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Predicting the future –with games — Introductory article
Information Dynamics Lab — Internal prediction markets
BRAIN - (Behaviorallly Robust Aggregation of Information in Networks) — Scoring Rules (i.e., non-trading technique)
Bernardo A. Huberman - Bernardo Huberman - (HP Labs, California, U.S.A.) - Bio - Senior Fellow & Director
Kay-Yut Chen - (HP Labs, California, U.S.A.)
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Tags: A. Huberman - Bernardo Huberman, Bernardo A. Huberman, California, consultancy firms, consultants, corporate prediction markets, enterprise prediction markets, Google, HP Labs, Information Dynamics Lab, internal prediction markets, Kay-Yut Chen, private prediction markets

Wrap-up reviews of the 2007 Consensus Point conference on prediction markets

Chris F. Masse October 1st, 2007

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#1. Justin Wolfers on the 2007 Consensus Point conference

Robin Hanson tells me that he is now (back to) bullish on prediction markets - he saw real evidence of real firms implementing prediction markets and taking them seriously.

#2. Jed Christiansen on the 2007 Consensus Point conference

Robin [Hanson] gave a fairly standard introduction to prediction markets lecture that some may have seen at other events or downloaded from his website. It was a good overview of the topic.

The question and answer period was the most interesting part with Robin. He was asked about manipulation, and provided some fairly convincing answers that manipulation shouldn’t be a worry (at least with the correct incentives.) Robin described the situation in terms of sheep and wolves. Sheep aren’t that knowledgeable; they are trading for any number of reasons, and are the “noise” in the marketplace. Wolves take advantage of that, and consequently they look for markets with lots of sheep. With better information, the wolves will easily have plenty to “eat.” The net result is that those noisy markets are accurate markets.

Another concept he talked about was creating a “fudge” account. Let’s say you want to weight one set of traders more than another, or simply want to “move” the forecast in one direction or another; create a “fudge” account to conduct those transactions. If after the account has been running for a while and it’s positive, you’ll know you’ve done a good job fudging. But if the fudge account is negative, you don’t know more than the market so just stop fudging and leave the market to itself. It’s a great idea, and fairly easy to implement.

Tags: Jed Christiansen, Justin Wolfers, Robin Hanson