That place where people, places, and things intersect is usually referred to as a "community." It doesn't matter what kind of people, places, and things we're talking about, it's still a community, and social networks are simply the most recent incarnation. One thing disparate communities share, at least according to Weixiong Zhang, a Washington University associate professor of computer science and engineering and of genetics, and Jianhua Ruan, a faculty member in Department of Computer Science at the University of Texas at San Antonio, are networks within communities that define the community's structure.

Continue reading "Communitites and the Networks that Define Them"

Freelancer Blog

Prediction markets go by a number of names -- information markets, decision markets, idea futures, event derivatives, virtual markets, and the like. But by whatever name, the idea is more or less the same -- you "bet" on the outcome of an event or market trends with the goal of aggregating information, not necessarily making money. (In most cases, participants use fake money, although with others like the Iowa Electronic Markets you can bet small amounts of real money.)

For example, when will the price of Intel QuadCore CPUs fall below $50 per unit? Or in 2009, what percentage of technology industry total advertising spend will constitute web display advertising? Or when will Windows 7 be released? Or, asks your manager, what's your best bet on when your project will be released?

One of my favorite prediction markets is the Popular Science Predictions Exchange, which lets you predict, for instance, stuff like: When will the PlayStation 3 outsell both the Xbox 360 and the Nintendo Wii? Or will Grand Theft Auto IV be the best-selling game of 2008?

So what's the big deal with prediction markets? Well, for one thing studies indicate that prediction markets can be more accurate than polls in predicting events. If so, then that's a pretty powerful case for prediction markets.

But we're finding out that prediction markets can be influenced in much the same way as, say, poker. Specifically, a new mathematical model by University of Michigan researchers suggests that bluffing in prediction markets can cloud their accuracy. The solution that Rahul Sami and Stanko Dimitrov propose in their paper Non-Myopic Strategies in Prediction Markets is to penalize later trades by charging participants to make them. For instance, says, Sami, suppose a prediction market involves two traders and the outcome of two coin flips. Participants bet on whether both coins will land the same or different. Each participant can see the outcome of one of the coin flips. This represents the fact that all participants in a prediction market presumably have a piece of information that helps them decide which outcome they believe is most likely. Each participant typically trusts that everyone is betting honestly.

One person must bet first and this person would not have the benefit of additional information from other participants. Say the first participant's coin is heads. If this trader wishes to bluff to extract more information from the other better, she could bet that both coins are tails (knowing this is impossible). The other trader might read this as proof that the first trader's coin is tails. So if his is tails, he would also bet that both coins are tails. Now, because of the bluff, the prediction market is not reflecting the outcome that is truly the most likely.

The first trader in this scenario now assumes that the second trader's coin is tails and would likely change her bet to reflect that the coins are different. She would win more money. Charging people to change their bets would give them more incentive to be honest from the start, the researchers say.

-- Jonathan Erickson
jerickson@ddj.com