Title: Algorithmic Decision Making with Imperfect Information and Practical Irrationality
Abstract: Market algorithms are ubiquitous in modern life, both online and offline. Historically, markets have been fundamental in matching supply with demand, where designers optimize regulations with global goals and participants optimize strategies with individual goals. Decision theory and mechanism design have evolved to study and prescribe the behavior and structure of these markets. This thesis addresses key questions at the intersection of market design and algorithmic decision-making: How do strategic decision-makers navigate choices, and how do resource producers allocate limited resources among strategic participants?
The advent of information technology and increased data availability has revolutionized markets, enabling unprecedented scale, efficiency, and control. This development allows mechanism designers to create or modify market conditions, provided we understand their impact on the designer's objectives. In this thesis, we explore market algorithms in dynamic environments and under practical irrationality, analyzing how deviations from ideal models affect the utility of both designers and participants. We examine environmental changes such as market imbalance, resource augmentation, and competition faced by dominant sellers. For participants, we consider agents who are computationally bounded, behaviorally biased, or using learning algorithms. Our work aims to provide insights into the robustness and adaptability of market algorithms amid practical complexities and diverse participant behaviors.