Cognitive Models of Decision-Making in High-Stakes Mobile Games
Kenneth Nelson 2025-02-09

Cognitive Models of Decision-Making in High-Stakes Mobile Games

Thanks to Kenneth Nelson for contributing the article "Cognitive Models of Decision-Making in High-Stakes Mobile Games".

Cognitive Models of Decision-Making in High-Stakes Mobile Games

The allure of virtual worlds is undeniably powerful, drawing players into immersive realms where they can become anything from heroic warriors wielding enchanted swords to cunning strategists orchestrating grand schemes of conquest and diplomacy. These virtual realms are not just spaces for gaming but also avenues for self-expression and creativity, where players can customize their avatars, design unique outfits, and build virtual homes or kingdoms. The sense of agency and control over one's digital identity adds another layer of fascination to the gaming experience, blurring the boundaries between fantasy and reality.

This paper examines the potential of augmented reality (AR) in educational mobile games, focusing on how AR can be used to create interactive learning experiences that enhance knowledge retention and student engagement. The research investigates how AR technology can overlay digital content onto the physical world to provide immersive learning environments that foster experiential learning, critical thinking, and problem-solving. Drawing on educational psychology and AR development, the paper explores the advantages and challenges of incorporating AR into mobile games for educational purposes. The study also evaluates the effectiveness of AR-based learning tools compared to traditional educational methods and provides recommendations for integrating AR into mobile games to promote deeper learning outcomes.

This paper examines the growth and sustainability of mobile esports within the broader competitive gaming ecosystem. The research investigates the rise of mobile esports tournaments, platforms, and streaming services, focusing on how mobile games like League of Legends: Wild Rift, PUBG Mobile, and Free Fire are becoming major players in the esports industry. Drawing on theories of sports management, media studies, and digital economies, the study explores the factors contributing to the success of mobile esports, such as accessibility, mobile-first design, and player demographics. The research also considers the future challenges of mobile esports, including monetization, player welfare, and the potential for integration with traditional esports leagues.

This paper explores the use of data analytics in mobile game design, focusing on how player behavior data can be leveraged to optimize gameplay, enhance personalization, and drive game development decisions. The research investigates the various methods of collecting and analyzing player data, such as clickstreams, session data, and social interactions, and how this data informs design choices regarding difficulty balancing, content delivery, and monetization strategies. The study also examines the ethical considerations of player data collection, particularly regarding informed consent, data privacy, and algorithmic transparency. The paper proposes a framework for integrating data-driven design with ethical considerations to create better player experiences without compromising privacy.

This paper explores the application of artificial intelligence (AI) and machine learning algorithms in predicting player behavior and personalizing mobile game experiences. The research investigates how AI techniques such as collaborative filtering, reinforcement learning, and predictive analytics can be used to adapt game difficulty, narrative progression, and in-game rewards based on individual player preferences and past behavior. By drawing on concepts from behavioral science and AI, the study evaluates the effectiveness of AI-powered personalization in enhancing player engagement, retention, and monetization. The paper also considers the ethical challenges of AI-driven personalization, including the potential for manipulation and algorithmic bias.

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