Author Topic: prediction of numbers  (Read 1750 times)


  • Global Moderator
  • Sr. Member
  • *****
  • Posts: 384
    • View Profile
prediction of numbers
« on: June 01, 2014, 09:41:54 pm »
people will want to prediction the future exchange rate between all sorts of things.

If there are 1000 possibilities, the initial liquidity has to be bigger than when there are only 2 possibilities.  ln(1000)/ln(2)=~10, so it has to be about 10 times bigger.
The initial deposit grows with log base 2 of the number of possible outcomes.

The number of decisions required to reach consensus on a number can also scale by the log.
instead of this list of questions:
"is BTC/USD bigger than 600"
"is BTC/USD bigger than 601"...

We ask this set of questions:
"is BTC/USD modulus 2 equal to 1"
"is BTC/USD modulus 4 equal to 1"
"is BTC/USD modulus 8 equal to 1"....

So the number of decisions necessary to build the market grows by log base 2 of the number of possible outcomes.


  • Administrator
  • Sr. Member
  • *****
  • Posts: 464
    • View Profile
Re: prediction of numbers
« Reply #1 on: June 01, 2014, 10:01:08 pm »
In whitepaper version 1.2, and the R code, you can predict continuous variables, and type in a literal number. Instead of binning to [0, .5, 1], you get a number in range(0,1) which scales up to anything you like.

Moreover, in Paper 2 (PM Types), I provide an example with the "exchange rate above (500, 1000, 1500, 2000, 2500, 3000, 3500)", but following that, I use a log scale on page 7 where I say “Did more than [10,000; 100,000; 1,000,000; 10,000,000] Americans die in 1917?”.
Nullius In Verba