Thesis advisor: Uzi Segal / This collection of papers examines applications of microeconomic theory to practical problems. More specifically, I identify frictions between theoretical results and agent behavior. I seek to resolve these tensions by either proposing mechanisms to more closely capture the theoretical environment of interest or extending the model to more closely approximate the world as individuals perceive it. In the first chapter, "Compensation without Distortion,'' I propose a new mechanism for compensating subjects in preference elicitation experiments. The motivation for this tool is the theoretical problem of incentive compatibility in decision experiments. A hallmark of experimental economics is the connection between a subject's payment with their actions or decisions, however previous literature has highlighted shortcomings in this link between compensation and methods currently used to elicit beliefs. Specifically, compensating individuals based on choices they make increases reliability, however these payments can themselves distort subjects' preferences, limiting the resulting data's usefulness. I reexamine the source of the underlying theoretical tension, and propose using a stochastic termination mechanism called the "random stopping procedure'' (RSP). I show that the RSP is theoretically able to structurally avoid preference distortions induced by the current state of the art protocols. Changing the underlying context subjects answer questions—by resolving payoff uncertainty immediately after every decision is made—the assumed impossibility of asking multiple questions without creating preference distortions is theoretically resolved. To test this prediction, I conduct an experiment explicitly designed to test the accuracy of data gathered by the RSP against the current best practice for measuring subject preferences. Results show that RSP-elicited preferences more closely match a control group's responses than the alternative. In the second chapter, "School Choice and Class Size Externalities,'' I revisit the many-to-one matching problem of school choice. I focus on the importance of problem definition, and argue that the "standard'' school choice model is insufficiently sensitive to relevant characteristics of student preferences. Motivated by the observation that students care about both the school they attend, and how over- or under-crowded the school is, I extend the problem definition to allow students to report preferences over both schools and cohort sizes. (Cohort size is intended as a generalization of school crowding, relative resources, or other similar school characteristics.) I show that, if students do have preferences over schools and cohort sizes, current mechanisms lose many of their advantageous properties, and are no longer stable, fair, nor non-wasteful. Moreover, I show that current mechanisms no longer necessarily incentivize students to truthfully report their preferences over school orderings. Motivated by the observation that students care about both the school they attend, and how over- or under-crowded the school is, in "School Choice and Class Size Externalities'' I extend the standard school choice problem to incorporate both of these elements. I show that, if students do have preferences over schools and cohort sizes, current mechanisms are no longer stable, fair, nor non-wasteful. In response, I construct an alternative matching mechanism, called the deferred acceptance with voluntary withdrawals (DAwVW) mechanism, which improves on the underlying (unobserved) manipulability of "standard" mechanisms. The DAwVW mechanism is deterministic and terminates, more closely satisfies core desirable matching properties, and can yield substantial efficiency gains compared to mechanisms that do not consider class size. In the third chapter, I provide an overview of the history of decision experiments in economics, describe several of the underlying tensions that motivate my other projects, and identify alternative potential solutions that have been proposed by others to these problems. In this project, I add context to the larger field of experimental economics in which my research is situated. In addition to the mechanisms discussed by prior literature reviews, I incorporate and discuss recently developed payment and elicitation methods, and identify these new approaches' advantages and drawbacks. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20435号 / 農博第2220号 / 新制||農||1049(附属図書館) / 学位論文||H29||N5056(農学部図書室) / 京都大学大学院農学研究科生物資源経済学専攻 / (主査)教授 栗山 浩一, 教授 福井 清一, 教授 伊藤 順一 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

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In this work, we will show a result derived from a class of economically inspired multi-agent differential games. The result shows that in the class of games we present, only a sparse cooperation structure is needed by the agents to achieve an outcome that is in a particular sense "optimal". One may expect that to achieve such an outcome, a centralized control or full cooperation would be necessary; it is not. Not only is it not necessary - it is never necessary. We precisely characterize what kind of cooperation IS necessary to attain that kind of optimality. Only the sparsest kind of nonzero cooperation is ever necessary to achieve this optimal outcome. We call these structures Maximally Sparse Distributed Monopoloies

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Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir's secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design.