This course explores the role of information in game-theoretic settings. Students are first introduced to classical Bayesian game theory and the frameworks of information design and persuasion. The course will then explore emerging directions such as the interplay between rational and subrational decision-making and endogenous information structures. Students will engage with recent research developments, including models of subrational behavior and emerging frameworks like Endogenous Bayesian Games. Through a combination of lectures and research discussions, students will critically assess state-of-the-art methodologies, identify open research questions, and develop their own models to explore game-theoretic phenomena. The course culminates in student-led research presentations and structured search proposals. Format: 10 Interactive 3-hours Lectures with ongoing student projects

VISITING PROFESSOR Philip N. Brown is an Assistant Professor in the Department of Computer Science at the University of Colorado Colorado Springs. Dr. Brown received the Bachelor of Science in Electrical Engineering in 2007 from Georgia Tech. He received the Master of Science in Electrical Engineering in 2015 from the University of Colorado at Boulder under the supervision of Jason R. Marden, where he was a recipient of the University of Colorado Chancellor’s Fellowship. He received the PhD in Electrical and Computer Engineering from the University of California, Santa Barbara under the supervision of Jason R. Marden. He received the 2018 CCDC Best PhD Thesis Award from UCSB, the Best Paper Award from GameNets 2021, and a 2023 AFOSR Young Investigator Program award. Philip is interested in the interactions between engineered and social systems. Week 1: Foundations and future directions L1: (Re)-Introduction to Game Theory: Motivating questions: transportation, mobility, and smart infrastructure; Refresher on classical game theory. L2: Games of incomplete information: Bayesian games; Information design/Bayesian persuasion; State of the art review. L3: Deficiencies in Existing Models: Case studies on model limitations; Understanding emerging research directions. Week 2: Principal-agent models L4: Contract Theory: General concepts; Classical results. L5: Bayesian Persuasion; Kamenica and Gentzkow; Classical results. L6: Information Design: Bergemann and Morris; Classical results. Week 3: New Work: Endogenous Bayesian games L7: Recent Results: Endogenous Bayesian Games; Introduce the EBG framework; Case study: V2V communication pathologies. L8: Research methodology: How to explore these?; Converting theoretical questions into implementable models; Writing precise, code-ready toy questions; Discussion of trade-offs in modeling choices; Hands-on: Structuring numerical experiments Week 4: New Research Directions: non-rational models L9: Recent Results: Subrational behavior models; Classic approaches: k-level, prospect theory Modern approaches: identifying key rationality assumptions. L10: Where do we go next: What are the right questions? How can we tell whether answers are impactful? This course will be strategic in stengthening the PhD students' competencies in the analysis and design of socio-technical systems. It will be particularly beneficial for PhD students in the Mathematical Sciences program as well as other PhD programs.

In presenza

Presentazione report scritto - Sviluppo di project work in team

P.D.1-1 - Febbraio