US scientists have invented a pill that can boost memory.

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Invented by Dr Gary Lynch from the University of California.

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John Delaney (CEO of InTrade) – (InTrade PDF file – CFTC PDF file):

Nearly all leading academics, not known for their attraction to unanimity, have publicly supported event markets. A great majority of these academics have been supplied with Intrade market data in the past, a service that Intrade intends to continue, for all study leads to an increase in transparency and understanding of event markets. It seems that the leading event market academics make no distinction between the benefits derived from academic owned markets like Iowa Electronic Markets and commercial market platforms like Intrade.

Yet many academics, with some notable exceptions, do temper their policy prescription to suggest a “safe harbor” for academic sites where research might be more generally available. As noted above Intrade has gladly supplied its event market data, typically free of charge to most of the leading prediction market academics and their students, and we are committed to encouraging the future study of event markets by continuing to supply our event market data free of charge or at very deep discounts. The academics that study event markets do a great service in developing our understanding of the strengths and weaknesses of event markets. Some commentators suggest that market liquidity and breadth typically benefit all event market stakeholders. Thus far commercial platforms like Intrade seem to be providing the greatest depth and breadth in event markets.

As Intrade has been a staunch supporter of event market academic study, and supplies greater depth and liquidity in its event markets than any other platform, it seems strange not to be a preferred purveyor. Perhaps the predominant reason many academics have held back from advocating and treating all event markets alike is a sense that initiatives to clarify or unwind the legislation restraining the optimal development of event markets is unlikely to be achievable. It seems many academics and commentators suggest a slow bureaucratic and pragmatic caution rather than focus on the optimal result. While the optimal result may be more challenging to achieve, for consistency, for better price discovery for the benefit of all, as well as for the development of Intrade, we encourage CFTC to apply common goals, objectives and standards for all participants.

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Prediction Market Industry Association – (PMIA)

4) Lobby for a clear legal and regulatory environment conducive to the productive adoption of prediction markets by individuals, firms, and governments, and ensuring free access to these markets by traders.

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The PMIA’s first board is comprised of:

[...]
Robin Hanson (George Mason University)
Justin Wolfers (Wharton School – University of Pennsylvania)

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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’ Web site.

That is a bet recorded at the LongBets foundation. 2,000 bucks are at stake. But two things have happened recently that may impact the outcome of this bet:

1. The New York Times has just opened his archives, and all the old stories are now free to read. That will prompt the bloggers to link to some interesting NYT oldies (as Jason Kottke has done already), which in turn will help these NYT stories to rank higher in Google Search results. That will also prompt the bloggers to link more to current stories from the New York Times, knowing that the dollar wall has disappeared.
2. The New York Times has just opened plenty of blogs (powered by market leader WordPress.org) under his umbrella site (NYtimes.com). The Freakonomics blog and the Paul Krugman blog are two examples of “weblogs”, as defined in the LongBets contract statement, that are hosted as sub-websites of the New York Times.

The two points above illustrate, as you’ve understood, the upcoming “convergence” or “fusion” between the so-called “mainstream media” and the new “citizen media”. Now, will this new trend in web publishing help the New York Times backers to win the LongBets bet? Jason Kottke, last year, investigated and said that the blogs are going to win. We will see.

Psstt… I don’t like that the bet lays on 5 news stories, only. The sample should have been made longer than that.

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ADDENDUM: Type “eLab eXchange” in Google Search, and our group blog on prediction markets (Midas Oracle) ranks higher than the New York Times story entirely devoted to publicize the eLab eXchange. Upon discovering that, Donna Hoffman’s face turned yellow, as you’ll see below.

HERE IS THE GOOGLE SEARCH SCREEN SHOT:

AND HERE’S DONNA HOFFMAN’S YELLOW MUGSHOT:

Donna Hoffman of the eLab eXchange and marketing professor at the University of California at Riverside

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Here’s another example where our group blog (Midas Oracle) ranks higher than the New York Times (more exactly, higher than the Freakonomics blog webhosted by the New York Times site):

Donna Hoffman of the eLab eXchange and marketing professor at the University of California at Riverside

Previous: Donna Hoffman of the eLab eXchange talks back to Robin Hanson.

Book Summary of Steve Levitt’s Freakonomics on YouTube – Part 2

Book Summary of Steve Levitt’s Freakonomics on YouTube – Part 3

+ YouTube – Steve Dubner’s Freakonomics lecture at the University of California State University at Fresno, California, State.

+ YouTube – Steven Levitt: the Freakonomics of inner-city gangs – TEDtalks

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Steve Levitt and Steve Dubner – Freakonomics: A Rogue Economist Explores the Hidden Side of Everything

Freakonomics website + Freakonomics blog

Student’s Guide to Freakonomics – PDF file

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Executive Summary

Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.

Introduction

Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election, the release date for new software, or the action taken by the Federal Reserve on short-term interest rates. A key benefit is that the market price of these contracts can potentially provide more accurate forecasts of future events than other methods. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. They also can help manage risk more efficiently. It is precisely because prediction markets have great potential that we think the government should facilitate rather than hinder the introduction of these markets.

There are significant regulatory barriers to establishing prediction markets in the United States, in part because they are potentially subject to gambling laws. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.

[...]

Conclusion

We believe prediction markets can significantly improve decision making in both the private and public sectors. One of the clear benefits of allowing small stakes, non-profit markets to operate would be the greater use of prediction markets to inform both public and private decision making. A second benefit would be that access to better information could promote greater transparency and accountability in decision making. A third benefit might be that other countries and regions would promote prediction markets with more sensible regulation. Finally, we think there would be benefits from the development of new knowledge on how to design prediction markets.

We are aware that Congress did not intend the CFTC to regulate gambling and we believe that it is important to design this safe harbor in such a fashion that socially valuable prediction markets can get in, but gambling markets cannot.

Prediction markets have great potential for improving economic welfare and the decisions of private and public institutions alike. To help achieve that potential, the regulatory impediments to the use of prediction markets in the U.S. should be lowered. Here, we have suggested one approach for reducing those regulatory barriers.

AEI-Brookings Joint Center – The views in this paper represent those of the authors and do not necessarily represent the views of the institutions with which they are affiliated.

Kenneth J. Arrow – Stanford University

Robert Forsythe – University of South Florida

Michael Gorham – Illinois Institute of Technology

Robert Hahn – AEI-Brookings Joint Center

Robin Hanson – George Mason University

Daniel Kahneman – Princeton University

John O. Ledyard – California Institute of Technology

Saul Levmore – University of Chicago

Robert Litan – AEI-Brookings Joint Center

Paul Milgrom – Stanford University

Forrest D. Nelson – University of Iowa

George R. Neumann – University of Iowa

Charles R. Plott – California Institute of Technology

Thomas C. Schelling – University of Maryland

Robert J. Shiller – Yale University

Vernon L. Smith – George Mason University

Erik Snowberg – Stanford University

Cass R. Sunstein – University of Chicago

Paul C. Tetlock – University of Texas at Austin

Philip E. Tetlock – University of California at Berkeley

Hal R. Varian – University of California at Berkeley

Marco Ottaviani – London Business School

Justin Wolfers – University of Pennsylvania

Eric Zitzewitz – Stanford University

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Previous: Statement on Prediction Markets – by Robert Hahn – 2007-05-07

WORLD-WIDE WEB EXCLUSIVE. — PLEASE, CREDIT “MIDAS ORACLE” FOR THE SCOOP. — FOR IMMEDIATE RELEASE

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Christian Hellmers’ profile at LinkedIn:

Christian Hellmers
Director of US Business Development, Betfair
Greater San Diego Area

Current
* Director of US Business Development at Betfair Limited

Past
* Manager, International Retail Strategy at DivX Networks, Inc.
* Consultant at HP Authority
* Business Development Manager at Scour, Inc.

Education
* University of California, Los Angeles

Industry:
Internet

Specialties:
Entrepreneurship, gambling, Internet strategy, horse racing, internet community, passion

Experience

Director of US Business Development
Betfair Limited
(Privately Held; 501-1000 employees; Internet industry)
August 2004 – Present (2 years 10 months)

Manager, International Retail Strategy
DivX Networks, Inc.
(Internet industry)
October 2003 – January 2005 (1 year 4 months)

Consultant
HP Authority
(Internet industry)
April 2001 – October 2003 (2 years 7 months)

Business Development Manager
Scour, Inc.
(Privately Held; 51-200 employees; Internet industry)
May 1999 – April 2001 (2 years)

Education
University of California, Los Angeles
BS, CEE Engineering, 1995 – 1999
Activities and Societies: President IFC

Great. The competition is looming.

What sort of GDP growth do we typically see during a recession? It is easy enough to answer this question just by selecting those postwar quarters that the National Bureau of Economic Research (NBER) has determined were characterized by economic recession and summarizing the probability distribution of those quarters. A plot of this density, estimated using nonparametric kernel methods, is provided in the following figure; (figures here are similar to those in a paper I wrote with UC Riverside Professor Marcelle Chauvet, which appeared last year in Nonlinear Time Series Analysis of Business Cycles). The horizontal axis on this figure corresponds to a possible rate of GDP growth (quoted at an annual rate) for a given quarter, while the height of the curve on the vertical axis corresponds to the probability of observing GDP growth of that magnitude when the economy is in a recession. You can see from the graph that the quarters in which the NBER says that the U.S. was in a recession are often, though far from always, characterized by negative real GDP growth. Of the 45 quarters in which the NBER says the U.S. was in recession, 19 were actually characterized by at least some growth of real GDP.

One can also calculate, as in the blue curve below, the corresponding characterization of expansion quarters. Again, these usually show positive GDP growth, though 10 of the postwar quarters that are characterized by NBER as part of an expansion exhibited negative real GDP growth.

The observed data on GDP growth can be thought of as a mixture of these two distributions. Historically, about 20% of the postwar U.S. quarters are characterized as recession and 80% as expansion. If one multiplies the recession density in the first figure by 0.2, one arrives at the red curve in the figure below. Multiplying the expansion density (second figure above) by 0.8, one arrives at the blue curve in the figure below. If the two products (red and blue curves) are added together, the result is the overall density for GDP growth coming from the combined contribution of expansion and recession observations. This mixture is represented by the yellow curve in the figure below.

It is clear that if in a particular quarter one observes a very low value of GDP growth such as -6%, that suggests very strongly that the economy was in recession that quarter, because for such a value of GDP growth, the recession distribution (red curve)is the most important part of the mixture distribution (yellow curve). Likewise, a very high value such as +6% almost surely came from the contribution of expansions to the distribution. Intuitively, one would think that the ratio of the height of the recession contribution (the red curve) to the height of the mixture distribution (the yellow curve) corresponds to the probability that a quarter with that value of GDP growth would have been characterized by the NBER as being in a recession. Actually, this is not just intuitively sensible, it in fact turns out to be an exact application of Bayes’ Law. The height of the red curve measures the joint probability of observing GDP growth of a certain magnitude and the occurrence of a recession, whereas the height of the yellow curve measures the unconditional probability of observing the indicated level of GDP growth. The ratio between the two is therefore the conditional probability of a recession given an observed value of GDP growth. This ratio is plotted as the red curve in the figure below.

Such an inference strategy seems quite reasonable and robust, but unfortunately it is not particularly useful– for most of the values one would be interested in, the implication from Bayes’ Law is that it’s hard to say from just one quarter’s value for GDP growth what is going on. However, there is a second feature of recessions that is extremely useful to exploit– if the economy was in an expansion last quarter, there is a 95% chance it will continue to be in expansion this quarter, whereas if it was in a recession last quarter, there is a 75% chance the recession will persist this quarter. Thus suppose for example that we had observed -10% GDP growth last quarter, which would have convinced us that the economy was almost surely in a recession last quarter. Before we saw this quarter’s GDP number, we would have thought in that case that there’s a 0.75 probability of the recession continuing into the current quarter. In this situation, to use Bayes’ Law to form an inference about the current quarter given both the current and previous quarters’ GDP, we would weight the mixtures not by 0.2 and 0.8 (the unconditional probabilities of this quarter being in recession and expansion, respectively), but rather by magnitudes closer to 0.75 and 0.25 (the probabilities of being in recession this period conditional on being in recession the previous period). The ratio of the height of the resulting new red curve to the resulting new yellow curve could then be used to calculate the conditional probability of a recession in quarter t based on observations of the values of GDP for both quarters t and t – 1. Starting from a position of complete ignorance at the start of the sample, we could apply this method sequentially to each observation to form a guess about whether the economy was in a recession at each date given not just that quarter’s GDP growth, but all the data observed up to that point.

Once can also use the same principle, which again is nothing more than Bayes’ Law, working backwards in time– if this quarter we see GDP growth of -6%, that means we’re very likely in a recession this quarter, and given the persistence of recessions, that raises the likelihood that a recession actually began the period before. The farther back one looks in time, the better inference one can arrive at. Seeing this quarter’s GDP numbers helps me make a much better guess about whether the economy might have been in recession the previous quarter. We then work through the data iteratively in both directions– start with a state of complete ignorance about the sample, work through each date to form an inference about the current quarter given all the data up to that date, and then use the final value to work backwards to form an inference about each quarter based on GDP for the entire sample.

All this has been described here as if we took the properties of recessions and expansions as determined by the NBER as given. However, another thing one can do with this approach is to calculate the probability law for observed GDP growth itself, not conditioning at all on the NBER dates. Once we’ve done that calculation, we could infer the parameters such as how long recessions usually last and how severe they are in terms of GDP growth directly from GDP data alone, using the principle of maximum likelihood estimation. It is interesting that when we do this, we arrive at estimates of the parameters that are in fact very similar to the ones obtained using the NBER dates directly.

What’s the point of this, if all we do is use GDP to deduce what the NBER is eventually going to tell us anyway? The issue is that the NBER typically does not make its announcements until long after the fact. For example, the most recent release from the NBER Business Cycle Dating Committee was announced to the public in July 2003. Unfortunately, what the NBER announced in July 2003 was that the recession had actually ended in November 2001– they are telling us the situation 1-1/2 years after it has happened.

Waiting so long to make an announcement certainly has some benefits, allowing time for data to be revised and accumulating enough ex-post data to make the inference sufficiently accurate. However, my research with the algorithm sketched above suggests that it really performs quite satisfactorily if we just wait for one quarter’s worth of additional data. Thus, for example, with the advance 2007:Q1 GDP data just released, we form an inference about whether a recession might have started in 2006:Q4. The graph below shows how well this one-quarter-delayed inference would have performed historically. Shaded areas denote the dates of NBER recessions, which were not used in any way in constructing the index. Note moreover that this series is entirely real-time in construction– the value for any date is always based solely on information as it was reported in the advance GDP estimates available one quarter after the indicated date.

Although the sluggish GDP growth rates of the past year have produced quite an obvious move up the recession probability index, it is still far from the point at which we would conclude that a recession has likely started. At Econbrowser we will be following the procedure recommended in the research paper mentioned above– we will not declare that a recession has begun until the probability rises above 2/3. Once it begins, we will not declare it over until the probability falls back below 1/3.

So yes, the ongoing sluggish GDP growth has come to a point where we would worry about it, but no, it’s not at the point yet where we would say that a recession has likely begun.

[James Hamilton is professor of economics at the University of California, San Diego. The above is cross-posted from Econbrowser].

Gambling: Design and Implementation 1
- “An Examination of the Determinants of Biased Behaviour in a Market for State Contingent Claims” Professor Johnnie Johnson, Southampton University Professor Alistair Bruce, University of Nottingham Jiejun Yu, University of Birmingham
- “Non-Expected Utility Models and Heterogeneity in Risk Attitudes: Towards an Explanation of Gambling Outcomes for Individuals and Markets” Professor David Peel, Lancaster University
- “Evidence of a Weekend Effect in a Market for State Contingent Claims” Professor Johnnie Johnson, Southampton University Dr. Ming-Chien Sung, Southampton University
Gambling: Design and Implementation 2
- “An Examination of the Economic Behavior In Buying Lottery Tickets” Professor Drew B. Winters, Texas Tech University
- “Why Score Probability Forecasts Categorically? Empirical Evidence of Increased Betting” Professor David Johnstone, University of Sydney Professor David Grant, University of Sydney
“Optimal Betting Strategies for Simultaneous Games” Professor Andrew Grant, University of Sydney
Gambling: Design and Implementation 3
- “Are Political Markets Really Superior to Polls as Election Predictors?” Professor Bob Erikson, Columbia University
- “Under Information Uncertainty” Professor Raphael Markellos, Athens University of Economics and Business
- “Betting on Yourself … and Losing: The Economics of Weight Loss Bets” Professor John Lynham, University of California, Santa Barbara
Gambling and Public Policy 1
- “Gambling with Public Monies: Would a Prediction Market Improve the Performance of the SBIR Program?” Professor Albert N. Link, University of North Carolina at Greensboro Professor John T. Scott, Dartmouth College
- “The Lure of Gambling: Wellspring of Revenue or a Faustian Bargain” Professor Richard McGowan, Boston College
- “Competition and Monopoly in the Gambling: Market-A Theoretical Approach” Professor Herbert Walther, Vienna University of Economics and Business Administration
Gambling and Public Policy 2
- “Different Country, Different Millennium, Different Technology. Same Subsidy. Same Result?” Professor Ramon DeGennaro, University of Tennessee
- “What Does a Concept Attract? The Case of Gaming in Macau” Professor Raymond So, Chinese University of Hong Kong
- “An Economic Analysis of Problem Gambling, The Gambler’s Fallacy, and the Taxation of Gambling” Professor John Pierson, University of Kent
Gambling and Public Policy 3
- “Is Casino Gaming a Productive Sector? A Conceptual and Cross-Jurisdiction Analysis” Professor Ricardo Siu, University of Macau
- “Productivity Measurement in Gambling: Plant-level Evidence from the United Kingdom” Professor David Paton, University of Nottingham Professor Donald Siegel, University of California at Riverside Professor Leighton Vaughan Williams, Nottingham Trent University
- “The Impact of Casino Gambling on Housing Markets: A Hedonic Approach” Professor Mike Wenz, Winona State University
Casinos and Economic Growth
- “The Casino Effect: Do Casinos Spur Growth and Which Communities Benefit?” Professor Chad D. Cotti, University of South Carolina
- “The Effect of Legalized Gambling on State Tax Revenue” Professor Douglas Walker, Georgia College
- “Spatial Characteristics of Gambling Expansion – An Analysis of Cross Border Effects” Professor Shannon Neibergs, Washington State University
Horse Racing
- “Inferring Risk Preferences Using Synthetic Win Bets in Horse Betting ‘Exotic’ Markets” Professor Philip O’Connor, University of Waikato
- “The Parimutuel Kelly Probability Scoring Rule” Professor David Johnstone, University of Sydney
- “Efficiency and Arbitrage Across Parimutuel Pools” Professor Marshall Gramm, Rhodes College
Prediction Markets and Public Policy 1
- “Hitting Home Runs and the Art of Corporate Valuation: Do Managers or the Prediction Market Make Better Predictions?” Professor Orly Sade, New York University and Hebrew University
- “Spread Betting as a Self-Enforcing Implicit Cartel” Professor Schnytzer, Adi, Bar Ilan University Professor Avichai Snir, Bar Ilan University
- “Prediction Markets as a Tool for Management of Political Risk” Professor Ole Jakob Bergfjord, Norwegian School of Economics
Prediction Markets and Public Policy 2
- “The Growth of Gambling and Prediction Markets: Economic and Financial Implications” Professor Alexander Koch, Royal Holloway College-University of London
- “Public Signal Bias and Prediction Market Accuracy” Professor Tom Gruca, University of Iowa
- “Ignorance Prior Bias in Prediction Markets” Professor Lionel Page, University of Westminster
Sports Betting 1
- “Ambiguity, Rules, and Asset Prices: The College Football Wagering Market” Professor Ron Mau, Western Carolina University
- “Does Sportsbook.com Set Pointspreads to Maximize Profits? Tests of the Levitt Model of Sportsbook Behavior” Professor Rodney J. Paul, St. Bonaventure University Andrew Weinbach
- “Market Efficiency and Internet Betting: Evidence from European Football” Professor Bruno Deschamps, University of Bath
Sports Betting 2
- “The Economics of the Setting of Bookmakers’ Odds” Professor John Pierson, University of Kent
- “An Analysis of Tradesports’ 2005-06 National Football League Prediction Market” Professor Feng Zhou, University of Waikato TBA Professor William H. Dare, Oklahoma State University
Sports Betting 3
- “Testing the Efficiency of Markets in the 2002 World Cup” Professor Steve Levitt, University of Chicago Professor Ricard Gil, University of California at Santa Cruz
- “The Impact of Sentiment on Point Spreads in the College Football Wagering Market” Professor Greg Durham, Montana State University TBA Professor Victor Matheson, College of the Holy Cross
Favorite-Longshot Bias
- “An Alternative Explanation of the Favorite–Longshot Bias” Professor Stefan Winter, Ruhr-Universität Bochum
- “The Link between Information and the Favorite-Longshot Bias in Pari-mutuel Wagering Markets” Professor Andy Weinbach, Coastal Carolina University
- “Anyone for Tennis (Betting)?” Professor Ian McHale, Salford University
Gambling and Prediction Markets: Experimental Methods
- “Time-inconsistent Risk Preferences in a Laboratory Experiment” Professor Jeremy Ko, Penn State
- “The Effect of Prior Beliefs and Outcomes on Information Processing in an Investment Experiment” Professor Jeremy Ko, Penn State
- “Forecasting Volume of Transactions in a Betting Market: An Experiment” Professor Loreto Llorente, Universidad Pública de Navarra
Gambling and Prediction Markets: Cross Cultural and Sociological Perspectives
- “Price Formation in Sports Prediction Markets: A Cross-Cultural Study” Professor Stefan Luckner, University of Karlsruhe
- “Socially Embedded Prediction Markets” Dr. Yiling Chen, Yahoo! Research
- “Do More Opportunities Lead to More Gamblers? Explaining the Growth in Gambling in the U.S. from a Cultural Perspective” Professor Andrew Economopoulos, Ursinus College
Plenary Session
- “Manipulators Increase Information Market Accuracy” Professor Robin Hanson, George Mason University
- “Is There a Favorite-Longshot Bias in Election Prediction Markets?” Professor Justin Wolfers, University of Pennsylvania
- “Using Markets to Inform Policy: The Case of the Iraq War” Professor Eric Zitzewitz, Stanford University

Organizers: Donald Siegel, David Paton, Leighton Vaughan-Williams and William Ziemba

Very impressive.

Plenty of papers that I will read and excerpt ASAP.

Feel free to publish a review of a paper on Midas Oracle, if you wish.

Psstt… Don Imus was born in Riverside, California.

David Yu, formerly CTO and then COO of Betfair, has been appointed Chief Executive Officer for the company.

David was previously Vice President, Engineering for Alta Vista’s e-Commerce and International divisions. He has spent over 10 years working for internet-based businesses, and holds an MS Computer Science from Stanford University and a BS Computer Science and Electrical Engineering from the University of California at Berkeley.

In other organisational changes, Betfair’s Executive Chairman Sir Robert Horton and non-executive directors Nick Irens and Justin Dowley have resigned with immediate effect.

Tim Bunting has joined the Board, and takes over as Chairman.

Betfair co-founder Edward Wray said today: “We would like to thank Bob, Nick and Justin for their valuable contribution to Betfair, which continues to go from strength to strength. We’re delighted that David steps up to the leading role in the company. And we welcome Tim Bunting, one of the City’s leading and most experienced professionals, as Chairman of the Company.”

The company, which in December became the first ever to retain its title of Company of the Year at the CBI’s Growing Business Awards, now employs 650 people in offices in Hammersmith and Stevenage. Founded less than six years ago, it produced an operating profit in its last financial year of £20million on revenues of £109million, and remains on-course to hit its aggressive financial targets for this financial year-end in May.

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The two BetFair co-founders chose (and imposed) for CEO a man who had no business background. As you have seen in the Press release above, that appointment pissed off some insiders.

I’m linking Google and BetFair together, in that they are both actively managed by information technologists who were in the firm from day one (or almost) —Larry Page and Sergey Brin co-founded Google; David Yu was acquired by BetFair in January 2002, part of the Flutter deal.

Both firms (Google and BetFair) are leading global services and technology companies —and I would deny the “technology” label to eBay and the CME, two exchanges with network externalities.

On the Web, survival is in the hands of your technologists, first —your sales persons come as #2. At Google, product definitions is left to engineers, whose mission is to create superior products. As for BetFair, it’s clear to me, looking at all their sub-websites and associated services, that the information technologists rule this British prediction exchange.