Using optimal decision making tools for balancing in-game economies
10-22, 11:00–11:25 (Europe/Stockholm), Data

Live stream:

Optimization libraries such as SciPy or Nevergrad are commonly used in different data science workflows, such as choosing optimal hyperparameters for a machine learning model or taking actions based on forecasts. In this presentation, we will discuss how such an optimizer can be used to build reward configurations for games (by rewards configurations here we mean bundles of different in-game items that players may get for completing different tasks/quests in a game) Using rewards in Candy Crush Soda as an example, I will show how the problem can be solved using the Nevergard library from Facebook.

In this talk, we will go through several aspects of the end-to-end optimization workflow:
- What is optimization
- The different Python libraries that can be used to perform optimization in practice
- How to optimize an in-game economy
- How this task differs from other optimization workflows (e.g. tuning hyperparameters etc.)
- Case study: using Nevergrad to create optimal reward packs in Candy Crush Soda

I am a Data Scientist at
I've been working in game dev for 6+ years on a lot of well-known mobile titles (such as Cut the Rope, Candy Crush Soda etc). I'm passionate about bringing data perspective to improve users' experience.
As a Data Scientist I'm interested in topics such as segmentation and personalisation, explainable ML models, A/B testing, optimization, and Bayesian statistics.