Playing around with horse racing (sports predictions markets) a lot at Betfair, I finally fully understood the flaw in Bayes, and the difference between near and far modes.
I noticed that far from a big race, the prices on offer are much poorer -the 'book' is well above 100% for backing(betting for), and well below 100% for Laying (betting against). Near a big race, the prices are much better, and the book begins to convergence on 100%.
I've observed the same thing for horses which are unknown qualities - foreign horses for instance, or horses running for the first time (maidins). Punters think them as far abstractions, and the odds are gnerally poor, whereas for horses that are known qualities (lots of info avaliable), they are thought of as near and the prices are generally better.
It seems that when thinking in far mode, punters badly miscalibrate probabilities in a very particular way. When thinking of how to take advantage of this, I suddeny had a casade of insights.
Here's the horse racing gambling insight: In far mode (a long way out from a race), you don't want to take the odds on offer. The only way to win is to be the market maker and be setting the odds yourself. You should back at the lay price, and lay at the back price
And here's the generalized insight:
Far mode is the market maker and near mode is only deciding on the accuracy of the prices
And applied to Bayes, decision theory and logic:
The flaw in Bayes is that is can only decide on the accuracy of the probabilities, it is not a market maker. Even decision theory can be redefined in purely passive terms, as a predefined set of actions which may or not be enacted. categorization on the other hand, it creative, it changes the very parameters on which decision making is based. It does not passively calcuate odds, instead it makes the odds.
Bayes (near mode thought) is the sucker punter that can only accept or reject the odds, but Categorization (far mode thought) is the bookie and ultimate market marker
Super click:
Playing around with horse racing (sports predictions markets) a lot at Betfair, I finally fully understood the flaw in Bayes, and the difference between near and far modes.
I noticed that far from a big race, the prices on offer are much poorer -the 'book' is well above 100% for backing(betting for), and well below 100% for Laying (betting against). Near a big race, the prices are much better, and the book begins to convergence on 100%.
I've observed the same thing for horses which are unknown qualities - foreign horses for instance, or horses running for the first time (maidins). Punters think them as far abstractions, and the odds are gnerally poor, whereas for horses that are known qualities (lots of info avaliable), they are thought of as near and the prices are generally better.
It seems that when thinking in far mode, punters badly miscalibrate probabilities in a very particular way. When thinking of how to take advantage of this, I suddeny had a casade of insights.
Here's the horse racing gambling insight: In far mode (a long way out from a race), you don't want to take the odds on offer. The only way to win is to be the market maker and be setting the odds yourself. You should back at the lay price, and lay at the back price
And here's the generalized insight:
Far mode is the market maker and near mode is only deciding on the accuracy of the prices
And applied to Bayes, decision theory and logic:
The flaw in Bayes is that is can only decide on the accuracy of the probabilities, it is not a market maker. Even decision theory can be redefined in purely passive terms, as a predefined set of actions which may or not be enacted. categorization on the other hand, it creative, it changes the very parameters on which decision making is based. It does not passively calcuate odds, instead it makes the odds.
Bayes (near mode thought) is the sucker punter that can only accept or reject the odds, but Categorization (far mode thought) is the bookie and ultimate market marker
And yes, my insights do work. I win.
Robin Hanson and John Delaney talking prediction markets: http://english.aljazeera.ne...