Explainable Machine Learning Models for Predicting Player Retention Patterns
Pamela Kelly 2025-02-01

Explainable Machine Learning Models for Predicting Player Retention Patterns

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

Explainable Machine Learning Models for Predicting Player Retention Patterns

This study explores the use of mobile games as tools for political activism and social movements, focusing on how game mechanics can raise awareness about social, environmental, and political issues. By analyzing games that tackle topics such as climate change, racial justice, and gender equality, the paper investigates how game designers incorporate messages of activism into gameplay, narrative structures, and player decisions. The research also examines the potential for mobile games to inspire real-world action, fostering solidarity and collective mobilization through interactive digital experiences. The study offers a critical evaluation of the ethical implications of gamifying serious social issues, particularly in relation to authenticity, message dilution, and exploitation.

This study examines the growing trend of fitness-related mobile games, which use game mechanics to motivate players to engage in physical activities. It evaluates the effectiveness of these games in promoting healthier behaviors and increasing physical activity levels. The paper also investigates the psychological factors behind players’ motivation to exercise through games and explores the future potential of fitness gamification in public health campaigns.

The intricate game mechanics of modern titles challenge players on multiple levels. From mastering complex skill trees and managing in-game economies to coordinating with teammates in high-stakes raids, players must think critically, adapt quickly, and collaborate effectively to achieve victory. These challenges not only test cognitive abilities but also foster valuable skills such as teamwork, problem-solving, and resilience, making gaming not just an entertaining pastime but also a platform for personal growth and development.

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.

This study explores the role of player customization in mobile games, focusing on how avatar and character customization can influence player identity, self-expression, and engagement. The research examines how customizing characters, outfits, and other in-game features enables players to create personalized experiences that reflect their preferences and identities. Drawing on social identity theory and self-concept research, the paper investigates how customization fosters emotional attachment to the game, as well as its impact on player behavior, such as social interaction and competition. The study also explores the commercial implications of offering customizable in-game items, including microtransactions and virtual economies.

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