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<span class="d-block text-center">Frank James</span>
<span class="date d-block text-center small text-uppercase text-black-50 mb-5">2025-02-01</span>
<h2 class="heading text-center">Cognitive Models of Decision-Making in High-Stakes Mobile Games</h2>
<p class="lead mb-4 text-center">Thanks to Frank James for contributing the article "Cognitive Models of Decision-Making in High-Stakes Mobile Games".
</p>
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<p>This paper offers a post-structuralist analysis of narrative structures in mobile games, emphasizing how game narratives contribute to the construction of player identity and agency. It explores the intersection of game mechanics, storytelling, and player interaction, considering how mobile games as “digital texts” challenge traditional notions of authorship and narrative control. Drawing upon the works of theorists like Michel Foucault and Roland Barthes, the paper examines the decentralized nature of mobile game narratives and how they allow players to engage in a performative process of meaning-making, identity construction, and subversion of preordained narrative trajectories.</p>
<p>This research explores the intersection of mobile gaming and behavioral economics, focusing on how in-game purchases influence player decision-making. The study analyzes common behavioral biases, such as the “anchoring effect” and “loss aversion,” that developers exploit to encourage spending. It provides insights into how these economic principles affect the design of monetization strategies and the ethical considerations involved in manipulating player behavior.</p>
<p>This paper explores the use of artificial intelligence (AI) in predicting player behavior in mobile games. It focuses on how AI algorithms can analyze player data to forecast actions such as in-game purchases, playtime, and engagement. The research examines the potential of AI to enhance personalized gaming experiences, improve game design, and increase player retention rates.</p>
<p>This study leverages mobile game analytics and predictive modeling techniques to explore how player behavior data can be used to enhance monetization strategies and retention rates. The research employs machine learning algorithms to analyze patterns in player interactions, purchase behaviors, and in-game progression, with the goal of forecasting player lifetime value and identifying factors contributing to player churn. The paper offers insights into how game developers can optimize their revenue models through targeted in-game offers, personalized content, and adaptive difficulty settings, while also discussing the ethical implications of data collection and algorithmic decision-making in the gaming industry.</p>
<p>The gaming industry's commercial landscape is fiercely competitive, with companies employing diverse monetization strategies such as microtransactions, downloadable content (DLC), and subscription models to sustain and grow their player bases. Balancing player engagement with revenue generation is a delicate dance that requires thoughtful design and consideration of player feedback.</p>
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