Game Dynamics Examplesllms.txt

Game dynamics models capture how rules, competition, and matchmaking shape the flow of play. They connect game-theory incentives with hands-on gameplay tuning.

Whether exploring auctions, queue-based matchmaking, or randomness in loot drops, these examples illustrate how small adjustments in strategy or pairing algorithms dramatically alter the player experience.

Select an example to explore it in the playground.

Intermediate - Classic Dynamics

First-Price vs. Vickrey Auctions

Repeated sealed-bid auctions comparing first-price and Vickrey rules with learning agents.

Level: Intermediate

game-theoryauctionlearningincentivesimpy

  • Probes: revenue, allocative_efficiency, truthful_bidding
Fork

Dynamic Difficulty Adjustment

Adaptive encounters that track win rate to keep players challenged.

Level: Intermediate

gameplayreinforcing-loopbalancing-loopadaptivegame

  • Stocks: skill, difficulty, retention
  • Flows: practice gains, difficulty adaptation
  • Feedback Loops: skill improvement (reinforcing), challenge tuning (balancing)
  • Probes: skill, difficulty, win_rate, frustration, retention
Fork

Inspection Game

A ranger who patrols just 40% of sites can still stop poaching - but only if the penalty is harsh enough. Soften it and the same patrols let crime pay.

Level: Intermediate

game-theoryreinforcementmonitoringdeterrence

  • Feedback Loops: poacher adaptation (reinforcement)
  • Probes: catch_rate, ranger_coverage, poacher_payoff
Fork

Matchmaking Queue

Simulates skill-based matchmaking with adjustable buckets, skill delta and max wait.

Level: Intermediate

matchmakingqueuegamefairness

  • Probes: queue_len, avg_wait, avg_delta
Fork

War of Attrition

Two-player waiting game exploring mixed strategies and the option value of waiting.

Level: Intermediate

game-theorywaiting-gamesimpy

  • Probes: contest_length, surplus, cost_variance
Fork