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Markets with Millions of Prices. R. Hanson, J. Ledyard, T. Ishikida IFREE Mini-Conference in Experimental Economics, May 3, 2003. We Want:. Every nation*quarter: Political stability Military activity Economic growth US $ aid US military activity. Combinatorial Info Markets.

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markets with millions of prices

Markets with Millions of Prices

R. Hanson, J. Ledyard, T. Ishikida

IFREE Mini-Conference in Experimental Economics, May 3, 2003

slide2

We Want:

  • Every nation*quarter:
  • Political stability
  • Military activity
  • Economic growth
  • US $ aid
  • US military activity
combinatorial info markets
Combinatorial Info Markets
  • Most markets aggregate info as side effect
  • Info markets beat competing institutions
    • I.E.M. beat president polls 451/596 (Berg etal 2001)
  • But, markets fail when #prices >> #traders
  • Solutions: combo markets, market makers
  • Experiments to test, DARPA funded
    • Caltech students, 12-15 minute periods
    • Train, give info, let trade, see price accuracy
experiment environment
Experiment Environment

(Really: W V X S U Z Y T )

Case A B C D E F G H

1 0 1 0 1 - - - -

2 1 0 0 1 - - - -

3 0 0 1 1 - - - -

4 1 0 1 1 - - - -

5 0 1 1 1 - - - -

6 1 0 0 1 - - - -

7 0 1 1 1 - - - -

8 1 0 0 1 - - - -

9 1 0 0 1 - - - -

10 1 0 0 1 - - - -

Sum 6 3 4 10 - - - -

Same A B C D E F G H

A -- 1 2 6 -- -- -- --

B -- -- 7 3 -- -- -- --

C -- -- -- 4 -- -- -- --

D -- -- -- -- -- -- -- --

  • 8 binary vars: STUVWXYZ
  • 28 = 256 combinations
  • 20% = P(S=0) = P(S=T) = P(T=U) = P(U=V) = … = P(X=Y) = P(Y=Z)
  • 6 people, each see 10 cases: ABCD, EFGH, ABEF, CDGH, ACEG, BDFH
  • random map STUVWXYZ to ABCDEFGH
conclusions
Conclusions
  • Experiments on complex info problem
  • 256 prices from 6 subjects in 15 min.
  • Bayesian estimates way too high a bar
  • Simple DA ~ combo call ~ score rule < combo market maker ~< opinion pool
    • But pools have weight choice problem when expertise is varied, specialized
slide12

We Want:

  • Every nation*quarter:
  • Political stability
  • Military activity
  • Economic growth
  • US $ aid
  • US military activity
environments goals training
Environments: Goals, Training

(Actually: X Z Y )

Case A B C

1 1 - 1

2 1 - 0

3 1 - 0

4 1 - 0

5 1 - 0

6 1 - 1

7 1 - 1

8 1 - 0

9 1 - 0

10 0 - 0

Sum: 9 - 3

Same A B C

A -- -- 4

B -- -- --

C -- -- --

  • Want in Environment:
    • explainable, fast, neutral
    • many variables, few directly related
    • few people, each not see all data cases
    • compute rational share-info estimates
  • Training Environment:
    • 3 binary variables X,Y,Z, 23 = 8 combos
    • P(X=0) = .3, P(X=Y) = .2, P(Z=1)= .5
    • 3 people, see 10 cases of: AB, BC, AC
    • random map XYZ to ABC
experiment structure
Experiment Structure
  • Subjects were Caltech students
  • 6 periods per session, 12-15 minutes each
  • Each subject trained in 3 variable session
  • Metric: Kulback-Leibler i qilog(pi /qi)
troop move decision advice
Troop Move Decision Advice

$1 if War & Move Troops

P(M)

$1 if

Move Troops

P(W | M)

$1

Compare!

P(W | not M)

$1 if Not Move Troops

$1 if War & Not Move Troops

P(not M)

old tech meet new

Accuracy

Simple Info Markets

Market Scoring Rules

Scoring

Rules

opinion

pool

problem

thin market

problem

100

.001

.01

.1

1

10

Estimates per trader

Old Tech Meet New
a scaleable implementation

D

A

C

G

F

B

E

H

A Scaleable Implementation
  • Overlapping variable patches
  • A simple MSR for each patch
  • Arbitrage neighbor patches
    • Limits profits to users who find inconsistencies
  • Only allow trade if all vars in same patch
  • User assets per patch, move via overlap
  • Regroup patches from request activity?
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