Explainable Machine Learning Models for Predicting Player Retention Patterns
Linda Miller 2025-02-05

Explainable Machine Learning Models for Predicting Player Retention Patterns

Thanks to Linda Miller for contributing the article "Explainable Machine Learning Models for Predicting Player Retention Patterns".

Explainable Machine Learning Models for Predicting Player Retention Patterns

This research explores the intersection of mobile gaming and digital citizenship, with a focus on the ethical, social, and political implications of gaming in the digital age. Drawing on sociotechnical theory, the study examines how mobile games contribute to the development of civic behaviors, digital literacy, and ethical engagement in online communities. It also explores the role of mobile games in shaping identity, social responsibility, and participatory culture. The paper critically evaluates the positive and negative impacts of mobile games on digital citizenship, and offers policy recommendations for fostering ethical game design and responsible player behavior in the digital ecosystem.

This paper investigates the dynamics of cooperation and competition in multiplayer mobile games, focusing on how these social dynamics shape player behavior, engagement, and satisfaction. The research examines how mobile games design cooperative gameplay elements, such as team-based challenges, shared objectives, and resource sharing, alongside competitive mechanics like leaderboards, rankings, and player-vs-player modes. The study explores the psychological effects of cooperation and competition, drawing on theories of social interaction, motivation, and group dynamics. It also discusses the implications of collaborative play for building player communities, fostering social connections, and enhancing overall player enjoyment.

This research explores the intersection of mobile gaming and behavioral economics, focusing on how in-game purchases influence player decision-making. The study analyzes common behavioral biases, such as the “anchoring effect” and “loss aversion,” that developers exploit to encourage spending. It provides insights into how these economic principles affect the design of monetization strategies and the ethical considerations involved in manipulating player behavior.

This paper investigates the role of social influence in mobile games, focusing on how social networks, peer pressure, and social comparison affect player behavior and in-game purchasing decisions. The study examines how features such as leaderboards, friend lists, and social sharing options influence players’ motivations to engage with the game and spend money on in-game items. Drawing on social psychology and behavioral economics, the research explores how players' decisions are shaped by their interactions with others in the game environment. The paper also discusses the ethical implications of using social influence to drive in-game purchases, particularly in relation to vulnerable players and addiction risk.

This research investigates how machine learning (ML) algorithms are used in mobile games to predict player behavior and improve game design. The study examines how game developers utilize data from players’ actions, preferences, and progress to create more personalized and engaging experiences. Drawing on predictive analytics and reinforcement learning, the paper explores how AI can optimize game content, such as dynamically adjusting difficulty levels, rewards, and narratives based on player interactions. The research also evaluates the ethical considerations surrounding data collection, privacy concerns, and algorithmic fairness in the context of player behavior prediction, offering recommendations for responsible use of AI in mobile games.

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