Tag Archives: betting markets

Professor Thomas Rietz (Iowa Electronic Markets) was so wrong on the usefulness of prediction markets about the 2016 Summer Olympics in Chicago.

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Chicago Olympic Market Might Have Value, Says Reitz (Chicago Tribune, April 17)
A credible source of information about Chicago&#8217-s chances of hosting the 2016 Olympics would have value, says columnist Bill Barnhart. Local real estate developers, hotel operators, employment agencies, vendors of products and services to major events and others have a direct stake in whether or not an Olympics is staged here. Politicians and civic leaders presumably would want to know whether the city&#8217-s bid has a chance, so that they wouldn&#8217-t throw good money after bad. An auction market centered on whether Chicago will win could provide that information, even if there were no huge payoff for hedgers or speculators, said finance professor THOMAS RIETZ at the University of Iowa, a board member of the popular Iowa Electronic Markets. The Iowa market limits wagers to $500 but has an enviable track record in picking the winners of national elections. &#8220-Our goal is to aggregate information, which is a different goal than being able to hedge the economic risk associated with something like this,&#8221- Rietz said. &#8220-I don&#8217-t think it&#8217-s an outlandish idea.&#8221-

http://www.chicagotribune.com/business/yourmoney/chi-0704160447apr17,0,2547860.column?coll=chi-business-hed

Prof, you were 100% wrong.

Prediction markets on which country will host the Olympics have never worked.

– BetFair&#8217-s event derivative prices (on the far right of the chart, you can see that the price went down to zero):

chicago-olympics-betfair

– InTrade&#8217-s event derivative prices (on the far right of the chart, you can see that the price went down to zero):

chicago-olympics-intrade

– HubDub&#8217-s event derivative prices:

Who will recieve the winning bid to host the 2016 Olympics?

Do businesses need enterprise prediction markets?

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Competitive advantage can be obtained either by differentiation or by low cost. Enterprise prediction markets certainly don&#8217-t foster the innovation process, and they are surely not the cheapest forecasting tool. EPMs require special software, the hiring of consultant(s), the participation of all, and a budget for the prizes. EPMs are costly, and they take time to deliver. As of today, I can&#8217-t see why any sane CEO should be implementing EPMs as a decision-making support. At the contrary, I would say that any sane CEO should fire any employee who tried to sneak in internal prediction markets, and should dismember any existing corporate prediction exchange. Right now.

It has been suggested that EPMs have helped Best Buy getting it right on the ‘HD-DVD versus Blu-Ray’ issue. It&#8217-s a boatload of bullsh*t. I know a lot about technology intelligence. It should be done by a smart and curious operator. There is no need of enterprise prediction markets to do this task. The tools you need consist of a bunch of IT news aggregators and a good search engine. Consider this:

The Inevitable Move Of iTunes To The Cloud

In the &#8216-cloud&#8217- piece above, there are facts and there are speculations. You&#8217-ve got much more technology intelligence reading the &#8216-cloud&#8217- piece above than you would get from a crude, plain and simple prediction market. Gimme a break with EPMs. Make no sense at all.

Contrast EPMs (which are costly) with public prediction markets (a la InTrade or BetFair), where probabilistic predictions are offered for free. That makes all the difference for the reason that the added accuracy brought by prediction markets is very small. Market-generated odds are handed out for free to journalists &#8212-still, few of them take the bait. The market-powered crystal ball is worth peanuts.

The reason CEOs are paid millions is that only a small percent of the population of business administration managers has the ability to cut through the non-sense and the balls to cut the cost of the non-sense. It is a rare skill. I am calling on CEOs to end EPMs. Right now.

Can we really assess InTrades *very short* prediction market on Van Jones?

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Carlos Graterol has a partial analysis on the Van Jones prediction market at InTrade. Basically, Carlos Graterol (an InTrade fanboy) says that InTrade should be credited for the accurate prediction.

  1. Carlos Graterol should publish what the politicians and editorialists were saying last week (the resignation calls were numerous)-
  2. The InTrade prediction market should have been created much, much earlier &#8212-when Glenn Beck started his &#8216-anti-czars&#8217- guerrilla (at the beginning of August 2009).

vanjonespricehistory

intrade-van-jones-chart

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Prediction markets: Sticking to the letter of the contract VERSUS Interpreting intent

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Chris Hibbert (of Zocalo):

I disagree, Chris. Much experience on FX has shown that interesting questions (those that aren’t routine repetitions of previous questions) often result in realities that diverge from the obvious expectations of nearly everyone involved in describing the possibilities. In those situations, we’ve found that trying to interpret intent leads to more confusion than sticking to the letter of the question as asked.

If a [prediction market] sticks to its written description of what the claims mean, then careful readers are rewarded, and they learn that they have a good chance to predict how the judge will interpret the question and events in the world. If questions are determined based on “intent”, then everyone has to spend time deciding which aspect of the question the judge will decide was more important, when reality decides not to conform to the question’s expectations.

Sometimes (as you argue was the case with the North Korea question) the result is surprising and disappointing, but choosing the other approach leads to much less participation as people who see that something surprising is preparing to happen or has happened back out of their bets rather than waiting to find out what the judge decides is important. I’m much happier when the participants spend their time figuring out what will happen in the world, rather than when they have to spend their time predicting how the judge will react. Strict construction gives us a predictable world.

See also Jason Ruspini&#8217-s comment on the same topic&#8230-

CrowdCast = market mechanism = binary spreads with a market maker

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Leslie Fine (CrowdCast Chief Scientist) to me:

Actually, our mechanism is a market, it&#8217-s just not a stock market. We use an automated market maker to efficiently price every bet, adjust crowd beliefs, and price an interim sell. In essence, participants trade binary spreads with the market maker.

Because our new version was not yet market-ready, I did not enter the markets vs. non-markets debate when you were having it some months ago. However, among other reasons, we avoid collective forecasting because it is too similar to collaborative forecasting, which is key in supply chain. Honestly, when all is said and done, our clients care not what the mechanism is. They care that we can efficiently gather team intelligence and translate it into actionable business intelligence. That is our mission.

CrowdCast website

Previously: CrowdCast = Collective Forecasting = Collective Intelligence That Predicts

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Will Google *launch* its own Operating System (OS) before July 13, 2009?

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Sounds like Google did &#8220-announce&#8221- &#8212-as opposed to &#8220-launch&#8221-. Sense the nuance?

And so the HubDub prediction market will expire as a &#8220-NO&#8221- &#8212-as I understand it.

Robin Hanson: My best idea was prediction markets.

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Robin Hanson&#8216-s auto-biography (i.e., how Our Master Of All Universes views HimSelf):

robin-hanson-drink

–

Robin Hanson:

Do you find it hard to summarize yourself in a few words? Me too.

But I love the above quote. I have a passion, a sacred quest, to understand everything, and to save the world. I am addicted to a€?viewquakesa€?, insights which dramatically change my world view. I loved science fiction as a child, and have studied physics, philosophy, artificial intelligence, economics, and political science a€” all fields full of such insights. Unfortunately, this also tempted me to leave subjects after mastering their major insights.

I also have a rather critical style. I beat hard on new ideas, seek out critics, and then pledge my allegiance only to those still left standing. In conversation, I prefer to identify a claim at issue, and then focus on analyzing it, rather than the usual quick tours past hundreds of issues. I have always asked questions, even when I was very young.

I have little patience with those whose thinking is sloppy, small, or devoid of abstraction. And Ia€™m not a joiner– I rebel against groups with a€?our beliefsa€?, especially when members must keep criticisms private, so as not to give ammunition to a€?them.a€?A  I love to argue one on one, and common beliefs are not important for friendship a€” instead I value honesty and passion.

In a€?77 I began college (UCI) in engineering, but switched to physics to really understand the equations.A  Two years in, when physics repeated the same concepts with more math,A  I studied physics on my own, skipping the homework but acing the exams.A  To dig deeper, I did philosophy of science grad school (U Chicago), switched back to physics, and was then seduced to Silicon Valley.

By day I did artificial intelligence (Lockheed, NASA), and by night I studied on my own (Stanford) and hung with Xanadua€™s libertarian web pioneers and futurists.A  I had a hobby of institution design– my best idea was idea futures, now know as prediction markets. Feeling stuck without contacts and credentials, I went for a Ph.D. in social science (Caltech).

The physicist in me respected only econ experiments at first, but I was soon persuaded econ theory was full of insight, and did a theory thesis, and a bit of futurism on the side.A  I landed a health policy postdoc, where I was shocked to learn of medicinea€™s impotency.A  I finally landed a tenure-track job (GMU), and also found the wide-ranging intellectual conversations Ia€™d lacked since Xanadu.

My Policy Analysis Market project hit the press shit fan in a€?03, burying me in media attention for a while, and helping to kickstart the prediction market industry, which continues to grow and for which I continue to consult.A  The press flap also tipped me over the tenure edge in a€?05- my colleagues liked my being denounced by Senators. :) A  Tenure allowed me to maintain my diverse research agenda, and to start blogging at Overcoming Bias in November a€?06, about the same time I became a research associate at Oxforda€™s Future of Humanity Institute.

My more professional bio is here.

–

Robin Hanson is an associate professor of economics at George Mason University, and a research associate at the Future of Humanity Institute of Oxford University. After receiving his Ph.D. in social science from the California Institute of Technology in 1997, Robin was a Robert Wood Johnson Foundation health policy scholar at the University of California at Berkeley. In 1984, Robin received a masters in physics and a masters in the philosophy of science from the University of Chicago, and afterward spent nine years researching artificial intelligence, Bayesian statistics, and hypertext publishing at Lockheed, NASA, and independently.

Robin has over 70 publications, including articles in Applied Optics, Business Week, CATO Journal, Communications of the ACM, Economics Letters, Econometrica, Economics of Governance, Extropy, Forbes, Foundations of Physics, IEEE Intelligent Systems, Information Systems Frontiers, Innovations, International Joint Conference on Artificial Intelligence, Journal of Economic Behavior and Organization, Journal of Evolution and Technology, Journal of Law Economics and Policy, Journal of Political Philosophy, Journal of Prediction Markets, Journal of Public Economics, Medical Hypotheses, Proceedings of the Royal Society, Public Choice, Social Epistemology, Social Philosophy and Policy, Theory and Decision, and Wired.

Robin has pioneered prediction markets, also known as information markets or idea futures, since 1988. He was the first to write in detail about people creating and subsidizing markets in order to gain better estimates on those topics. Robin was a principal architect of the first internal corporate markets, at Xanadu in 1990, of the first web markets, the Foresight Exchange since 1994, and of DARPA&#8217-s Policy Analysis Market, from 2001 to 2003. Robin has developed new technologies for conditional, combinatorial, and intermediated trading, and has studied insider trading, manipulation, and other foul play. Robin has written and spoken widely on the application of idea futures to business and policy, being mentioned in over one hundred press articles on the subject, and advising many ventures, including Consensus Point, GuessNow, Newsfutures, Particle Financial, Prophet Street, Trilogy Advisors, XPree, YooNew, and undisclosable defense research projects.

Robin has diverse research interests, with papers on spatial product competition, health incentive contracts, group insurance, product bans, evolutionary psychology and bioethics of health care, voter information incentives, incentives to fake expertize, Bayesian classification, agreeing to disagree, self-deception in disagreement, probability elicitation, wiretaps, image reconstruction, the history of science prizes, reversible computation, the origin of life, the survival of humanity, very long term economic growth, growth given machine intelligence, and interstellar colonization.

Robin Hanson: My best idea was prediction markets.

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Robin Hanson‘s auto-biography (i.e., how Our Master Of All Universes views HimSelf):

robin-hanson-drink

–

Robin Hanson:

Do you find it hard to summarize yourself in a few words? Me too.

But I love the above quote. I have a passion, a sacred quest, to understand everything, and to save the world. I am addicted to “viewquakes”, insights which dramatically change my world view. I loved science fiction as a child, and have studied physics, philosophy, artificial intelligence, economics, and political science — all fields full of such insights. Unfortunately, this also tempted me to leave subjects after mastering their major insights.

I also have a rather critical style. I beat hard on new ideas, seek out critics, and then pledge my allegiance only to those still left standing. In conversation, I prefer to identify a claim at issue, and then focus on analyzing it, rather than the usual quick tours past hundreds of issues. I have always asked questions, even when I was very young.

I have little patience with those whose thinking is sloppy, small, or devoid of abstraction. And I’m not a joiner; I rebel against groups with “our beliefs”, especially when members must keep criticisms private, so as not to give ammunition to “them.”  I love to argue one on one, and common beliefs are not important for friendship — instead I value honesty and passion.

In ‘77 I began college (UCI) in engineering, but switched to physics to really understand the equations.  Two years in, when physics repeated the same concepts with more math,  I studied physics on my own, skipping the homework but acing the exams.  To dig deeper, I did philosophy of science grad school (U Chicago), switched back to physics, and was then seduced to Silicon Valley.

By day I did artificial intelligence (Lockheed, NASA), and by night I studied on my own (Stanford) and hung with Xanadu’s libertarian web pioneers and futurists.  I had a hobby of institution design; my best idea was idea futures, now know as prediction markets. Feeling stuck without contacts and credentials, I went for a Ph.D. in social science (Caltech).

The physicist in me respected only econ experiments at first, but I was soon persuaded econ theory was full of insight, and did a theory thesis, and a bit of futurism on the side.  I landed a health policy postdoc, where I was shocked to learn of medicine’s impotency.  I finally landed a tenure-track job (GMU), and also found the wide-ranging intellectual conversations I’d lacked since Xanadu.

My Policy Analysis Market project hit the press shit fan in ‘03, burying me in media attention for a while, and helping to kickstart the prediction market industry, which continues to grow and for which I continue to consult.  The press flap also tipped me over the tenure edge in ‘05; my colleagues liked my being denounced by Senators. :)   Tenure allowed me to maintain my diverse research agenda, and to start blogging at Overcoming Bias in November ‘06, about the same time I became a research associate at Oxford’s Future of Humanity Institute.

My more professional bio is here.

–

Robin Hanson is an associate professor of economics at George Mason University, and a research associate at the Future of Humanity Institute of Oxford University. After receiving his Ph.D. in social science from the California Institute of Technology in 1997, Robin was a Robert Wood Johnson Foundation health policy scholar at the University of California at Berkeley. In 1984, Robin received a masters in physics and a masters in the philosophy of science from the University of Chicago, and afterward spent nine years researching artificial intelligence, Bayesian statistics, and hypertext publishing at Lockheed, NASA, and independently.

Robin has over 70 publications, including articles in Applied Optics, Business Week, CATO Journal, Communications of the ACM, Economics Letters, Econometrica, Economics of Governance, Extropy, Forbes, Foundations of Physics, IEEE Intelligent Systems, Information Systems Frontiers, Innovations, International Joint Conference on Artificial Intelligence, Journal of Economic Behavior and Organization, Journal of Evolution and Technology, Journal of Law Economics and Policy, Journal of Political Philosophy, Journal of Prediction Markets, Journal of Public Economics, Medical Hypotheses, Proceedings of the Royal Society, Public Choice, Social Epistemology, Social Philosophy and Policy, Theory and Decision, and Wired.

Robin has pioneered prediction markets, also known as information markets or idea futures, since 1988. He was the first to write in detail about people creating and subsidizing markets in order to gain better estimates on those topics. Robin was a principal architect of the first internal corporate markets, at Xanadu in 1990, of the first web markets, the Foresight Exchange since 1994, and of DARPA’s Policy Analysis Market, from 2001 to 2003. Robin has developed new technologies for conditional, combinatorial, and intermediated trading, and has studied insider trading, manipulation, and other foul play. Robin has written and spoken widely on the application of idea futures to business and policy, being mentioned in over one hundred press articles on the subject, and advising many ventures, including Consensus Point, GuessNow, Newsfutures, Particle Financial, Prophet Street, Trilogy Advisors, XPree, YooNew, and undisclosable defense research projects.

Robin has diverse research interests, with papers on spatial product competition, health incentive contracts, group insurance, product bans, evolutionary psychology and bioethics of health care, voter information incentives, incentives to fake expertize, Bayesian classification, agreeing to disagree, self-deception in disagreement, probability elicitation, wiretaps, image reconstruction, the history of science prizes, reversible computation, the origin of life, the survival of humanity, very long term economic growth, growth given machine intelligence, and interstellar colonization.