UROP Research Mentor Project Submission Portal: Submission #1282
Submission information
              Submission Number: 1282
  Submission ID: 21006
  Submission UUID: f892e7f3-9c3b-49f6-a673-73edd169ee8b
      Submission URI: /urop-research-mentor-project-submission-portal
          Submission Update: /urop-research-mentor-project-submission-portal?token=so6g_AoUgt0PbKfOW0ylffrnT2m912sl91zwW4KY9w4
      Created: Mon, 08/18/2025 - 08:45 PM
  Completed: Mon, 08/18/2025 - 09:23 PM
  Changed: Wed, 10/29/2025 - 03:27 PM
  Remote IP address: 179.218.246.76
  Submitted by: Anonymous
  Language: English
  Is draft: No
    Webform: UROP Project Proposal Portal
      Submitted to: UROP Research Mentor Project Submission Portal
    
          Research Mentor Information
      
  
  
  Marcos Müller Vasconcelos
  
  
  
  
      
  
  
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  FAMU-FSU College of Engineering
  
  
  
  
      
  
  
  Electrical Engineering
  
  
  
  
  
  
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          Overall Project Details
      
  
  
  The Logic of the Feed: Mathematical Models of Recommender Systems in Digital Media Platforms
  
  
  
  
      
  
  
  Economics, Game Theory, Recommender Systems, Human-AI Interaction
  
  
  
  
      
  
  
  No
  
  
  
  
      
  
  
  2
  
  
  
  
      
  
  
  Economics, Statistics, Business, Electrical Engineering, Computer Science
  
  
  
  
      
  
  
  FAMU-FSU College of Engineering
  
  
  
  
      
  
  
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  Partially Remote
  
  
  
  
      
  
  
  10 hours/week
  
  
  
  
      
  
  
  Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
  
  
  
  
      
  
  
  Most information consumed online today flows through digital media platforms such as YouTube, TikTok, and Instagram. Because the volume of available content is overwhelming, these platforms rely on recommendation systems to filter and promote material. However, recommendations are made under uncertainty: platforms cannot directly observe a user’s private preferences. Instead, they adapt their strategies to maximize engagement, even if it means amplifying extreme or polarized content. Our recent research has modeled this interaction as a signaling game between a platform and a user: the user seeks content aligned with their preferences, while the platform seeks to maximize engagement regardless of alignment. Theoretical results show that equilibrium strategies in such games can naturally lead to the escalation of extreme content availability. This raises important questions about how algorithms, user behavior, and economic incentives jointly shape the online information ecosystem.
Objectives:
This UROP project aims to explore recommender systems by focusing on three interconnected areas:
1. Algorithmic Foundations – how recommendation strategies influence exposure to extreme versus moderate content.
2. User Behavior & Engagement – how individuals respond to content depending on alignment and private preferences.
3. Economic & Game-Theoretic Aspects – how engagement-driven incentives can lead to unintended outcomes, such as polarization and escalation of content intensity.
This is a joint project with professors from the University of Southern California (USC).
  
  
  
  Objectives:
This UROP project aims to explore recommender systems by focusing on three interconnected areas:
1. Algorithmic Foundations – how recommendation strategies influence exposure to extreme versus moderate content.
2. User Behavior & Engagement – how individuals respond to content depending on alignment and private preferences.
3. Economic & Game-Theoretic Aspects – how engagement-driven incentives can lead to unintended outcomes, such as polarization and escalation of content intensity.
This is a joint project with professors from the University of Southern California (USC).
      
  
  
  1. Simulation & Modeling: Build simplified game-theoretic or agent-based models of user–platform interactions, exploring equilibrium dynamics.
2. Data Analysis: Use small-scale datasets (e.g., Reddit, Twitter/X, or simulated feeds) to study recommendation patterns and engagement responses.
3. Theoretical Exploration: Extend the signaling game framework to test how assumptions (e.g., user heterogeneity, platform objectives) affect equilibrium outcomes.
  2. Data Analysis: Use small-scale datasets (e.g., Reddit, Twitter/X, or simulated feeds) to study recommendation patterns and engagement responses.
3. Theoretical Exploration: Extend the signaling game framework to test how assumptions (e.g., user heterogeneity, platform objectives) affect equilibrium outcomes.
      
  
  
  Students should have:
1. Basic programming (Python, MATLAB, or R) - recommended
2. Probability/statistics background - recommended
3. Interest in digital media, algorithms, and game theory - required
  1. Basic programming (Python, MATLAB, or R) - recommended
2. Probability/statistics background - recommended
3. Interest in digital media, algorithms, and game theory - required
      
  
  
  Our mentoring philosophy centers on empowering students to gain confidence in their ideas and nurturing their creativity. At the MINDS lab, we embrace the motto that there is no limit to what the human mind can accomplish and that the world of ideas offers an infinite number of low-hanging fruits. Currently, our lab has supported a diverse group of researchers, including many undergraduates, two PhD students, and one postdoc.  We believe that a diverse team, representing a wide spectrum of backgrounds and perspectives, leads to more innovative work, thereby contributing to the broadening of participation of underrepresented groups in the scientific community and society as a whole.
  
  
  
  
      
  
  
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  2025
  
  
  
  
      
  
  
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