Self-Learning Algorithms for Autonomous World Evolution in Games
Donald Green 2025-02-13

Self-Learning Algorithms for Autonomous World Evolution in Games

Thanks to Donald Green for contributing the article "Self-Learning Algorithms for Autonomous World Evolution in Games".

Self-Learning Algorithms for Autonomous World Evolution in Games

This systematic review examines existing literature on the effects of mobile gaming on mental health, identifying both beneficial and detrimental outcomes. It provides evidence-based recommendations for stakeholders in the gaming industry and healthcare sectors.

This study presents a multidimensional framework for understanding the diverse motivations that drive player engagement across different mobile game genres. By drawing on Self-Determination Theory (SDT), the research examines how intrinsic and extrinsic motivation factors—such as achievement, autonomy, social interaction, and competition—affect player behavior and satisfaction. The paper explores how various game genres (e.g., casual, role-playing, and strategy games) tailor their game mechanics to cater to different motivational drivers. It also evaluates how player motivation impacts retention, in-game purchases, and long-term player loyalty, offering a deeper understanding of game design principles and their role in shaping player experiences.

This research explores the role of big data and analytics in shaping mobile game development, particularly in optimizing player experience, game mechanics, and monetization strategies. The study examines how game developers collect and analyze data from players, including gameplay behavior, in-app purchases, and social interactions, to make data-driven decisions that improve game design and player engagement. Drawing on data science and game analytics, the paper investigates the ethical considerations of data collection, privacy issues, and the use of player data in decision-making. The research also discusses the potential risks of over-reliance on data-driven design, such as homogenization of game experiences and neglect of creative innovation.

This paper investigates how different motivational theories, such as self-determination theory (SDT) and the theory of planned behavior (TPB), are applied to mobile health games that aim to promote positive behavioral changes in health-related practices. The study compares various mobile health games and their design elements, including rewards, goal-setting, and social support mechanisms, to evaluate how these elements align with motivational frameworks and influence long-term health behavior change. The paper provides recommendations for designers on how to integrate motivational theory into mobile health games to maximize user engagement, retention, and sustained behavioral modification.

This research critically examines the ethical implications of data mining in mobile games, particularly concerning the collection and analysis of player data for monetization, personalization, and behavioral profiling. The paper evaluates how mobile game developers utilize big data, machine learning, and predictive analytics to gain insights into player behavior, highlighting the risks associated with data privacy, consent, and exploitation. Drawing on theories of privacy ethics and consumer protection, the study discusses potential regulatory frameworks and industry standards aimed at safeguarding user rights while maintaining the economic viability of mobile gaming businesses.

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