Global Game Jam Stockholm Presentation some additional slides & bullets (that I removed to fit the presentation in 20 minutes)

1. Simulations
Evaluating game system behavior
Petri Lankoski
Södertörn Univeristy

2. Simulations
 Game systems with random component are
complex
 Simulations can help to understand how a part of
the system behaves
 One does not need ready game for simulation
 Does not replace playtesting
 But simulation can show the features work in the long
run
 Balancing weapons & troops
 non-symmetrical things are hard to balance
Petri Lankoski
Södertörn Univeristy

3. Simulating a game system
 Model
 sum of two six sided dice -> sum of two random
numbers between 1 to 6
 Weapon: change to hit, damage dealt & fire rate
 Simulating system
 Run model many times to learn how the system
behaves
 Run 50000 times and calculate distribution or
averages, average damage per minute, etc.
Petri Lankoski
Södertörn Univeristy

4. Settlers of Catan

5. Simulation
 How the players gain resources
 Simplified
 Robber vs no robber discard
 Only resource amount simulated, not types
 Assumptions
 Four player game
 0-3 resources at hand when ones turn ends
 Model for using resources
 One specific board set-up
 The results does not vary much board to board
 The results can vary with not optimal settlement placements
 50 000 iterations used
Petri Lankoski
Södertörn Univeristy

6. Simulation set-up
• 4 victory point set-up
• Settlements -> cities
• 6 victory point sim
• 1&2) 8 victory point sim
Petri Lankoski
Södertörn Univeristy

7. Model
#!/usr/bin/python
import random
from collections import Counter
# board model (2 victory points)
field1 = {
2: {'white': 0, 'blue':0, 'red': 0, 'orange': 0},
3: {'white': 0, 'blue':0, 'red': 1, 'orange': 1},
4: {'white': 1, 'blue':1, 'red': 0, 'orange': 0},
5: {'white': 0, 'blue':2, 'red': 1, 'orange': 0},
6: {'white': 1, 'blue':1, 'red': 1, 'orange': 1},
8: {'white': 1, 'blue':1, 'red': 1, 'orange': 1},
9: {'white': 1, 'blue':0, 'red': 0, 'orange': 1},
10: {'white': 1, 'blue':0, 'red': 1, 'orange': 1},
11: {'white': 0, 'blue':0, 'red': 1, 'orange': 1},
12: {'white': 0, 'blue':0, 'red': 0, 'orange': 0}
}
 The above model does not contain handling for
robber
 The code for simulating this model is bit more
complicated
Petri Lankoski
Södertörn Univeristy

8. Resource gain
Petri Lankoski
Södertörn Univeristy

9. Robber Effect
Petri Lankoski
Södertörn Univeristy

10. Balance of set-up
1
2
3
4
White
2.0553
2.6120
3.1700
3.7267
Blue
2.0761
2.6593
3.2396
3.8224
Red
2.0808
2.6661
3.2496
3.8348
Orange
2.0892
2.6745
3.2605
3.8454
• Resource gain for each color is very similar
• White might have small disadvantage
Petri Lankoski
Södertörn Univeristy

11. What can one learn?
 Easy to run what if scenarios
 Robber -> discard all
 Discard if more than four resources
 Estimating the costs for building
 Balance of the the initial set-up
Petri Lankoski
Södertörn Univeristy

12. Monopoly
Petri Lankoski
Södertörn Univeristy

13. Board & Movement
A player can
increase
probability to
land to
These squares
(out with doubles)
Chance to end
Up In a square
1/40 = 2,50%?
1/16 Card takes
to Jail
3 doubles
in a row

14. Chance to Land at a
Square
Petri Lankoski
Södertörn Univeristy

15. Break Even Times
Petri Lankoski
Södertörn Univeristy

16. What we learned
 Staying in prison strategy alters changes to land
other squares
 Long prison stay good at the end game
 Break even time downward trend is good
 Breakeven times are long
 Slow start
 Note that one cannot build before owning all
squares with that color
Petri Lankoski
Södertörn Univeristy

17. Thank You!
 http://petrilankoski.wordpress.com
Petri Lankoski
Södertörn Univeristy