[Paper Review] Data-driven Design: A Case for Maximalist Game Design

Gabriella A. B. Barros, Michael Cerny Green+2 more|arXiv (Cornell University)|May 30, 2018

Digital Games and Media27 references3 citations

TL;DR

This paper introduces maximalist game design as a data-driven approach that combines diverse open data sources—such as Wikipedia and OpenStreetMap—into procedurally generated adventure games via the Data Adventures framework. It proposes a design space centered on balancing data transformation vs. fidelity and functional vs. decorative content, while highlighting ethical risks like bias, misinformation, and unintended representation of real individuals.

ABSTRACT

Maximalism in art refers to drawing on and combining multiple different sources for art creation, embracing the resulting collisions and heterogeneity. This paper discusses the use of maximalism in game design and particularly in data games, which are games that are generated partly based on open data. Using Data Adventures, a series of generators that create adventure games from data sources such as Wikipedia and OpenStreetMap, as a lens we explore several tradeoffs and issues in maximalist game design. This includes the tension between transformation and fidelity, between decorative and functional content, and legal and ethical issues resulting from this type of generativity. This paper sketches out the design space of maximalist data-driven games, a design space that is mostly unexplored.

Motivation & Objective

• To explore the design space of data-driven games that embrace heterogeneous data sources, inspired by artistic maximalism.
• To examine the tradeoffs between transforming data for gameplay versus preserving its original form (fidelity).
• To investigate how data can serve both functional and decorative roles in game content.
• To identify ethical and legal challenges in generating games from open, potentially biased or inaccurate data.
• To propose a framework for maximalist game design that supports creativity, learning, and critical engagement with data.

Proposed method

• Using the Data Adventures series of game generators to create adventure games from open data like Wikipedia and OpenStreetMap.
• Mapping the design space along two dimensions: data transformation vs. data fidelity, and functionality vs. decoration.
• Applying procedural content generation (PCG) techniques to recombine data into narrative-driven games with mechanics like murder mysteries.
• Analyzing generated games for narrative coherence, data accuracy, and ethical implications such as misrepresentation or bias.
• Drawing analogies between data game design and artistic practices like collage, sampling, and remixing to inform conceptual frameworks.
• Evaluating real-world examples, including games featuring fictionalized deaths of real celebrities, to assess ethical risks.

Experimental results

Research questions

• RQ1 What does it mean for games designed from data to be maximalist, and how does this differ from traditional procedural game design?
• RQ2 What is the tradeoff between transforming data for gameplay and maintaining fidelity to the original source data?
• RQ3 How do functional and decorative uses of data shape the character and quality of data-driven games?
• RQ4 What purposes can maximalist data-driven games serve, and how does purpose influence design choices?
• RQ5 What new legal and ethical issues—such as bias, misinformation, or offensive content—arise from combining open data in game generation?

Key findings

• The Data Adventures generators successfully created playable adventure games using open data, including narrative-driven murder mysteries based on real people and places.
• A significant bias was observed in generated content, with 8 of the 10 most common locations in 100 games being in North America, reflecting biases in Wikipedia and DBpedia data.
• Instances of data misrepresentation occurred, such as image searches for Margaret Thatcher returning images of Aung San Suu Kyi, highlighting risks in automated data linking.
• Games featuring real celebrities as victims or perpetrators—such as Justin Bieber’s fictional murder—raise ethical concerns about unintended harm or public perception.
• The use of social media data or trending topics increases the risk of generating offensive or provocative content due to volatile or stereotyped associations.
• Despite risks, data-driven maximalist games offer potential for engaging, personalized learning experiences by enabling players to interact with data through narrative and gameplay.