Modelling game economy with Neo4j OSCON

1. Modelling Game Economy with Neo4j Yan Cui (@theburningmonk)

2. 1MILLION USERS ACTIVE DAILY

3. 250MILLION DAY PER REQUEST

4. noSQL = MongoDB

5. stolefrom loves loves enemy enemy A Good Man Goes to War appeared in appeared in appeared in appeared in Victory of the Daleks appeared in appeared in companion companion enemy appeared in appeared in planet prop species species species character character character episode episode

6. source : http://db-engines.com/en/ranking_categories

7. Graph DBs are great for modelling complex domains

8. Leonhard Euler invented Graph Theory in 1736

9. Leonhard Euler invented Graph Theory in 1736 (that’s 275 years before Edgar Codd formulated the relational model)

10. Find a walk through the city that would cross each bridge once and only once.

11. Eliminate features to leave just the land masses and the bridges.

12. Land masses became vertices, and bridges became edges.

13. location

14. 500+ Spots

15. location episodic

16. Season 1 London

17. Season 2 Nan Jing

18. location episodic multi-player

19. herebemonstersgame.com iPad buddies

20. location episodic multi-player RPG

21. 4000 Items

22. 800 Recipes

23. 1500+ Quests

24. 100+ Monsters

25. game balancing

26. item Pricing

27. item Pricing

28. item Pricing

29. item Pricing

30. I’ll just change this one thing…

31. manual game balancing is SLOW

32. it is REPETITIVE

33. ERROR-PRONE! SUBJECTIVE

34. there must be a better way...

35. Hello, Neo4j

36. catchin

37. location bait attraction rate catch rate

38. as a graph in Neo4j

39. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in

40. nodes & edges can have! arbitrary properties

41. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in Strength = 502! Speed = 201! Intelligence = 184 Strength = 420! Speed = 210! Technology = 240

42. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in CatchRate = 0.774

43. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in Buy Price = 20BN! Sell Price = 482 Gold! Sellable = true! …

44. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in DropRate = 0.1

45. Bigfoot Alice Lake exists_in Alluring Goat can_attract Bigfoot Toenail Clippings Musket-teer Trap loots can_catch Omar Lake exists_in Yowie Yeti can_catch can_catch Apprentice’s Workshop can_m ake Goat Honey Yeti Fur requires loots makes Alluring Goat Recipe requires requires Beeswax loots Bee Hiveharvests McDonald’s Farm sells loots Goat’s Milk harvests London exists_in Peryton Fawn Decoy requires Peryton Fawn Decoy makes

49. game balancing (revisited)

50. impact analysis

51. What if I increase the price of “White Bread”?

52. CRAFTS RECIPE ITEMITEM IS_USED_IN

53. MATCH! (wb:BaseItem { Name:"White Bread"})! -[rel:CRAFTS | IS_USED_IN*1..]! ->(i:BaseItem)! RETURN i, rel, wb

54. MATCH! (wb:BaseItem { Name:"White Bread"})! -[rel:CRAFTS | IS_USED_IN*1..]! ->(i:BaseItem)! RETURN i, rel, wb Node-[rel]->Node

55. CRAFTS RECIPE ITEMITEM IS_USED_IN Node-[rel]->Node

61. RECIPE White Bread IS_USED_IN ITEM CRAFTS MONSTERATTRACTS RECIPE IS_USED_IN ITEM CRAFTS ITEM LOOTS RECIPE IS_USED_IN ITEM CRAFTS IS_USED_IN RECIPEIS_USED_IN CRAFTS

67. scarcity analysis

68. How scarce is “Durian” compared to “Dragonfruit”?

69. EXISTS_IN FRUIT TREE SPOTFRUIT FORAGES

70. MATCH! ! (fruit)<-[:FORAGES]-(tree)-[:EXISTS_IN]->(spot)! WHERE! ! fruit.Name=‘Durian’ OR! fruit.Name=‘Dragonfruit’! RETURN fruit, tree, spot

71. MATCH! ! (fruit)<-[:FORAGES]-(tree)-[:EXISTS_IN]->(spot)! WHERE! ! fruit.Name=‘Durian’ OR! fruit.Name=‘Dragonfruit’! RETURN fruit, tree, spot Node<-[r1]-Node-[r2]->Node

72. EXISTS_IN FRUIT TREE SPOTFRUIT FORAGES Node<-[r1]-Node-[r2]->Node

73. MATCH! ! (fruit)<-[:FORAGES]-(tree)-[:EXISTS_IN]->(spot)! WHERE! ! fruit.Name=‘Durian’ OR! fruit.Name=‘Dragonfruit’! RETURN fruit, tree, spot

74. Durian Dragonfruit

75. quest lines

76. AWARDS QUEST ITEMITEM REQUIRES UNLOCKS

77. What quests come after “Year of the Horse”?

78. MATCH! (q1:Quest { Name: “Year of the Horse” })! -[:UNLOCKS]! ->(q2:Quest) ! RETURN q1, q2

79. How do you model quest progression?

80. 1. Price Items 2. Enrich Model 3. “Price” Quests

81. monster hierarchy

82. Catch me No, catch ME

83. IS_USED_IN CAN_ATTRACT ITEMMONSTER LOOTS RECIPE CRAFTSITEMMONSTER

84. MATCH! (monster1:Monster)-[:LOOTS]->(loot)! -[r:IS_USED_IN | CRAFTS*0..]->(bait)! -[:CAN_ATTRACT]->(monster2)! RETURN monster1, monster2

85. MATCH! (monster1:Monster)-[:LOOTS]->(loot)! -[r:IS_USED_IN | CRAFTS*0..]->(bait)! -[:CAN_ATTRACT]->(monster2)! RETURN monster1, monster2

86. Monster 1Monster 2 Quest 1 Quest 2 Unlocks Unlocks

87. Requires Monster 1Monster 2 Quest 1 Quest 2 Unlocks Unlocks Requires

88. successful catch = loot + gold

89. ∑(Bait Price * Attraction Rate) Input = Output ( ∑(Loot Price * Drop Rate) + Gold ) * Success Rate

90. IS_USED_IN CAN_ATTRACT ITEMMONSTER LOOTS RECIPE CRAFTSITEMMONSTER

91. NEW monster ! = More competitor for bait! = Lower attraction rate for all monsters

92. Graph DBs are great for modelling complex domains

93. impact analysis

94. fraud detection

95. social networks

96. recommendation engine

97. graphdatabases.com

98. Thank you! @theburningmonk theburningmonk.com github.com/theburningmonk

Modelling game economy with Neo4j OSCON

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Modelling game economy with Neo4j OSCON

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