GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study)
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ABSTRACT:
SUBMITTER: Lauw H
PROVIDER: S-EPMC7206269 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
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Project description:People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.
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Project description:BackgroundMaintaining social relationships is a basic human need and particularly essential in old age, including when living in a retirement home. Multiplayer video games can promote positive social interactions among players from different generations while playing. Yet, such facilitation of positive social interactions depends on specific game design. To systematically investigate the effects of game design on social interaction between seniors and their coplayers, the game Myosotis FoodPlanet was developed in this study, and the impacts of 3 different game modes on social interaction were compared in a controlled field trial.ObjectiveThis study aims to compare the effects of 3 different game modes (competitive, cooperative, and creative) on social interactions (verbal and nonverbal communication) between seniors and their younger coplayers.MethodsThis study was conducted in a Swiss retirement home as a controlled field trial. Participants were residents of the retirement home (N=10; mean age 84.8 years, SD 5.9 years) and played in pairs with their caregivers. Each pair played 3 game modes in random order. This resulted in 30 game sequences of 20 minutes each. A within-subject design was applied with game mode as the within-factor and social interaction as the outcome variable. To assess the quality of social interaction, 30 video-recorded game sequences were analyzed based on an event sampling method.ResultsAnalysis of variance for repeated measurements revealed significant effects: there was significantly more verbal communication in the creative mode than in the cooperative mode (P=.04) with a strong effect size (Cohen f=0.611). An examination of verbal communication revealed more game-related communication in the creative mode than in the cooperative mode (P=.01) and the competitive mode (P=.09) with marginally significant effects and strong effect sizes (Cohen f=0.841). In addition, significantly more biography-related communication occurred in the creative mode than in the cooperative mode (P=.03), with a strong effect size (r=0.707). Regarding nonverbal communication (eg, laughing together), analysis of variance for repeated measurements showed significant differences among the game modes (P=.02) with a strong effect size (Cohen f=0.758). Results showed that there was significantly more laughing together in the competitive mode (competitive>cooperative>creative).ConclusionsThe results show that game mode can be an important factor for shaping the social interactions of players playing together. Compared with other modes, creative game modes can increase verbal communication. In contrast, competitive modes may stimulate more laughing together. This has important implications for game design and the use of computer games to promote social interaction between seniors and their coplayers in practice.
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Project description:Spatial structure greatly affects the evolution of cooperation. While in two-player games the condition for cooperation to evolve depends on a single structure coefficient, in multiplayer games the condition might depend on several structure coefficients, making it difficult to compare different population structures. We propose a solution to this issue by introducing two simple ways of ordering population structures: the containment order and the volume order. If population structure S1 is greater than population structure S1 in the containment or the volume order, then S1 can be considered a stronger promoter of cooperation. We provide conditions for establishing the containment order, give general results on the volume order, and illustrate our theory by comparing different models of spatial games and associated update rules. Our results hold for a large class of population structures and can be easily applied to specific cases once the structure coefficients have been calculated or estimated.
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Project description:This paper examines quantitative predictors of team performance in Massively Multiplayer Online Games (MMOGs) based on team management literature. Analyzing data from more than 140,000 squad-mode matches involving over 500,000 players, we replicate and extend existing research by confirming a curvilinear association between behavioral interdependence and team performance and introduce the moderating effect of experience. For less experienced teams, behavioral interdependence follows an inverted U-shaped pattern showing that excessive collaboration may be counterproductive. However, this is not the case for experienced teams, where the relationship is fairly linear. Additionally, we observe that riskier teams tend to perform worse. Moreover, our research also highlights the potential of e-sports data in advancing behavioral science and management research. The digital nature of e-sports datasets, characterized by size and granularity, mitigates concerns related to reproducibility, replicability, and generalizability in social science research, offering a cost-effective platform for scholars with diverse backgrounds.
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Project description:Evolutionary game dynamics in structured populations has been extensively explored in past decades. However, most previous studies assume that payoffs of individuals are fully determined by the strategic behaviors of interacting parties, and social ties between them only serve as the indicator of the existence of interactions. This assumption neglects important information carried by inter-personal social ties such as genetic similarity, geographic proximity, and social closeness, which may crucially affect the outcome of interactions. To model these situations, we present a framework of evolutionary multiplayer games on graphs with edge diversity, where different types of edges describe diverse social ties. Strategic behaviors together with social ties determine the resulting payoffs of interactants. Under weak selection, we provide a general formula to predict the success of one behavior over the other. We apply this formula to various examples which cannot be dealt with using previous models, including the division of labor and relationship- or edge-dependent games. We find that labor division can promote collective cooperation markedly. The evolutionary process based on relationship-dependent games can be approximated by interactions under a transformed and unified game. Our work stresses the importance of social ties and provides effective methods to reduce the calculating complexity in analyzing the evolution of realistic systems.
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