Americans love rankings, but Americans hate to be assessed subjectively.

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I am afraid that Nigel Eccles&#8217- answer to The Numbers Guy does not go deep enough into the arguments.

The Numbers Guy (and his interviewees) made some points that Nigel does not address.

See his Video Response.


PS: Nigel, Seesmic is a piece of shit. The video does not go into the feeds (unlike YouTube or Blip.TV.)

Background Info: The Numbers Guy: HubDub&#8217-s PunditWatch is not as rigorous as Philip Tetlock was.

More Info: Interview via OPMs

Previous blog posts by Chris F. Masse:

  • The FaceBook profiles of the 2 most important men of the field of prediction markets
  • THE HUMAN GADFLY WHOSE OBJECTIONS ROBIN HANSON IS DUCKING…???…
  • Google now considers Midas Oracle as a major blog.
  • Horizon 2015: A long-term strategic perspective for the real-money prediction markets
  • Join our group at LinkedIn to have your “Prediction Markets” badge on your profile. It’s ‘chic’. (“Groups” info should be set as “visible”, in your profile options.) We are 63 this early Saturday morning —keeps growing.
  • If you have been using PayPal to fund your InTrade, TradeSports or BetFair account, please, check that horror story.
  • 48 hours after the launch of the “Prediction Markets” group at LinkedIn, we have already 52 members —both prediction market luminaries and simple people (trading the event derivatives or collecting the market-generated probabilities).

The Numbers Guy

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&#8230- says that HubDub&#8217-s PunditWatch is not as rigorous as Philip Tetlock was.

UPDATE: Interview + Video Response

Previous blog posts by Chris F. Masse:

  • The CFTC is going to close the comments in 9 days. We have 9 days left to convince the CFTC to accept FOR-PROFIT prediction exchanges (e.g., InTrade USA or BetFair USA), and counter the puritan and sterile petition organized by the American Enterprise Institute (which has on its payroll Paul Wolfowitz, the bright masterminder of the Iraq war).
  • Forrest Nelson valids Emile Servan-Schreiber.
  • Averaging One’s Guesses
  • Americans love rankings, but Americans hate to be assessed subjectively.
  • A libertarian view on the Internet betting and gambling industry in the United States of America
  • The CFTC is going to close the comments in 10 days. We have 10 days left to convince the CFTC to accept FOR-PROFIT prediction exchanges (e.g., InTrade USA or BetFair USA), and counter the puritan and sterile petition organized by the American Enterprise Institute (which has on its payroll Paul Wolfowitz, the bright masterminder of the Iraq war).
  • The CFTC Readings Of The Day —Thursday Morning Edition

VIDEO – Bo Cowgill on Googles enterprise prediction markets – OReilly Money:Tech

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Using Prediction Markets to Track Information Flows: Evidence from Google – (PDF file – PDF file) – by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz – 2008-01-06

Blip.TV

I already published this video. The reason I do it again is that I found out a hidden function in WordPress to increase the dimensions of the embedded video player. I think it is useful in this particular case because Bo shows us some slides, in this video. So, my hope is that those slides will be more readable that way. Let&#8217-s try that. I am pressing &#8220-publish&#8221-&#8230- let&#8217-s see.

Our previous blog post on the Google paper

Previous blog posts by Chris F. Masse:

  • The CFTC is going to close the comments in 9 days. We have 9 days left to convince the CFTC to accept FOR-PROFIT prediction exchanges (e.g., InTrade USA or BetFair USA), and counter the puritan and sterile petition organized by the American Enterprise Institute (which has on its payroll Paul Wolfowitz, the bright masterminder of the Iraq war).
  • Forrest Nelson valids Emile Servan-Schreiber.
  • Averaging One’s Guesses
  • Americans love rankings, but Americans hate to be assessed subjectively.
  • A libertarian view on the Internet betting and gambling industry in the United States of America
  • The CFTC is going to close the comments in 10 days. We have 10 days left to convince the CFTC to accept FOR-PROFIT prediction exchanges (e.g., InTrade USA or BetFair USA), and counter the puritan and sterile petition organized by the American Enterprise Institute (which has on its payroll Paul Wolfowitz, the bright masterminder of the Iraq war).
  • The Numbers Guy

EXPIRATIONS: Puerto Rico, South Dakota, and Montana

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Expired prediction markets

InTrade

Previous blog posts by Chris F. Masse:

  • IIF’s SIG on Prediction Markets
  • Science
  • Why did prediction markets do well in the pre-polling era, professor Strumpf?
  • Mozilla FireFox users, do you have trouble downloading academic papers (as PDF files) from SSRN?
  • “Impact Matrix. Used to collect and gauge the likelihood and business impact of various events in the very long term.”
  • Ends and Means of Prediction Markets — Tom W. Bell Edition
  • How to run enterprise prediction markets… legally

Using Prediction Markets to Track Information Flows: Evidence from Google – VIDEO – Bo Cowgill on Googles enterprise prediction markets – OReilly Money:Tech

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Using Prediction Markets to Track Information Flows: Evidence from Google – (PDF file – PDF file) – by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz – 2008-01-06

Via Daniel Horowitz (Business and Technology Consultant)

Blip.TV &#8212- (FLV file)

It&#8217-s cool. :-D

Google Web Search

Using Prediction Markets to Track Information Flows: Evidence from Google – (PDF file – PDF file) – by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz – 2008-01-06

ABSTRACT: In the last 2.5 years, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information. We document a number of biases in Google’s markets, most notably an optimistic bias. Newly hired employees are on the optimistic side of these markets, and optimistic biases are significantly more pronounced on days when Google stock is appreciating. We find strong correlations in trading for those who sit within a few feet of one another- social networks and work relationships also play a secondary explanatory role. The results are interesting in light of recent research on the role of optimism in entrepreneurial firms, as well as recent work on the importance of geographical and social proximity in explaining information flows in firms and markets.

DISCUSSION: In the past few years, many companies have experimented with prediction markets. In this paper, we analyze the largest such experiment we are aware of. We find that prices in Google’s markets closely approximated event probabilities, but did contain some biases, especially early in our sample. The most interesting of these was an optimism bias, which was more pronounced for subjects under the control of Google employees, such as would a project be completed on time or would a particular office be opened. Optimism was more present in the trading of newly hired employees, and was significantly more pronounced on and immediately following days with Google stock price appreciation. Our optimism results are interesting given the role that optimism is often thought to play in motivation and the success of entrepreneurial firms. They raise the possibility of a “stock price-optimism-performance-stock price” feedback that may be worthy of further investigation. We also examine how information and beliefs about prediction market topics move around an organization. We find a significant role for micro-geography. The trading of physically proximate employees is correlated, and only becomes correlated after the employees begin to sit near each other, suggesting a causal relationship. Work and social connections play a detectable but significantly smaller role.

An important caveat to our results is that they tell us about information flows about prediction market subjects, many of which are ancillary to employees’ main jobs. This may explain why physical proximity matters so much more than work relationships – if prediction market topics are lower-priority subjects on which to exchange information, then information exchange may require the opportunities for low-opportunity-cost communication created by physical proximity. Of course, introspection suggests that genuinely creative ideas often arise from such low-opportunity-cost communication. Google’s frequent office moves and emphasis on product innovation may provide an ideal testing ground in which to better understand the creative process.

PAPER BODY: In the last 4 years, many large firms have begun experimenting with internal prediction markets run among their employees. The primary goal of these markets is to generate predictions that efficiently aggregate many employees’ information and augment existing forecasting methods. […] In this paper, we argue that in addition to making predictions, internal prediction can provide insight into how organizations process information. Prediction markets provide employees with incentives for truthful revelation and can capture changes in opinion at a much higher frequency than surveys, allowing one to track how information moves around an organization and how it responds to external events. […]

We can draw two main conclusions. The first is that Google’s markets, while reasonably efficient, reveal some biases. During our study period, the internal markets overpriced securities tied to optimistic outcomes by 10 percentage points. The optimistic bias in Google’s markets was significantly greater on and following days when Google stock appreciated. Securities tied to extreme outcomes were underpriced by a smaller magnitude, and favorites were also overpriced slightly. These biases in prices were partly driven by the trading of newly hired employees- Google employees with longer tenure and more experience trading in the markets were better calibrated. Perhaps as a result, the pricing biases in Google’s markets declined over our sample period, suggesting that corporate prediction markets may perform better as collective experience increases.

The second conclusion is that opinions on specific topics are correlated among employees who are proximate in some sense. Physical proximity was the most important of the forms of proximity we studied. Physical proximity needed to be extremely close for it to matter. Using data on the precise latitude and longitude of employees’ offices, we found that prediction market positions were most correlated among employees sharing an office, that correlations declined with distance for employees on the same floor of a building, and that employees on different floors of the same building were no more correlated than employees in different cities.4 Google employees moved offices extremely frequently during our sample period (in the US, approximately once every 90 days), and we are able to use these office moves to show that our results are not simply the result of like-minded individuals being seated together. […]

Our findings contribute to three quite different literatures: on the role of optimism in entrepreneurial firms, on employee communication in organizations, and on social networks and information flows among investors. […]

The lessons of the literature informed Google CEO Eric Schmidt and Chief Economist Hal Varian’s (2005) third rule for managing knowledge workers: “Pack Them In.” Indeed, the fact that Google employees moved so frequently during our sample period suggests that considerable thought is put into optimizing physical locations. To this literature, which has largely relied on retrospective surveys to track communication, we illustrate how prediction markets can be used as high-frequency, market-incentivized surveys to track information flows in real-time. […]

Google’s prediction markets were launched in April 2005. The [Google prediction] markets are patterned on the Iowa Electronic Markets (Berg, et. al., 2001). In Google’s terminology, a market asks a question (e.g., “how many users will Gmail have?”) that has 2-5 possible mutually exclusive and completely exhaustive answers (e.g., “Fewer than X users”, “Between X and Y”, and “More than Y”). Each answer corresponds to a security that is worth a unit of currency (called a “Gooble”) if the answer turns out to be correct (and zero otherwise). Trade is conducted via a continuous double auction in each security. As on the IEM, short selling is not allowed– traders can instead exchange a Gooble for a complete set of securities and then sell the ones they choose. Likewise, they can exchange complete set of securities for currency. There is no automated market maker, but several employees did create robotic traders that sometimes played this role.

Each calendar quarter from 2005Q2 to 2007Q3 about 25-30 different markets were created. Participants received a fresh endowment of Goobles which they could invest in securities. The markets’ questions were designed so that they could all be resolved by the end of the quarter. At the end of the quarter, Goobles were converted into raffle tickets and prizes were raffled off. The prize budget was $10,000 per quarter, or about $25-100 per active trader (depending on the number active in a particular quarter). Participation was open to active employees and some contractors and vendors– out of 6,425 employees who had a prediction market account, 1,463 placed at least one trade. […]

Common types of markets included those forecasting demand (e.g., the number of users for a product) and internal performance (e.g., a product’s quality rating, whether a product would leave beta on time). […]

In addition, about 30 percent of Google’s markets were so-called “fun” markets –markets on subjects of interest to its employees but with no clear connection to its business (e.g., the quality of Star Wars Episode III, gas prices, the federal funds rate). Other firms experimenting with prediction markets that we are aware of have avoided these markets, perhaps out of fear of appearing unserious. Interestingly, we find that volume in “fun” and “serious” markets are positively correlated (at the daily, weekly, and monthly frequencies), suggesting that the former might help create, rather than crowd out, liquidity for the latter. […]

Google’s prediction markets are reasonably efficient, but did exhibit four specific biases: an overpricing of favorites, short aversion, optimism, and an underpricing of extreme outcomes. New employees and inexperienced traders appear to suffer more from these biases, and as market participants gained experience over the course of our sample period, the biases become less pronounced. […]

FOOT NOTE: One trader in Google’s markets wrote a trading robot that was extremely prolific and ended up participating in about half of all trades. Many of these trades exploited arbitrage opportunities available from simultaneously selling all securities in a bundle. In order to avoid having this trader dominate the (trade-weighted) results in Table 9, we include a dummy variable to control for him or her. None of the results discussed in the above paragraph are sensitive to removing this dummy variable.

APPENDIX:

Google Chart 1

Google Chart 2

Bo Cowgill&#8217-s precision point on micro-geography:

Below you can see a snapshot of trading in one of our offices. The areas where employees are making profitable decisions is green, and the areas where employees are making unprofitable decisions is red. There are about 16 profitable traders in that big green blotch in the middle!

Chart of the Day: Information Sharing at Google

More information from our previous blog post on the Google paper

Using Prediction Markets to Track Information Flows: Evidence from Google – (PDF file – PDF file) – by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz – 2008-01-06

Hillary Clinton won West Virginia. – Nobody cares but her.

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Previous blog posts by Chris F. Masse:

  • Prediction Markets
  • Meet professor Justin Wolfers.
  • Become “friend” with me on Google E-Mail so as to share feed items with me within Google Reader.
  • Nigel Eccles’ flawed “vision” about HubDub shows that he hasn’t any.
  • How does InTrade deal with insider trading?
  • Modern Life
  • “The Beacon” is an excellent blog published by The Independent Institute.

Collecting bits and pieces of information, and aggregating it, so we can understand what people know.

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Charles Plott has nailed it.

I would lay out this dichotomy:

  • Some of our academics, consultants, and exchange executives have sold the prediction markets as the ultimate forecasting tool &#8212-which is true, but people translated that as &#8220-this is an omniscient tool for forecasting&#8221-
  • The best usage of the prediction markets is that they do average what the experts think (see Justin Wolfers&#8217- mention of a &#8220-useful counterweight&#8221-) &#8212-but that&#8217-s a far cry from being an omniscient oracle (which is what people are expecting).

Some people would enjoy the usage of a &#8220-useful counterweight&#8221- &#8212-but not that many.

The &#8220-useful counterweight&#8221- thing is not a hot-selling proposition.

You don&#8217-t draw crowds with that.

You draw crowds with an over-selling proposition.

You draw crowds by manufacturing hype.

As a result of the collective intelligence of more than 77,000 bettors on Intrade, the prices on the site may be a good way to predict the outcome of current events &#8212- more accurate than some polls and pundits. In 2004, the market odds on Intrade predicted the presidential vote of every state but Alaska. In 2006, the odds correctly indicated the outcome of every Senate race.

Our Explainer On Prediction Markets

Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that traders bring when they agree on prices. Prediction markets are meta forecasting tools that feed on the advanced indicators (i.e., the primary sources of information). Garbage in, garbage out&#8230- Intelligence in, intelligence out&#8230-

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 6 times out of 10, the favored outcome will occur- and 4 times out of 10, the unfavored outcome will occur.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

Previous blog posts by Chris F. Masse:

  • Become “friend” with me on Google E-Mail so as to share feed items with me within Google Reader.
  • Nigel Eccles’ flawed “vision” about HubDub shows that he hasn’t any.
  • How does InTrade deal with insider trading?
  • Modern Life
  • “The Beacon” is an excellent blog published by The Independent Institute.
  • The John Edwards Non-Affair… is making Memeorandum (twice), again.
  • Prediction Markets = marketplaces for information trading… and for separating the wheat from the chaff.

ABC 20/20 featuring InTrade on May 9, 2008 – 10:00 pm ET

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ABC 20/20 featuring InTrade on May 9, 2008 &#8212- 10:00 pm ET

Foretelling the Future: Online Prediction Markets &#8212- (4 pages in all)

Now Intrade is more than just a place where people win or lose money making bets. It turns out that the share prices on Intrade can be accurate predictors of the future. Intrade attracts a large and diverse crowd of bettors, and because each participant puts their money on the line, they may be more likely to make careful decisions. As a result of the collective intelligence of more than 77,000 bettors on Intrade [*], the prices on the site may be a good way to predict the outcome of current events &#8212- more accurate than some polls and pundits.

[*] and thanks to InTrade&#8217-s market mechanism&#8230- :-D

UPDATE:

ABC video

YouTube video

Previous blog posts by Chris F. Masse:

  • Become “friend” with me on Google E-Mail so as to share feed items with me within Google Reader.
  • Nigel Eccles’ flawed “vision” about HubDub shows that he hasn’t any.
  • How does InTrade deal with insider trading?
  • Modern Life
  • “The Beacon” is an excellent blog published by The Independent Institute.
  • The John Edwards Non-Affair… is making Memeorandum (twice), again.
  • Prediction Markets = marketplaces for information trading… and for separating the wheat from the chaff.

Final InTrade v. Zogby Showdown Results

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Superdelegate chicanery notwithstanding, Obama has won his party&#8217-s nomination, so my head-to-head contest between a major pollster (Zogby) and major prediction market (Intrade) will be coming to a close.

Unsurprising to those who know a little bit about the scholarship, economics, and/or track record of prediction markets, the traders of Intrade provided us much better data this election season than the respondents to the Zogby polls.

Standings
Wins Losses Ties Pct Contender Avg Eve Prob
7 3 11 59.5% Intrade 71.3%
3 7 11 40.5% Zogby 40.7%

Schedule
Score Date State Party Intrade Zogby Winner Intrade Pct Zogby Pct
7-3-11 6-May IN Dem Clinton 2-way-tie Clinton 85% 42%
6-3-11 6-May NC Dem Obama Obama Obama 90% 50%
6-3-10 22-Apr PA Dem Clinton Clinton Clinton 82% 47%
6-3-9 4-Mar OH Dem Clinton 2-way-tie Clinton 70% 45%
5-3-9 4-Mar TX Dem Obama 2-way-tie Clinton 57% 44%
5-2-9 5-Feb NJ Rep McCain McCain McCain 96% 52%
5-2-8 5-Feb NJ Dem Clinton 2-way-tie Clinton 67% 43%
4-2-8 5-Feb NY Rep McCain McCain McCain 98% 53%
4-2-7 5-Feb GA Dem Obama Obama Obama 96% 48%
4-2-6 5-Feb MO Dem Obama Obama Obama 63% 47%
4-2-5 5-Feb CA Rep McCain Romney McCain 56% 40%
3-2-5 5-Feb CA Dem Obama Obama Clinton 52% 46%
3-2-4 29-Jan FL Rep McCain 2-way-tie McCain 51% 33%
2-2-4 26-Jan SC Dem Obama Obama Obama 90% 38%
2-2-3 19-Jan SC Rep McCain McCain McCain 56% 29%
2-2-2 19-Jan NV Dem Obama Clinton Clinton 54% 42%
2-1-2 15-Jan MI Rep McCain 2-way tie Romney 54% 27%
2-0-2 8-Jan NH Dem Obama Obama Clinton 91% 39%
2-0-1 8-Jan NH Rep McCain McCain McCain 82% 34%
2-0-0 3-Jan IA Dem Obama 3-way tie Obama 54% 28%
1-0-0 3-Jan IA Rep Huckabee 2-way tie Huckabee 53% 28%

Lord willing, I will have another showdown season, but will most likely choose another pollster. Whereas Intrade listed contracts and probabilities for every state primary, Zogby only provided election eve updates for only 21 of the 87 held to date. Rasmussen stands out as a worthy pollster, but I would be happy with any candidate referrals from my readers as well.

Cross-posted from Caveat Bettor.

Previous blog posts by Caveat Bettor:

  • Land-Ocean year-to-date temperatures 0.35 Celsius over baseline
  • Intrade lists Global Warming Contracts!
  • Intrade beats Zogby on Super Tuesday
  • Super Tuesday Showdown: Intrade v. Zogby
  • The Democrat SC Showdown: Intrade v. Zogby
  • Zogby beats Intrade in predicting Nevada caucus winner Clinton.
  • The GOP SC and Dem NV Showdown: Intrade v. Zogby

Barack Obama will finish off Hillary Clinton by June 15.

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The End

Lawrence O&#8217-Donnell (a leftist journalist &#8211-but a good one, whom I appreciate):

A senior campaign official and Clinton confidante has told me that there will be a Democratic nominee by June 15. […] Yes, Clinton spokespersons publicly seem to be lost on gravity-free planet Clinton, but privately they know the end is near.

There&#8217-s a quasi consensus among the political pundits to say that Hillary Clinton will not be the Democratic nominee in November 2008.

Tim Russert:

That was Wednesday night. I have just watched NBC Nightly News this Thursday, and the same Tim Russert appeared with 2 white boards full of calculations, which all pointed to Hillary Clinton being toasted.

My general thoughts about the place of the political prediction markets in this primary election season:

  1. The weight of the political prediction markets in the US political scenery is close to epsilon. I have been monitoring Memeorandum (the Web&#8217-s best political news and opinion aggregator), and it has never featured one piece of political prediction market journalism &#8212-not only that, but none of the popular popular pieces, featured by Memeorandum, has ever mentioned the political prediction markets and their probabilities. The people who breathe politics on a daily basis (the experts and the bloggers) don&#8217-t give the first fig about the prediction markets. They couldn&#8217-t care less.
  2. The prediction market luminaries who predicted that the prediction markets were to become a tool used in political campaigns were dead wrong. I have never read that campaign staffers use actively the political prediction markets. Campaigns use private polls, only.
  3. Like in 2004 (when Howard Dean was crowned, early on), the prediction markets, at the start of the primary season, were incapable of foreseeing who would be each party&#8217-s nominee, ultimately &#8212-Barack Obama and John McCain both came out of the blue. But the polls and the political experts didn&#8217-t see them, too.
  4. Nothing surprising in that. While the idiots emphasize the supposed magical power of the prediction markets (using adjectives such as &#8220-fascinating&#8221- or &#8220-intriguing&#8221- when writing about them), the well informed people know for a fact that they simply aggregate information from the primary, advanced indicators and the opinions expressed by the political experts. Nothing more than that. The prediction markets are incapable of foretelling upsets, by essence.
  5. The last weeks were particularly interesting, in that regard, because the Obama-vs-Clinton polls have been of no interest &#8212-only the views of the political experts who could count in terms of delegates and super-delegates were of interest. The political prediction markets on the Democratic side, these last weeks, have been a reflection of the pundits&#8217- calculations.
  6. Outside of our blog, the only person who has aimed at practicing prediction market journalism is Justin Wolfers. He has understood the concept.
  7. I would have my own concept of prediction market journalism, and I don&#8217-t agree with the way he executes, but that&#8217-s a detail. The main thing is that he has gotten the concept. That&#8217-s what is important, and that&#8217-s what makes all the difference between Justin Wolfers and the HubDub bloggers (for instance). The concept. The concept. The concept. The idea is to center the narrative around the inputs given by the relevant prediction market(s) &#8212-not just gluing artificially news bits and a prediction market chart (or a link to a prediction market).
  8. InTrade, BetFair and NewsFutures are, in my view, the 3 prediction exchanges that matter for prediction market journalism &#8212-as of now.

Now, the charts of the expired prediction markets &#8212-starting with Pennsylvania (of 2 weeks ago):

Yesterday&#8217-s North Carolina:

Yesterday&#8217-s Indiana:

Sources: InTrade &amp- BetFair

(Go there for the remaining primaries and caucuses. I don&#8217-t put them on, here, because they don&#8217-t matter anymore.)

Now, the charts of the prediction markets, going forward:

2008 US Presidential Election Winner – Individual

Price for 2008 Presidential Election Winner (Individual) at intrade.com

Price for 2008 Presidential Election Winner (Individual) at intrade.com

Price for 2008 Presidential Election Winner (Individual) at intrade.com

2008 US Presidential Elections

Source: Dynamic, compound prediction market charts from InTrade

Next US President

Next US President

Winning Party

Winning Party

Female President?

Female President?

Democratic Candidate

Democratic Candidate

Republican Candidate

Republican Candidate

Source: BetFair Politics Zone

Barack Obama to win the Democratic nomination


© NewsFutures

Hillary Clinton to win the Democratic nomination


© NewsFutures

Next US President Will Be Democratic.


© NewsFutures

Next US President Will Be Republican.


© NewsFutures

Explainer On Prediction Markets

Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that traders bring when they agree on prices. Prediction markets are meta forecasting tools that feed on the advanced indicators (i.e., the primary sources of information). Garbage in, garbage out&#8230- Intelligence in, intelligence out&#8230-

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 6 times out of 10, the favored outcome will occur- and 4 times out of 10, the unfavored outcome will occur.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

More Info:

– The Best Resources On Prediction Markets = The Best External Web Links + The Best Midas Oracle Posts

– Prediction Market Science

– The Midas Oracle Explainers On Prediction Markets

– All The Midas Oracle Explainers On Prediction Markets

Previous blog posts by Chris F. Masse:

  • Prediction Markets
  • Meet professor Justin Wolfers.
  • Become “friend” with me on Google E-Mail so as to share feed items with me within Google Reader.
  • Nigel Eccles’ flawed “vision” about HubDub shows that he hasn’t any.
  • How does InTrade deal with insider trading?
  • Modern Life
  • “The Beacon” is an excellent blog published by The Independent Institute.