Debate is raging between Robin Hanson and the futarchy critics

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Robin Hanson comments on Paul Hewitt&#8217-s blog.

Paul Hewitt comments on Eric Crampton&#8217-s blog.

Paul Hewitt comments on Robin Hanson&#8217-s blog. Many exchanges with Robin Hanson. Read it all.

Paul Hewitt:

[…] My point is that the case for prediction markets has not been made, at all. There is a tiny bit of proof that they are as good as alternative methods, and in a very few cases, very slightly better. Also, you need to be aware that even the slightly better prediction markets had the benefit of the alternative forecasting institution available to it. That is, the official forecasters at HP were also participants in the ever-so-slightly better prediction markets. […]

&#8211-&gt- I personally stay away from any discussion about conditional prediction markets (and futarchy). I prefer focusing on the &#8217-simple&#8217- prediction markets.

If he had balls, Robin Hanson would debate Paul Hewitt, instead.

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Paul Hewitt: The Essential Prerequisite for Adopting Prediction Markets

It is a long text, so I will post again about it, in the near future. (Happy Xmas, by the way.)

ADDENDUM: Saturday, January 16, 2010: Debate between Robin Hanson and Mencius Moldbug

The Accuracy Of Prediction Markets

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

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-


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?

The truth about (enterprise) prediction markets

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Paul Hewitt:

[…] In virtually every case, the prediction market forecast is closer to the official HP forecast than it is to the actual outcome. Perhaps these markets are better at forecasting the forecast than they are at forecasting the outcome! Looking further into the results, while most of the predictions have a smaller error than the HP official forecasts, the differences are, in most cases, quite small. For example, in Event 3, the HP forecast error was 59.549% vs. 53.333% for the prediction market. They’re both really poor forecasts. To the decision-maker, the difference between these forecasts is not material.

There were eight markets that had HP official forecasts. In four of these (50%), the forecast error was greater than 25%. Even though, only three of the prediction market forecast errors were greater than 25%, this can hardly be a ringing endorsement for the accuracy of prediction markets (at least in this study). […]

To the despair of the Nashville imbecile, Paul&#8217-s analysis is quite similar to mine (circa February 14, 2009):

The prediction market technology is not a disruptive technology, and the social utility of the prediction markets is marginal. Number one, the aggregated information has value only for the totally uninformed people (a group that comprises those who overly obsess with prediction markets and have a narrow cultural universe). Number two, the added accuracy (if any) is minute, and, anyway, doesn’t fill up the gap between expectations and omniscience (which is how people judge forecasters). In our view, the social utility of the prediction markets lays in efficiency, not in accuracy. In complicated situations, the prediction markets integrate expectations (informed by facts and expertise) much faster than the mass media do. Their accuracy/efficiency is their uniqueness. It is their velocity that we should put to work.

Prediction markets are not a disruptive technology, but merely another means of forecasting.

Go reading Paul&#8217-s analysis in full.

I would like to add 2 things to Paul&#8217-s conclusion:

  1. We have been lied to about the real value of the prediction markets. Part of the &#8220-field of prediction markets&#8221- (which is a terminology that encompasses more people and organizations than just the prediction market industry) is made up of liars who live by the hype and will die by the hype.
  2. Prediction markets have value in specific cases where it could be demonstrated that an information aggregation mechanism is the appropriate method that should be put at work in those cases (and not in others). Neither the Ivory Tower economic canaries nor the self-described prediction market &#8220-practitioners&#8221- have done this job.

Paul Hewitt on enterprise prediction markets

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– Here, in response to Jed Christiansen. (Scroll down.)

– Here, on his own blog.

Interesting. (Paul should learn to pepper his posts with external links, though. Otherwise, a web visitor out of the loop can&#8217-t get the background of an issue that is discussed. The foundation of the Web is hyper-linking, Paul.)