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Essays on political economy [electronic resource]

This dissertation consists of three chapters. The first chapter, Predictability and Power in Legislative Bargaining, is a joint paper with Nageeb Ali and B. Douglas Bernheim. This paper examines the effect of the predictability of recognition processes on the concentration of political power in legislative bargaining. For a broad class of legislative bargaining games, we identify a mild predictability condition on the recognition rule, requiring an ability to rule out some minimum number of legislators as the next proposer, under which Markovian equilibria deliver all economic surplus to the first proposer. When legislators can be nearly certain that the next proposer belongs to a class of the requisite size, the first proposer receives nearly all of the surplus. The second chapter, Designing Checks and Balances, is a joint paper with Tiberiu Dragu and James Kuklinski. We study the design of checks and balances institutions when two (or more) parties need to agree on changing an existing policy in environments with single-peaked preferences. We characterize a class of checks and balances rules that satisfy strategy-proofness and Pareto efficiency and show that there is a unique rule that satisfy these properties. It consists of implementing the ideal policy of the more moderate player, that is, the player that prefers the less aggressive change from the policy status-quo. We study the implications of our analysis for constitutional review. The third chapter is Regulation and Liabilities. I develop a game theoretic model of the interaction among a firm, a regulatory agency and a consumer group. I use this framework to analyze the effect of allowing product liability compensation on the incentives and utility of the firm. I show that the firm's incentive to produce a quality product increases in the level of the damage compensation awarded through tort claims; however, the firm's utility may increase or decrease in the level of tort claim compensation.

Multi-dimensional urban sensing using crowdsensing data

In smart cities, the indispensable devices used in peoples daily lives, such as smartphones, smartwatches, vehicles, and smart buildings, are equipped with more and more sensors. For example, most smartphones now have cameras, GPS, acceleration and light sensors. Leveraging the massive sensing data produced by users common devices for large-scale, fine-grained sensing in smart cities is referred to as the urban crowdsensing. It can enable applications that are beneficial to a broad range of urban services, including traffic, wireless communication service (4G/5G), and environmental protection. In this book, we provide an overview of our recent research progress on urban crowdsensing. Unlike the extant literature, we focus on multi-dimensional urban sensing using crowdsensing data. Specifically, the book explores how to utilize crowdsensing to see smart cities in terms of three-dimensional fundamental issues, including how to incentivize users participation, how to recommend tasks, and how to transmit the massive sensing data. We propose a number of mechanisms and algorithms to address these important issues, which are key to utilizing the crowdsensing data for realizing urban applications. Moreover, we present how to exploit this available crowdsensing data to see smart cities through three-dimensional applications, including urban pollution monitoring, traffic volume prediction, and urban airborne sensing. More importantly, this book explores using buildings sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation. Given its scope, the book will be of particular interest to researchers, students, practicing professionals, and urban planners. Furthermore, it can serve as a primer, introducing beginners to mobile crowdsensing in smart cities and helping them understand how to collect and exploit crowdsensing data for various urban applications.

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