Considering that he is the #2 researcher in our field, that he issued bold statements on the use of market-generated predictions by journalists (”-2020″-), that the WSJ is the premier business publication, then that’-s bad omen for prediction market journalism —-well, at least, for the version that he has put out. The feedback of the Blogosphere is clear: WE COULDN’-T CARE LESS.
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JW + WSJ
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The richer people and countries become, the happier they get.
Wow, that’-s what I call a discovery.
Freakonomics – #2 – #3
New York Times
CNBC video
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Previous blog posts by Chris F. Masse:
ProTrade
(Justin Wolfers was a ProTrade adviser at inception, but his name was later removed from their website.)
Previously: The Jock Exchange: Wall Street is about to launch a new way to trade professional athletes the way you trade stocks.
Previous blog posts by Chris F. Masse:
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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
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Bo Cowgill:
[…-] Trade-by-trade data can reveal characteristics of specific working groups: What they know, how they feel, how they process and share information and how all of that changes over time. I didn’-t try to put any of this in the paper because the conclusions would be sensitive, and I thought this application was pretty obvious to anybody who understood our methodology. […-]
Bo Cowgill:
I’-ve also heard that other companies would find it impossible to analyze the interaction between their market and the organization. Why? Lack of data. […-]
Bo Cowgill:
Some more remarks about applications that combine prediction markets and organizational data (org charts, social networks, seating locations). The obstacle to these applications is not a lack of data. Jed mentions privacy concerns —- and if he thinks this is a big obstacle then I’-d be interested in discussing his thoughts.
A bigger problem is that that current prediction market vendors and consultants cannot support these applications. At heart, these vendors are software engineers and salespeople at heart, not statisticians or data miners. They want to write one system that can support lots of clients. At conferences, one hears PM vendors complain about having to do “-customization”- work for clients.
This approach would not work for the applications I describe for two reasons:
- The inputs for different clients won’-t be the same. Each client’-s organizational data will likely take a different structure. This makes it difficult for prediction market vendors to architect a single system that can served many clients (yet another challenge with integrating markets with other corporate IT services).
- The outputs for different clients won’-t be the same. The business relevance and statistical power of each analysis will differ with each client’-s data.
Prediction market vendors may also need to familiarize themselves with the statistical learning methods necessary to fully utilize these rich datasets. So what’-s the solution? First, move to a software-and-consulting model. By ‘-consulting,’- I don’-t mean ‘-consulting on how to implement the market.’- I’-m talking about helping the client solve its problem using a variety of data, including prediction market data.
Second, the vendors also need to pitch prediction markets as more than a forecasting tool. People in the business world commonly identify as data junkies —- probably more so than they identify with the ‘-wisdom of crowds’- ethos. It is unclear how much companies really care about accurate forecasting anyway.
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On a related note, there is something that only the prediction market software vendors could do, at this time, for those who are in capacity to do so: setting up inter-industry prediction markets —-or at least, handing over (with everybody’-s agreement) anonymized prediction market data on industry topics to anyone else in the industry who is a client of that PM firm. I don’-t know about NewsFutures or Inkling Markets, but if you look at Consensus Point’-s list of clients, you’-ll see that David Perry’-s firm is strong in the (consumer) electronic industry —-Motorola, Qualcomm, Siemens, Nokia. Use your imagination, or ask David Perry directly, for more…- (I can’-t talk- otherwise, next thing, I’-m a dead blogger.)…-
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Previous blog posts by Chris F. Masse:
I have just visited plenty of scholars‘- websites, and I noted that the 2 websites where I could download the most papers on prediction markets were:
– Robin Hanson
– Justin Wolfers
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Many (otherwise smart and friendly) researchers in our field don’-t understand how important it is to have a clean, comprehensive website, alas. Papers should be linked to and ready for download.
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Young researcher to monitor: Richard Borghesi, who is deeply into TradeSports. Interesting person, whose work doesn’-t get enough blog or media coverage, in my judgment.
Previous blog posts by Chris F. Masse:
That’-s what many libertarians think, right? But Nicholas Kristof has a piece in the New York Times that is well informed and balanced…- and that could change the mind of some libertarians…-
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“-Kristen”- (outed in today’-s NYT), photographed in a French port…- (Saint Tropez, it is said, but I’-m not so sure…- St-Trop, as I know it, is a fishermen’-s village…-)
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More on the Eliot Spitzer scandal from Slate…-
Inside the Emperors Club V.I.P.
Suspicious Activity Report filed by Mr. Spitzer’s bank
Eliot Spitzer’-s bio
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Zubin Jelveh highlights this piece of wisdom (which I didn’-t quote in my previous post):
It seems to me that the truly important violations of the public trust are when the power we give our government officials is sold, rather than what government officials choose to buy. Yet our political scandals are too often dominated by private mistakes, rather than public misdeeds. This is why I’-m more worried about what the SEC is selling than what Eliot Spitzer has been buying.
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Psstt…- CNBC will have a special Eliot Spitzer piece starting at 8:00 PM EST, tonite (Wednesday, March 12), I’-ve just heard.
Psstt…- And “-Page 6″- has the pic of that woman, that “-Kristen”-…-
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Room 871:
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Justin Wolfers:
If we judge politicians by what they buy, then Eliot Spitzer has clearly violated the public’s trust: he purchased the services of a high-priced prostitute, and may well end his political career as a result.
Other people think very differently.
More from Reason magazine.
Justin Wolfers investigated the Rudy Giuliani free-fall thru the prism of the InTrade prediction markets in the Wall Street Journal (PDF file).
All told, prediction market data tend to confirm that Mr. Giuliani’-s recent decline is due to a poor campaign, rather than a poor strategy.
I have just read the 3-page New York Times story on Rudy Giuliani’-s “-dizzying free-fall”- (who has not yet dropped out and endorsed John McCain, as I write this). And, as I understand the two NYT writers, they attribute his failure to both a strategic mistake and a poor campaign.
As of today, I am not yet convinced that Justin Wolfers saw something in the prediction markets that the political analysts didn’-t see in the polls and in the votes. Thus, I will find other Giuliani reports. I’-ll see. What is needed is an independent judgment on this.
Read the previous blog posts by Chris. F. Masse:
Yet.
That’-s key to see whether his flavor of prediction market journalism (heavily based on a single technique called prediction market event study) can take off. As I said in yesterday’-s blog post, even though his work is interesting, he is up against formidable competitors. Political analysts like Lawrence O’-Donnell, Eleanor Clift, Mort Zuckerman, Pat Buchanan, Radley Blako, etc. –-to name a few pundits (left and right)–- produce riveting content. Can Justin Wolfers, as a political analyst using a prediction market analysis tool, break through this competitive pack? As of today, Justin Wolfers’- journalistic work is artificially subsidized (so to speak), because the WSJ wants to launch and establish a play-money prediction sub-exchange. A step in the forward direction would be to see the big political blogs like InstaPundit or DailyKos stealing Justin Wolfers’- method, and boosting their pageviews thanks to that. We will see. I’-m not holding my breath.
The criteria that I would use to assess prediction market journalism are professional credibility, webpage popularity, the quantity and quality of the blog reactions, the amount of visitors sent to the discussed prediction market(s), and the number of journalists who ultimately embrace this method. See another criterion?
Read the previous blog posts by Chris. F. Masse: