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Categorizing prediction markets

August 25th, 2008

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I’ve had a couple of discussions recently about the general state of prediction markets; where the industry is going, where companies are finding successes and growth, etc. Some of these discussions can be difficult since there are several dimensions to a prediction marketplace, and thus the dynamics of what makes each successful (or not) varies.

So I am proposing a categorization method for prediction markets. Together, the criteria should be exhaustive and mutually exclusive. This could hopefully help describe the wide variety of potential prediction marketplaces.

Without further ado, here it is:

ICROP Criteria

  • [I] - Interest: Horizontal, Vertical

    Horizontal indicates a broad focus, such as the exchanges run by Inkling, Newsfutures, and Hubdub. Vertical prediction markets are focused on a particular industry or sector, such as the Hollywood Stock Exchange, the now-defunct Storage Markets, or many enterprise prediction markets.

  • [C] - Currency: Real-Money, Play-Money

    This should be straightforward. Is real-money or play-money used to trade on the marketplace?

  • [R] - Rewards (maximum): Real (tangible) rewards, Intangible rewards

    Rewards should be classified by the maximum reward available on the exchange. It only distinguishes between tangible rewards, such as cash or gifts, and intangible rewards such as names on a leaderboard. So even Newsfutures and Hollywood Stock Exchange would be classified as Real Rewards sites, since you can win prizes on both exchanges. This is a fundamentally different potential motivation than a site like Inkling or Hubdub, where there are no tangible rewards available at all.

  • [O] - Objective: Community, Market Results, or Background data

    This refers to the objective of the market creator. Is the purpose of the site to build or leverage a community, such as Inkling or the Chicago Sun-Times?

    Is the purpose to generate unique market results, such as Intrade or an enterprise prediction market?

    Or is the purpose to gather both the market results and information about the traders to generate additional background data known only to administrators, such as HSX?

  • [P] - Privacy: Private, Semi-Private, or Public

    Again, this is largely straightforward. The semi-private case accounts for sites like Betfair that is generally public, but needs to limit participation for legal reasons.

Examples

These are how I would classify a number of prediction marketplaces based on the ICROP criteria.

Betfair:
I - Horizontal
C - Real-Money
R - Real rewards
O - Market results
P - Semi-Private

HSX:
I - Vertical
C - Play-Money
R - Real rewards
O - Background data
P - Public

Hubdub:
I - Horizontal
C - Play-Money
R - Intangible rewards
O - Community
P - Public

InTrade:
I - Horizontal
C - Real-Money
R - Real rewards
O - Market results
P - Semi-Private

Newsfutures:
I - Horizontal
C - Play-Money
R - Real rewards
O - Community
P - Public

[Acme] Enterprise prediction market:
I - Vertical
C - Play-Money
R - Real rewards
O - Market results
P - Private

Some initial thoughts

A few things strike me immediately. First is how so very few prediction marketplaces really utilize what I’m calling “Background data.” (This is something that Bo has mentioned before in reference to his prediction market work at Google.) There is a tremendous opportunity both in public and private markets to analyze and use trader data such as demographic information in conjunction with market data to create a completely new dataset. The only public site that I know of that has really done well here is the Hollywood Stock Exchange. But like Bo, I believe that this information could potentially be invaluable for corporate prediction markets. Unfortunately, none of the software solutions available have good feature-sets for this.

Second, there is certainly a link between Currency and Rewards. Real-money markets result in real tangible rewards, where play-money markets can result in either tangible or intangible rewards. Perhaps they shouldn’t be somehow combined, but I think they are important enough to list separately.

Summary

This isn’t a fully-formed classification scheme, and would certainly appreciate any comments (publicly in the comments or privately via e-mail) to refine it.

This entry was posted on Monday, August 25th, 2008 at 1:28 pm and is filed under General, Implementation, Software.

Viewing 8 Comments

    • ^
    • v
    Interesting categorization. A couple of points:
    1. Prediction markets with tangible rewards rely on both tangible and intangible rewards. The vast majority of people who play games with prizes generally aren't doing list the prizes as a important motivation.
    2. On objective I would say we are actually aiming for all three however initially we are focusing on community.

    BTW Any chance of a link on the blog roll?
    • ^
    • v
    Hi, Nigel.

    1. Completely agree. I know I participate on HSX and other sites without thinking about prizes, though I'm not sure how widespread that is. To be honest, I was thinking here of Predictify. They offer small cash rewards and play-money winnings. But when I go on Predictify, all I do is trade on the markets where there is a potential cash reward, and ignore the rest. I think this is an important dimension, perhaps there are more "steps" than I indicated here.

    2. That's what I was thought based on what I've seen on Hubdub so far. Again, you hit on the criterion where there's certainly some grey areas. Perhaps the first two examples I mentioned are actually the same thing? (They are both different than the "background data" intention, in my opinion.)

    And I just added you to the blog roll. Sorry; it's been a while since I updated it!
    • ^
    • v
    On currency or rewards, they should be listed seperately, because the behavior of the players in the market will vary and differ based on whether there are tangible rewards or not (this is mainly for play money, I cant think of a situation with real money currency for intangible rewards). It makes sense that the people in Currency - Play Money, Rewards - Tangible are going to be the least risk averse, since they have nothing to lose and everything to gain, with the opposite being Currency - Real Money, Rewards - Real Money, which should lead to the most risk-averse behavior. Not sure which leads to "better" prediction markets, since differing levels of credibility could be applied based on the Currency/Rewards setup.

    One thing about Background Data, I'm curious to see if any prediction market uses the background/demographic data to "weigh" trades/predictions, to help either control for insider knowledge or to promote insider knowledge. For example, if I'm trading in stock for Iron Man 2 on HSX, it would be helpful to know industry insiders are trading on the stock (similar to the SEC filings that are required by Wall Street).

    By the way, like the ICROP acronym, easy to remember!
    • ^
    • v
    Hi, Jose. Thanks for commenting.

    I do think that Currency and Rewards should be kept seperate. But perhaps there should be a more granular scale for Rewards, from:
    Intangible Rewards -> "Gift"-type Rewards -> Cash Rewards

    Though I still believe that marketplaces should be classified by the maximum reward a trader can earn, not every person is really even trying to be in competition for that top slot.

    On Background Data, I know HSX has some algorithms that they use to generate their forecasts for the studios that essentially "correct" the publically traded figures. But no market uses any demographic data to change the publically-traded figures. However, if you're a good trader you should have earned more capital, and thus have more influence on the price through shear volume of shares/contracts purchased.
    • ^
    • v
    I'm curious; I completely forgot to address the method of trading in the criteria above. I think this is probably a fairly critical distinction in prediction marketplaces. Sites that use a standard Continuous Double Auction (CDA) certainly operate differently than those using Robin Hanson's MSR or David Pennock's DPM. Thoughts?
    • ^
    • v
    Hey Jed,

    When I did my heuristic reviews of prediction markets almost two years ago, I used the following categorizations:

    Market Purpose: (e.g. fun, gambling, create industry specific predictions, etc.)
    Market Control: (i.e. who creates the markets: users, market makers, the platform providers, etc.)
    Targeted End User: (e.g. gambler, farmer, general user, doctor, etc.)
    Currency: (real or play)
    Underlying Event Types: (e.g. events, stock market, etc)
    Contract Types: (e.g. binary, etc.)
    Trading Mechanism:
    Size of Market: (i.e. volume)
    Number of Contracts: (or, rather, number of markets)
    • ^
    • v
    Thanks for that, Alex. I think I remember that, but do you have a link?

    That's some good food for thought. I hope to post an updated version soon.
    • ^
    • v
    interesting effort
 

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