What prediction markets can learn from Twitter – REDUX

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S-I-M-P-L-I-C-I-T-Y + C-U-S-T-O-M-I-Z-A-T-I-O-N

Twitter is a very simplistic core. Around that core is a whole ecosystem that adds value to Twitter. You have a large choice of &#8220-Twitter clients&#8221-, and there are plenty of websites out there that complement the basic Twitter service.

It is the anti-BetFair case. The future prediction exchanges will be built around a simplistic core, and there will be plenty of add-ons and complimentary services &#8212-all of which will be optional, so as not to scare off the newbies.

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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Flawed New Hampshire polls = Non-accurate New Hampshire prediction markets

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The most comprehensive analysis ever conducted of presidential primary polls:

&#8220-a handful of methodological missteps and miscalculations combined to undermine the accuracy of predictions about presidential primary winners in New Hampshire and three other states.&#8221-

Via Mister the Great Research Scientist David Pennock &#8211-who is an indispensable element of the field of prediction markets.

As I blogged many times, prediction markets react to polls&#8230- See the addendum below&#8230- – [UPDATE: See also Jed’s comment.] – Prediction markets should not be hyped as crystal balls, but simply as an objective and continuous way to aggregate expectations. So, if you think of it, their social utility is much smaller than what the advocates of the &#8220-idea futures&#8221-, &#8220-wisdom of crowds&#8221- or &#8220-collective intelligence&#8221- concepts told us. Much, much, much, much smaller&#8230- They all make the mistake to put accuracy forward. (By the way, somewhat related to that issue, please go reading the dialog between Robin Hanson and Emile Servan-Schreiber.)

Addendum

California Institute of Technology economist Charles Plott:

What you&#8217-re doing is collecting bits and pieces of information and aggregating it so we can watch it and understand what people know. People picked this up and called it the &#8220-wisdom of crowds&#8221- and other things, but a lot of that is just hype.

New Hampshire – The Democrats

The Hillary Clinton event derivative was expired to 100.

Dem NH Clinton

Dem NH Obama

Dem NH Edwards

New Hampshire – The Republicans

The John McCain event derivative was expired to 100.

Rep NH McCain

Rep NH Romney

Rep NH Huckabee

Rep NH Giuliani

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Blogging Against The Hype

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gartner_hype_cycle

I have been blogging a lot about the damage done by some Ivory Tower economic professors and some commercial practitioners who exaggerate the benefits of the prediction markets. (Some people are not very happy with what I said. :-D ) The Gartner consultants have a word for that &#8212-&#8221-hype&#8221-. Hyping is defined as the act of publicizing in an exaggerated and often misleading manner. The way Internet citizens can guard against hype is to read bloggers and journalists (whatever you call them) who publish high-quality reports and opinions about the brand-new products and the fresh startups. It is a difficult task. It requires a solid expertise and a way to deflect away commercial influence and pressure (e.g., from some professionals who think that bloggers shouldn&#8217-t publish anything without their prior &#8220-consent&#8221-). If you want a role model for such an impartial journalist, I recommend to look at search engine expert Danny Sullivan of Search Engine Land. If you have 2 minutes, you could go there and scan his hype-bursting talking points.

Addendum:

gartner-2008

George Mason University is *not* in the top 50 graduate schools of economics.

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US News &amp- World Report – Rankings

GMU is in the second tier, not even ranked numerically.

Maybe some of the GMU professors spend too much time blogging (as opposed to producing fundamental and applied research), and maybe some of them spend too much time putting up the show. They should focus on improving the standing of their university, first.

gmu

eLab eXchange – Which real estate search site will see the most traffic?

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The eLab eXchange is up and running again with new markets and more to come on a regular basis!

Spring is the traditional home buying season and many real estate professionals are predicting a significant &#8220-spring bounce.&#8221- Will it happen? Try your hand at predicting the rise (or fall) in web traffic to 5 popular real estate search sites.

If you haven’t visited us lately, come back and see what we’ve got for you to judge: http://elabexchange.com.

Judge right and you can win $25 and have a chance to win the quarterly mega-prize.

Hope to see you soon,

Lawrence D. Wright, Ph.D.

Research Associate, UCR eLab eXchange

Does information economics apply to prediction markets?

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Who is Paul Hewitt&#8230- and what the hell is &#8220-information economics&#8221-&#8230-!??&#8230-

paul-hewitt

About Paul S. Hewitt, B.Comm, CA

I am a Chartered Accountant with a public practice in Toronto, Canada. While much of my work involves personal and corporate income tax, my practice consults with corporations to improve their business planning processes. I am a graduate of the University of Toronto, with a B.Comm degree, but this is somewhat misleading. A significant portion of my course load was focused on economics, and in particular, information economics. Then, it was a relatively new branch of economics, and it had yet to become overly bogged down by theoretical calculus! In short, it was fun. I wrote an undergraduate thesis: “A New Theory of the Economics of Discrimination.”

Then, I moved on to the corporate world, obtaining my CA designation while working at Price Waterhouse in Toronto. Several years later, I branched out on my own, developing a public practice primarily focused on tax consulting.

&#8220-Information Economics&#8221-:

Information economics or the economics of information is a branch of microeconomic theory that studies how information affects an economy and economic decisions. Information has special characteristics. It is easy to create but hard to trust. It is easy to spread but hard to control. It influences many decisions. These special characteristics (as compared with other types of goods) complicate many standard economic theories.

The subject is treated under Journal of Economic Literature classification code JEL D8 – Information, Knowledge, and Uncertainty. The present article reflects topics included in that code. There are several subfields of information economics. The first insights in information economics related to the economics of information goods. In recent decades, there have been influential advances in the study of information asymmetries and their implications for contract theory. Finally, with the rise of computers, economists have begun to study economics of information technology.

The starting point for economic analysis is the observation that information has economic value because it allows individuals to make choices that yield higher expected payoffs or expected utility than they would obtain from choices made in the absence of information.

I like that. For those interested in more, Paul tells me that the Toronto Public Library has freed some academic papers on information economics. E-mail him for more info.

Does information economics apply to prediction markets?

  1. Information generated by our prediction markets is easy to create but hard to trust.
  2. The market-generated predictions are easy to spread but hard to control.
  3. They influence many decisions.

I think that only #2 is true &#8212-and #1 is half true (although I could also say it is true, too, I am not really sure about that one). The fact that #3 is untrue infirms the Hanson approach. Your comments?