An Interesting New Prediction Market

Speaking of which I think the guy who talked about having a trustworthiness rating for individuals is on the right track. Except, anything that distracts users from making immediate changes (such as logging in, or a notion that I have to build my reputation to have an equal voice) could be the death of wikipedia. My approach to improve wikipedia would be to include an “information liquidity” metric along with each page, similar to a stock’s trading volume. Pages could be grey-scale coded based on the page change history, with high-volume pages appearing darker, more solid. Of course this can be gamed, but here gaming has visible artifacts. As far as accuracy goes, I think this would solve Stephen’s objection because the informational backwaters — pages with lower liquidity — would appear visually distinct from the heavily modified. In machine learning there’s a construct called a Boltzmann Machine (aka simulated annealing machine) which describes the dynamics of systems like wikipedia, but it requires a metric like volume/liquidity/energy. An alternative approach to social networking for solving accuracy/trust problems is this one, which I’m very intrigued by, but the system isn’t big enough yet to bear out the promises.

When coming up with the concept for Truth Markets, I originally was looking to create an “information reputation authority” based on the Device Reputation Authority model. I was unable though to get the model to work because of the trust bootstrap issue. Earlier incarnations of truth markets suffered from the same fate, at least in theory. The fatal argument was essentially, with no agreed-upon underlying value, what’s to keep truth claims from becoming tulips? So based on the advice of Robin Hansen and Patri Friedman and a bunch of bloggers, I modified the model to have a quasi-external “authority” to arbitrate truth claims. The interesting thing is that the expiration date for current truth claims is a statistical distribution rather than a hard and fast date. Namely, when you trade a truth claim, all you know is that at some point in the future the issue will be voted on by a random sample of the entire market (there are mechanisms for obviating collusion or any sort of coordinated cheating). My hope is that the uncertainty of exact judgment date is enough to make individual traders act rationally with respect to their wallets at all points in time and thus coordinate price with actual truth value at all times and mitigate bubbles.

Bringing it back to the Digg controversy, I think that a variant of truth markets (call it relevance markets) could fix the problem. Digg is essentially a current popularity poll for news items that works on the aggregate of people surfing as they normally would, but giving a thumbs up rating to anything that they “digg”. The stuff that bubbles to the top has a decidedly techy-libertarian slant, which makes sense because of the self-selecting population of Digg users. The controversy is about whether this truly is wisdom of the crowds, or whether there’s too much mob behavior and undue influence by high-profile Diggers. By substituting “truth” with “relevance” or “importance”, the truth market mechanism could solve the Digg dilemma. In general, the underlying value of the claims in the interlocking market system described in the truth markets document could be anything that people care about: truth, importance, humor, attractiveness, morality, etc.