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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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Petri Lankoski Södertörn Univeristy
SimulationsEvaluating game system behavior
Petri Lankoski Södertörn Univeristy
SimulationsGame 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
Simulating a game systemModel
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.
Settlers of Catan
Petri Lankoski Södertörn Univeristy
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
Simulation set-up• 4 victory point set-up
• Settlements -> cities• 6 victory point sim• 1&2) 8 victory point sim
Petri Lankoski Södertörn Univeristy
Model#!/usr/bin/python import randomfrom 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
Resource gain
Petri Lankoski Södertörn Univeristy
Robber Effect
Petri Lankoski Södertörn Univeristy
Balance of set-up1 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
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
Monopoly
Board & Movement
Petri Lankoski Södertörn Univeristy
Chance to end Up In a square1/40 = 2,50%?
3 doublesin a row
1/16 Card takesto Jail
A player can increaseprobability to land to These squares(out with doubles)
Petri Lankoski Södertörn Univeristy
Chance to Land at a Square
Petri Lankoski Södertörn Univeristy
Break Even Times
Petri Lankoski Södertörn Univeristy
What we learnedStaying 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
Thank You!
http://petrilankoski.wordpress.com