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IN NOVEMBER '07 I VISITED DUBAI to speak at a global strategy conference for McKinsey, its senior strategy partners and strategists at client firms. I was on a panel with Todd Henderson at Chicago Law, Jeff Severts at Best Buy and James Surokweicki of the New Yorker. The transcript of the talk appears in this month's McKinsey Quarterly -- including this amusing sketch of me:
I'm better looking in real life, promise ;). Anyhow, the panel was moderated by McKinsey guru Renee Dye (author of these articles) -- an excellent host who posed thoughtful questions and drew out each panelists' distinct strengths. Jed Christiansen has a review of the article in case you're not able to see it.
I enjoyed spending time with my fellow panelists, and the rest crowd was fantastic. I'm quite thankful to McKinsey and Renee for the opportunity, and hope the article contributes substantively to the growing conversation about using markets inside of organizations.
UPDATE: How did the artist do? See the original pic here.
AFTERMATH: In 2001, Ruler of Dubai Sheikh Mohammed bin Rashid bought Jonabell Horse farms in Lexington, KY from family friends. This gave me some local contacts to show me around the area after the conference. Many thanks to Ajay and gang for a great experience. For travellers to the area, be sure you visit Khasab, Oman.
WHOA, LIKE, I'M SO IMPRESSED: "When [new Facebook COO Sheryl Sandberg] was completing her senior thesis as an undergraduate at Harvard, she ran so much data through one of the school's computers that she crashed it."
Please.
THIS NEW YORK TIMES ARTICLE ON CORPORATE PREDICTION MARKETS doesn't mention our implementation at Google, but the accompanying graphic (below) represents what Justin Wolfers, Eric Zitzewitz and I did better than anything I've seen (including our own fairly cool graphic).
No company mentioned in the article (or anywhere else, to my knowledge) is actually doing what is depicted in the picture besides Google.
Notice that the manager of the market (above) can see much, much more than current prices and trading volume. He can see who bets how how and where they sit. He can compare how specific teams or social groups are betting and whether managers for a project are betting more optimistically than their employees.
And so on. The view is a lot more interesting when you're looking at the market like this instead of like this.
NPR HAS A VERY GOOD DESCRIPTION of why everyone liked Zach Morris from Saved by the Bell so much.
"EVEN IN A BORDER-FREE EUROPE, EVERYONE WANTS A HOMELAND": The Economist says that Europe has made peace with nationalism and notes: An essay in the current issue of Foreign Affairs makes the incendiary suggestion that the EU has kept the peace for 60 years thanks to nationalism, not despite it. The author, Jerry Muller of the Catholic University of America, argues that the brutal genocides and forced population shifts of the 20th century helped to make peace possible. With a few exceptions (he cites Belgium as one), Europe's ethnic and state boundaries now match (ie, most Germans live in Germany, Greece is dominated by Greeks and so on). That has removed a big reason for fighting. Thus the post-war peace may not mark a defeat for ethnic nationalism, but rather demonstrates its "success".
JUST FINISHED READING Dreams and Shadows: The Future of the Middle East by Robin Wright. Interesting portrait of the Middle East from early part of this decade to present. Great storytelling, but no big conceptual insights.
TNR HAS A COOL STORY about the struggle for control over the Wikipedia entries for Barack Obama and Hillary Clinton's Wikipedia pages. Great reporting from Eve Fairbanks.
WELL, YEAH: "If Barack gets past the primary," said the Rev. Jeremiah Wright to the New York Times in April of last year, "he might have to publicly distance himself from me. I said it to Barack personally, and he said yeah, that might have to happen."
Pause just for a moment, if only to admire the sheer calculating self-confidence of this. Sen. Obama has long known perfectly well, in other words, that he'd one day have to put some daylight between himself and a bigmouth Farrakhan fan. But he felt he needed his South Side Chicago "base" in the meantime. So he coldly decided to double-cross that bridge when he came to it.
And now we are all supposed to marvel at the silky success of the maneuver. --Christopher Hitchens, Slate.
ME TOO: "I liked TNR better when it was treating politicians as politicians, not boyband heartthrobs." -- TNR reader on the magazine's Obama crush.
For that matter, I liked it better when my generation treated politicians as politicians, and not as boyband heartthrobs.
On the other hand, this is pretty funny: "I'm angry," says black congressman Charlie Rangel, "I'm looking for the white people that are insulting me, and I can't find them."
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 PM 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 PM 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.
PM 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 PM 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 moreso than they identify with the 'wisdom of crowds' ethos. It is unclear how much companies really care about accurate forecasting anyway.
FOLLOWING UP ON MY PREVIOUS POST on Jed Christiansen's comments of our prediction market paper: 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. Our analysis benefited from a wealth of internal data (including GPS coordinates of offices) that other companies don't store.
You may be surprised at how much data average companies really have. For example, Google had social network surveys; many companies do not. However, many standard corporate applications (such as email, calendars, telephones and code reviews) contain implicit social networks that can be used in place of data gathered from surveys.
Or, consider this: I recently met with people from Google's real estate management group. Turns out, they have records of the floorplans of Google's offices in electronic format. Not only can someone use these records to find the distance between offices (without GPS coordinates) -- you can also find the total area and perimeter of each office, which desks are open (cube-style) vs. enclosed, the walking distances between offices and more.
Surprised and impressed, I asked if it was typical for companies to have all of this information. The response was: "Any Fortune 1000 company would have this data about their offices." Everyone in the room said his previous employer had the same data -- typically managed through computer-aided facility management systems such as Archibus or Infor.
SIGNS OF INTELLIGENT LIFE SPOTTED NEAR AL SHARPTON: "Sharpton says he backs the Illinois Senator, but believes it wiser not to formally endorse him."
I APPRECIATE JED CHRISTIANSEN'S SUMMARY of my paper with Eric Zitzewitz and Justin Wolfers. Go check it out. To me, the most interesting line is the following: The second half of the paper examined the transmission of information within Google based on the authors’ analysis of the traders and their behaviours. While there is some really interesting analysis there, it has more to do with organisational behaviour than being directly applicable to prediction markets[.] This is not the first reaction along these lines. I am perplexed by the response. I can understand why other companies may not want to replicate our analysis of information flows. Perhaps it wouldn't be worth the effort. Perhaps they would get identical results. And perhaps the company wouldn't have the all the necessary data.
However, I expected that people could easily see value in the analysis of granular trade-by-trade data -- especially if that data is joined with data about traders and outside events happening at the moment of the trades. We described one very generic application of this approach, but you can imagine much more actionable and company-specific ones.
I will mention one: The data contains real-time metrics on the distribution of knowledge and attitudes within a firm at a highly granular level. You can get metrics on for specific of the firm, for specific classes of employees and for specific topics. You can do this for either customers or employees, and have the metrics for any moment in time. The quality of these metrics will be extremely strong, because participants have been incentivized to reveal their true expectations.
Our analysis spoke in very general terms about the flow of information between Google employees -- we don't reference specific groups or draw distinctions between them -- which is where a lot of actionable data was. 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.
UPDATE: Our findings about the clustering of attitudes should also inform anyone who thinks that diversity is important for crowd-wisdom applications -- as James Surowiecki famously suggests in The Wisdom of Crowds.
Our analysis suggests that groupthink primarily happens within language networks and small physical spaces (with social/professional networks playing a secondary role, and demographic networks playing a non-existent one). Remember that as you're selecting your traders. If they already work/sit/chat together, the groupthink may already exist and the market won't cure it.
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Disclaimer: Opinions expressed on this site are the author's and not necessarily his employer's.
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