What We Did

• Played a LOT of games of Jenga ~400• Chose 5 different strategies to play• Recorded 3 observables

– Number of bricks that fell in “avalanche”– Last brick touched before “avalanche”– Distance from base of tower to furthest brick after the

tower fell

From This To This

StrategiesMiddles OutMiddles Out

Middle Then SidesMiddle Then Sides

ZigZagZigZag

Side 1 Side 2

JENGA JENG

A

JENGA

Side 1

JENGA

JENGA

Side 1 Side 2

JENGA JENG

A

JENGA

All Outside BricksAll Outside Bricks

Side 1 Side 2

JENGA JENG

A

JENGA

AND FINALLY…AND FINALLY…

An optimal game strategy An optimal game strategy where we would start from where we would start from

the bottom and work our way the bottom and work our way up, pulling out any bricks up, pulling out any bricks

which were loose enough to which were loose enough to pull out easilypull out easily

Many Strategies So We Could …

• Compare strategies to see if any patterns were emerging

• Compare more ordered methods of pulling bricks out to the random optimal strategy

• See if strategies used had a large impact on the data obtained.

Whoooooaaaaaaa!!!!!!!!

• We hoped to see at least some emerging signs of a complex system as more data was taken

• We assumed the distance of blocks from base would be Gaussian to begin with but maybe tend towards a power law

• Perhaps some patterns relating to strategies used and observables

What We Expected

Results for Different Strategies

Everyone's All Middle Norm Data

-10123456789

10

0 10 20 30 40

Number of Values in Ranges of 5

Everyone's Norm ZigZag Data

-2

0

2

4

6

8

10

12

0 5 10 15 20 25

Number of Values in Ranges of 5

Everyone's All Outside Norm Data

-2

0

2

4

6

8

10

12

0 10 20 30

Number of Values in Ranges of 5

Everyone's Optimal Norm data

-2

0

2

4

6

8

10

12

0 10 20 30 40 50

Number of Values in Ranges of 5

Everyone's Mid/Out Norm data

-5

0

5

10

15

0 10 20 30 40

Number of Values in Ranges of 5Nu

mbe

r of C

ount

s

Results for Brick DistancesAll Data Put Into Ranges and With Means Subtracted

-505

101520253035404550

0 10 20 30 40 50

Number of Values In Ranges of 5

Nu

mb

er

of

Co

un

ts Data

Gaussian Fit

Cauchy-Lorentz Fit

Not Enough Data to definitively rule out one distribution, Gaussian and Not Enough Data to definitively rule out one distribution, Gaussian and Cauchy-Lorentz look to fit data quite wellCauchy-Lorentz look to fit data quite well