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

Markets with Millions of Prices

R. Hanson, J. Ledyard, T. Ishikida

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


Markets with millions of prices

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


Theory benchmarks

Theory Benchmarks


Simple double auction

Simple Double Auction


Combinatorial call

Combinatorial Call


Individual scoring rule

Individual Scoring Rule


Market scoring rule

Market Scoring Rule


Log opinion pool

Log Opinion Pool


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


Markets with millions of prices

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


3 variable training data

3 Variable Training Data


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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