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Essays on the Ideology of Private Interests, Campaign Spending, and Legislative Agenda Control
Thieme, Sebastian ProQuest Dissertations & Theses New York Universit 2018 해외박사(DDOD)
This dissertation makes use of rules in three American states (Iowa, Nebraska, and Wisconsin) that require or permit groups to disclose their positions on bills on which they lobby the state legislature. I use these disclosures to estimate the p.
Making Sense of a Complex World: A Data-Driven Approach
Thiem, Thomas N Princeton University ProQuest Dissertations & Thes 2022 해외박사(DDOD)
In this dissertation we develop a suite of data-driven modeling techniques for dynamical systems by leveraging manifold learning, dimensionality reduction, and deep learning methodologies. We apply these techniques to a variety of systems including coupled oscillators, agent-based systems, and optimization and root-finding algorithms. With each system we demonstrate a different facet of the techniques.Beginning with coupled oscillator systems in chapter 1, we describe how manifold learning can be used to tackle the challenge of finding coarse descriptions of collective dynamics. We then present a specialized neural network architecture that allows us to learn the dynamics of coarse variables directly from time series. Finally, we leverage manifold learning once again to find reduced sets of parameters, the "effective" parameters, for multi-parameter coupled oscillator models.Moving on to heterogeneous agent-based models in chapter 2, we present techniques for deriving data-driven surrogate models in the presence of low-dimensional dynamics. We present a global approach, that constructs a basis from spatiotemporal data and learns a system of ODEs in the basis coordinates, and a local approach, that learns a PDE in heterogeneity space. We discuss the implications of an agent-based PDE and illustrate a subsampling approach. In chapter 3 we utilize manifold learning to extend our agent-based PDE approach to cases in which the heterogeneities are unknown. We present examples of this modification for both one and two-dimensional data-driven spaces.We conclude by examining algorithms from the Koopman point of view in chapter 4. We discuss how employing the Koopman framework proves beneficial for the analysis and acceleration of algorithms and showcase the approach with examples including both the gradient descent and Nesterov optimization algorithms and the Newton-Raphson root-finding algorithm.As the world continues to become ever more saturated in data new analysis techniques will be required to extract meaningful information. We believe that our data-driven modeling techniques offer a step toward making sense of a data-driven future.