UC Berkeley

As artificial intelligence and autonomous agents become primary actors in modern economic systems, the dynamics of market interactions have shifted from human-led deliberation to high-frequency, data-driven algorithmic competition. This dissertation investigates the theoretical foundations and practical design of robust algorithmic ecosystems, organized around three core pillars: robust mechanism design, reliable decision-making algorithms, and strategic data governance. Part I addresses the vulnerability of classical auctions to strategic manipulation. We introduce the VCG-Posted Price (V-PoP) mechanism, which uses a collusion detection oracle to maintain incentive compatibility and efficiency in the presence of bid manipulation through coordination. We establish welfare and revenue guarantees, demonstrating how side information can be leveraged to improve on classical mechanisms. Part II focuses on the challenge of building computationally efficient and reliable decision-making algorithms in complex environments. We first propose an exact iterative algorithm for continuous trilevel optimization, a framework essential for modeling hierarchical interactions like defender-attacker-defender games. By exploiting the parametric structure of KKT conditions, we observe finite-time convergence to the optima in our numerical experiments. Additionally, we develop a tensor completion algorithm using a gauge norm over the polytope of rank-1 tensors. This approach achieves the information-theoretic sampling rate while maintaining computational scalability, which we validate using numerical experiments on large-scale energy storage data. Part III explores the role of strategic players in data sharing and governance. We analyze the economic viability of data dividends, identifying the regulatory conditions under which platforms are incentivized to compensate users for their data and invest in privacy protection. Finally, we examine partial coordination among electric vehicle (EV) charging stations, providing analytical conditions where coalition formation may paradoxically harm participants or social welfare. Together, these contributions build on techniques from mechanism design and game theory, optimization, and statistics to provide a framework for designing algorithmic systems that are efficient, equitable, and resilient to the strategic complexities of the modern digital economy.