Thomas Clark
2025-02-02
Adaptive Difficulty Systems in Mobile Games: A Machine Learning Approach
Thanks to Thomas Clark for contributing the article "Adaptive Difficulty Systems in Mobile Games: A Machine Learning Approach".
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.
This paper focuses on the cybersecurity risks associated with mobile games, specifically exploring how game applications collect, store, and share player data. The study examines the security vulnerabilities inherent in mobile gaming platforms, such as data breaches, unauthorized access, and exploitation of user information. Drawing on frameworks from cybersecurity research and privacy law, the paper investigates the implications of mobile game data collection on user privacy and the broader implications for digital identity protection. The research also provides policy recommendations for improving the security and privacy protocols in the mobile gaming industry, ensuring that players’ data is adequately protected.
This study examines how engaging with mobile games affects attention span and cognitive control processes. It investigates both the potential benefits, such as improved focus, and the risks, such as attention deficits.This paper analyzes the development and diversification of mobile game genres over time, highlighting key trends and innovative game mechanics. It discusses how these changes reflect technological advancements and shifting player preferences.
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