# Simulating dice casting, to calculate Risc game fairness

DevFeed: [Simulating dice casting, to calculate Risc game fairness](<https://devfeed.tech/articles/simulating-dice-casting-to-calculate-risc-game-fairness-39372.md>)

Original publisher: [Read original article](<https://kt.academy/article/risc-dice-dilemma>)

Published: 2024-01-03T00:00:00Z

Content type: article

Language: en

Sources: [Kt. Academy](<https://devfeed.tech/sources/kt-academy.md>)

Topics: [Programming](<https://devfeed.tech/topics/programming.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Simulation](<https://devfeed.tech/topics/simulation.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [game](<https://devfeed.tech/tags/game.md>), [programming](<https://devfeed.tech/tags/programming.md>), [simulation](<https://devfeed.tech/tags/simulation.md>), [strategy](<https://devfeed.tech/tags/strategy.md>), [workshop-learning-programming](<https://devfeed.tech/tags/workshop-learning-programming.md>)

## AI overview

The article examines whether the Risc battle dice-resolution algorithm is fair. It enumerates all 46,656 possible outcomes for five dice and finds that the attacker loses an average of 0.92 soldiers per fight, while the defender loses 1.08.

## Source excerpt

Using programming to simulate a battle in Risc, and learn about its fairness.