Does it make sense to apply the Kelly Criterion to Hanson's LMSR? It seems to intuitively, but my math skills are too weak.
Thanks.
So I've kind of formulated a possible way to use markets to predict quantiles. It seems quite flawed looking back on it two and a half weeks later, but I still think it might be an interesting line of inquiry.
This doesn't always apply. It can, for example, leave you with an hour to kill at a train station, because you decided it would be really embarrassing to show up late for your ride to a CFAR workshop because of the planning fallacy.
Shorter posts when you're starting is a step in the right direction.
What could you do to make reading alone more pleasant, without a trade-off in productivity?
System 1 is the intuitive one, system 2 is the formal reasoning.
"If it's yellow let it mellow, if it's brown flush it down."
This is one of the first things I remember learning, growing up with tank water.
Sorry, should've been more clear.
I've started work on a rudimentary play money binary prediction market using LMSR in django (still very much incomplete, PM me for a link if you'd like), and my present interface is one of buying and selling shares, which isn't very user friendly.
With a "changing the price" interface that Hanson details in his paper, accurate participants can easily lose all their wealth on predictions that they're moderately confident in, depending on their starting wealth. If I have it so agents can always bet, then the wealth accumulation in accurate predictors won't happen and the market won't actually learn which agents are more accurate.
With an automated Kelly interface, it seems that participants should be able to input only their probability estimates, and either change the price to what they believe it to be if the cost is less than Kelly, or it would find a price which matches the Kelly criterion, so that agents with poorer predictive ability can keep playing and learn to do better, and agents with better predictive ability accumulate more wealth and contribute more to the predictions.
However, I'm uncertain as to whether a) the markets would be as accurate as if I used a conventional "changing the price" interface (due to the fact that it seems we're doing log utility twice), and b) whether I can find find the Kelly criterion for this, with a probability estimate being the only user input and the rest calculated from data about the market, the user's balance, etc.