Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Chase Covello, Graduate Student Hyunjang Jung, Graduate Student Bryan C Watson, Faculty
Lead Presenter's Name
Chase Covello
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Bryan C Watson
Abstract
Research into critical infrastructure network architecture design faces two significant challenges. First, real-world network performance data is often not available due to being proprietary. Secondly, many efforts focus on analyzing the structure of an infrastructure network at a single point in time, while real-world networks are constantly evolving. In this article, these two gaps (need for more data and for time-series data) are examined by utilizing a new data source: the video game Factorio. Factorio is a manufacturing simulator. Utilizing publicly available recordings of players’ networks in game, a shared end point, and completion time stamps allows the examination of different network strategies. The key research question examined in this work is how does network evolution change when comparing ten expert and ten novice designers? This article provides two key contributions. First, a qualitative and quantitative analysis of how ten different structural graph theory metrics evolve when comparing expert and novice designers is provided. The expert dataset has a narrower distribution, indicating common strategies, and focuses on critical path manufacturing early in the network’s evolution. The second contribution is a set of time-series network data that can be used for additional studies. By examining the differences in network evolution between experts and novices, this article performs a critical first step towards using in-situ graph theory metrics as a decision aid for designers during infrastructure evolution.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Using graph theory to investigate the role of expertise on infrastructure evolution: A case study examining the game Factorio
Research into critical infrastructure network architecture design faces two significant challenges. First, real-world network performance data is often not available due to being proprietary. Secondly, many efforts focus on analyzing the structure of an infrastructure network at a single point in time, while real-world networks are constantly evolving. In this article, these two gaps (need for more data and for time-series data) are examined by utilizing a new data source: the video game Factorio. Factorio is a manufacturing simulator. Utilizing publicly available recordings of players’ networks in game, a shared end point, and completion time stamps allows the examination of different network strategies. The key research question examined in this work is how does network evolution change when comparing ten expert and ten novice designers? This article provides two key contributions. First, a qualitative and quantitative analysis of how ten different structural graph theory metrics evolve when comparing expert and novice designers is provided. The expert dataset has a narrower distribution, indicating common strategies, and focuses on critical path manufacturing early in the network’s evolution. The second contribution is a set of time-series network data that can be used for additional studies. By examining the differences in network evolution between experts and novices, this article performs a critical first step towards using in-situ graph theory metrics as a decision aid for designers during infrastructure evolution.