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Simulations: Evaluating game system behavior

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

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Simulations: Evaluating game system behavior

1. 1. Simulations Evaluating game system behavior Petri Lankoski Södertörn Univeristy
2. 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. 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. 4. Settlers of Catan
5. 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. 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. 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. 8. Resource gain Petri Lankoski Södertörn Univeristy
9. 9. Robber Effect Petri Lankoski Södertörn Univeristy
10. 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. 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. 12. Monopoly Petri Lankoski Södertörn Univeristy
13. 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. 14. Chance to Land at a Square Petri Lankoski Södertörn Univeristy
15. 15. Break Even Times Petri Lankoski Södertörn Univeristy
16. 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. 17. Thank You!  http://petrilankoski.wordpress.com Petri Lankoski Södertörn Univeristy