A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games
Karen Harris 2025-01-31

A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games

Thanks to Karen Harris for contributing the article "A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games".

A Framework for Explainable AI in Predicting Player Behavior in Multiplayer Games

This paper examines the application of behavioral economics and game theory in understanding consumer behavior within the mobile gaming ecosystem. It explores how concepts such as loss aversion, anchoring bias, and the endowment effect are leveraged by mobile game developers to influence players' in-game spending, decision-making, and engagement. The study also introduces game-theoretic models to analyze the strategic interactions between developers, players, and other stakeholders, such as advertisers and third-party service providers, proposing new models for optimizing user acquisition and retention strategies in the competitive mobile game market.

Game streaming platforms like Twitch, YouTube Gaming, and Mixer have revolutionized how gamers consume and interact with gaming content, turning everyday players into content creators, influencers, and entertainers. Livestreamed gameplay, interactive chats, and community engagement redefine the gaming experience, transforming passive consumption into dynamic, participatory entertainment.

This research examines how mobile gaming facilitates social interactions among players, focusing on community building, communication patterns, and the formation of virtual identities. It also considers the implications of mobile gaming on social behavior and relationships.

The future of gaming is a tapestry woven with technological innovations, creative visions, and player-driven evolution. Advancements in artificial intelligence (AI), virtual reality (VR), augmented reality (AR), cloud gaming, and blockchain technology promise to revolutionize how we play, experience, and interact with games, ushering in an era of unprecedented possibilities and immersive experiences.

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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