we&#8217-ve seen that TradeSports-InTrade is a much more powerful predictive tool for the US elections. Ditto for BetFair for U.K. elections.

#4. Decision-oriented prediction markets/exchanges: I would put here the kind of stuff that Robin Hanson is so excited about.

#5. Entertainment-oriented prediction markets/exchanges: Hollywood Stock Exchange, Washington Stock Exchange, Inkling, NewsFutures.

#6. Education-oriented prediction markets/exchanges: The Iowa Electronic Markets fits here, partially, regarding the use that professors around the country make of their markets in classrooms.

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– I disagree with Google in #4. Maybe the Google internal prediction markets would fit in #3.

– I disagree with NewsFutures in #3 &#8212-I acknowledge (at least partially) the predictive power of play-money prediction exchanges, of course.

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Should we judge markets/exchanges on INTENTIONS or on RESULTS? I don&#8217-t give a damn that TradeSports-InTrade and BetFair were created for speculation– if they have better predictive power than IEM, I&#8217-m fine with them. Ditto for the HSX. I don&#8217-t give the first fig that it was created as an entertainment tool. It&#8217-s the best forecasting tool for the movie business, period.

&#8212-

For the links to the prediction exchanges, see CFM.

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Previous Blog Posts:

Prediction Markets DEFINITIONS – not a “taxonomy”

Professor Robin Hanson’s draft definitions is validated by professor Eric Zitzewitz.

Prediction Markets Definitions – REDUX

Prediction Markets Definitions – by Robin Hanson – 2006-11-21

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Addendum: Robin Hanson has posted a comment&#8230-

“Oriented” is not clear enough for my tastes. Is this about trader motives? Trader results? Price results? Exchange motives?

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My Answer: I meant &#8220-exchange motives&#8221-. [&#8230- See my comments. &#8230-] But now that I think of it, another classification taking account of the &#8220-price results&#8221- makes more sense.

Previous blog posts by Chris F. Masse: