Markets with millions of prices
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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

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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

  • 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

(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


Theory Benchmarks


Simple Double Auction


Combinatorial Call


Individual Scoring Rule


Market Scoring Rule


Log Opinion Pool


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


We Want:

  • Every nation*quarter:

  • Political stability

  • Military activity

  • Economic growth

  • US $ aid

  • US military activity


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


3 Variable Training Data


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

$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)


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


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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