The Use of Theory in Designing a Serious Game for the Reduction of Cognitive Biases

---

Barton Meg Symborski Carl Quinn Mary Magee Morewedge Carey K. Kassam Karim S. Korris James H.
2015 DiGRA '15 - Proceedings of the 2015 DiGRA International Conference

In the current study, a serious game was developed to address a training challenge: teaching players to recognize and mitigate their cognitive biases. Cognitive biases, which are human tendencies to commit systematic errors in thinking that lead to irrational judgments, are deeply ingrained and difficult to alter. This paper describes the theorybased approach we employed to create a game for the mitigation of cognitive biases – a challenging and abstract training topic. A cognitive bias framework that relates the target cognitive biases, their causes, and effective bias mitigation techniques was developed and incorporated into the game design. The resultant serious game, titled Missing: The Final Secret, pairs the most promising mitigation strategies with the primary causes of the targeted cognitive biases and incorporates them into game-play. Further, we present preliminary results from a game efficacy evaluation suggesting that Missing is an effective tool for training cognitive bias recognition and mitigation.

 

Fusing Quantitative and Qualitative Methods in Virtual Worlds Behavioral Research

---

Symborski Carl Jackson Gary M. Barton Meg Cranmer Geoffrey Raines Byron Quinn Mary Magee Pearce Celia
2014 DiGRA '13 - Proceedings of the 2013 DiGRA International Conference: DeFragging Game Studies

In this study, Science Applications International Corporation (SAIC) and Georgia Institute of Technology (GT) developed a quantitative-qualitative mixed methods research technique to investigate the extent to which real world characteristics of Massively Multiplayer Online Role-Playing Game (MMORPG) players can be predicted based on the characteristics and behavior of their avatars. SAIC used three primary assessment instruments to quantitatively rate videos of participant gameplay sessions, while GT produced detailed qualitative descriptions of avatar activities and behavior. Automated textual analysis was then used to identify conceptual themes across all of the descriptions produced by the qualitative team. Using the themes generated by the automated textual analysis in combination with the quantitative variables, we were able to demonstrate the efficacy of the hybrid method for the prediction of real world characteristics from avatar characteristics and behavior.