Tag Archives: Collective Forecasting

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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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.

Can prediction markets help improve economic forecasts?

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At VOX, David Hendry and James Reade examine the question, &#8220-How should we make economic forecasts?&#8221- Among the ideas discussed is whether prediction markets could be used to improve economic forecasting. Interesting suggestion and seeming to be worthy of additional exploration, but the authors don&#8217-t go too deep here.  Instead, they assert that &#8220-prediction markets can be viewed as a form of &#8230- model averaging,&#8221- and then drift into a discussion of forecast averaging. I&#8217-m not sure that forecast averaging is a good way to look at prediction markets.

Here is what they say:

Prediction markets can be viewed as a form of forecast pooling or model averaging, a common forecast technique (Bates and Granger 1969, Hoeting et al 1999 and Stock and Watson 2004). That is, forecasts from different models are combined to produce a single forecast. In prediction markets, each market participant makes a forecast based on his or her own forecasting model, and the market price is some function of each of these individual forecasts.

Since the &#8220-prediction&#8221- implied by a prediction market is set by the marginal transaction, it depends not at all on the distribution of earlier trades, nor on the valuations of parties priced out of the market at the current price.

For example, consider two event markets: in the first 999 contracts trade at $0.50 and the 1000th and final trade is at $0.75- in the second 999 contracts trade at $0.76 and the 1000th and final trade is at $0.75.  In the typical interpretation of prediction markets, the event is &#8220-predicted&#8221- to result with a 75 percent probability in both cases.  However, averaging among the different predictions doesn&#8217-t get you that result.

(Well, strictly speaking the market price is &#8220-some function&#8221- of the prices &#8211- namely, one in which all trades but the last are weighted zero and the last trade is weighted one. You can call this &#8220-averaging,&#8221- but that isn&#8217-t the most useful explanation of the function.)

I&#8217-m not arguing that forecast averaging might not be a good idea in many situations, just that averaging doesn&#8217-t seem like a good way to explain what a prediction market is doing.

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The Accuracy Of Prediction Markets

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– A Lesson in Prediction Markets from the Game of Craps &#8211- by Paul Hewitt

– Why Public Prediction Markets Fail &#8211- by Paul Hewitt

Both articles are required reading for Jed Christiansen and Panos Ipeirotis (alias &#8220-Prof Panos&#8221-). :-D

Were prediction markets useless during the H1N1 breakout?

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Lance Fortnow:

Might this be an instance where prediction markets greatly out-performed the experts? In short, no. There were relevant markets but two big problems:

  • No one thought to create a market for the number of flu cases over a couple of thousand.
  • Prediction markets require a verifiable outcome so they were based on CDC confirmed cases. But after the flu turned out not to be that dangerous, the CDC stopped confirming most cases and there were less than 7500 confirmed cases by the end of May.

Why CrowdCast ditched Robin Hansons MSR as the engine of its IAM software

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Dump

–

Leslie Fine of CrowdCast:

Chris,

As Emile points out, in 2003 I started experimenting with (and empirically validating) alternatives to the traditional stock-market metaphor that will be more viable in corporate settings. We found the level of confusion and lack of interest in the usual fare led to a death spiral of disuse and inaccuracy. BRAIN was a first stake in the ground in prediction market mechanism design with usability as a fundamental premise.

When I joined Crowdcast (then Xpree) in August of 2008, Mat and the team already recognized the confusion around, and consequent poor adoption of, the MSR mechanism. The number of messages I fielded in my first month here asking me to explain pricing, shorting, how to make money, etc. was astounding. We all knew that we had to start from scratch, and rebuild a mechanism that was easy to use, expressive both in terms of the question one can ask and the message space in which one can answer, and provided a high level of user engagement. We have abandoned the MSR in favor of a new method that users are already finding much simpler and that requires a lower level of participation and sophistication than the usual stock market analogy.

I wish I could go into more detail. However, we need to keep a little bit of a lid on things for our upcoming launch. I can only beg your patience a little while longer, and I hope you will judge our offering worth the wait.

Regards,
Leslie

Nota Bene: IAM = information aggregation mechanism

UPDATE: They are out with their new collective forecasting mechanism.

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