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Competitive gaming, a long-standing study context for CSCW, has recently faced criticism due to its design emphasis on competition and achievement, which is associated with adverse phenomena such as player toxicity and anxiety. Recognizing this limit, game designers have proactively made design attempts to ameliorate these unintended consequences of competitive gaming. A notable example is the All Random All Mid (ARAM) mode in League of Legends (LoL), designed to introduce casualness into competitive gaming. To understand how players experience both casualness and competitiveness, a seemingly contradictory pair, we conducted an interview study with ARAM players, finding that ARAM supports 'casual competition' through decentering competition, diversifying interpersonal dynamics, and filling gaps in player needs. We further discuss how game design and player agency co-constitute casual competition, reflect on key aspects of competitive gaming design such as diversity and fairness, and provide implications for competitive gaming design, which may help combat toxicity.
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Multiplayer Space Invaders: A Platform for Studying Evolving Fairness Perceptions in Human-Robot Interaction
Current methods of measuring fairness in human-robot interaction (HRI) research often gauge perceptions of fairness at the conclu- sion of a task. However, this methodology overlooks the dynamic nature of fairness perceptions, which may shift and evolve as a task progresses. To help address this gap, we introduce a platform designed to help investigate the evolution of fairness over time: the Multiplayer Space Invaders game. This three-player game is structured such that two players work to eliminate as many of their own enemies as possible while a third player makes decisions about which player to support throughout the game. In this paper, we discuss different potential experimental designs facilitated by this platform. A key aspect of these designs is the inclusion of a robot that operates the supporting ship and must make multiple decisions about which player to aid throughout a task. We discuss how capturing fairness perceptions at different points in the game could give us deeper insights into how perceptions of fairness fluctuate in response to different variables and decisions made in the game.
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- Award ID(s):
- 2106690
- PAR ID:
- 10507774
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
- ISBN:
- 9798400703232
- Page Range / eLocation ID:
- 347 to 350
- Subject(s) / Keyword(s):
- Human-Robot Interaction Fairness Resource Allocation
- Format(s):
- Medium: X
- Location:
- Boulder CO USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3610978.3640687
