Structural Incentives for Political Party Polarization

Politics is often thought of as a pie cut in half and split between republicans and democrats. A more accurate representation would be a pie cut into several uneven slices; most of the small pieces would go to the democrats and the few large slices would go to republicans. The existing literature on political parties indicates that parties are not mirror opposites of one another. Issue density is not uniform among the parties. Since the New Deal, democrats have pushed extensive policy from Social Security to new roads and dams. The trend to expand the scope of government policy continues today in the form of universal healthcare, combating global warming, and gay rights. The tendency of democrats to expand their policy agenda stems from the makeup of the party. Unlike republicans, the Democratic party is composed of a coalition of interest groups. Republicans, in contrast, can be described as ideological and have held consistent over time. Republicans are more easily thought of in terms of big ideological principles that include low taxes, defense, and family values. Republicans, being more ideological, have a few core tenants. Democrats, being a coalition of interest groups, have a wide and diverse set of principles but less support behind each issue area. Given two political parties, one with a smaller but deeper set of beliefs and a second with a wider and shallower set of beliefs, the group with a smaller number of principles will find it relatively more difficult to compromise. Since politicians are single-minded seekers of reelection, they try to capture a comfortable number of votes to become reelected; however, if the party with the smaller number of principles were to compromise on a single principle, they would risk losing a proportionally greater number of voters. For instance, let us assume two political parties ‘R’ and ‘D’. R holds two principles ‘1’ and ‘2’, while D holds principles ‘3’, ‘4’, ‘5’, ‘6’, and ‘7’. If R compromises on principle 2 to gain access to voters from principle area 3, they risk losing half of their voter base, assuming 1 and 2 contain equal numbers of voters who care deeply about that principle. Whereas if D compromises on principle 3 to gain access to voters from principle area 2 they risk only losing one-fifth of their voter base, assuming 3, 4, 5, 6, and 7 all contain equal numbers of voters who care deeply about that issue. Therefore, republicans are disincentivized from compromising while democrats have an incentive to work with republicans. Democrats compromise because they are likely to gain more votes from sacrificing small areas for a bigger traditionally republican area.Governmen

BIG PROBLEMS, SMALL SOLUTIONS A Solution to the Housing Crisis Through Prefabrication.

Coronavirus has come as America is in the midst of a crisis-level affordable housing issue. In 2019, 37.1 million households were identified as “housing cost-burdened,” spending 30% or more of their income on housing. 63% of Americans have been living pay-check to pay-check since the pandemic. And in addition to that, it has been estimated that we as a country have a deficit of at least 1,000,000 single-family homes. This is affordable housing as it stands today, with a huge percentage of Americans searching for permanent housing and unable to find any, and if they do, unable to afford it for long. It seems fitting, albeit terrifying, that an epicenter of these occurrences is so close to home. Atlanta’s populating has grown by almost 20% since 2010, yet between 2010 and 2018, only 3,301 units have been erected that could be classified as “affordable”. So with an influx of close to 100,000 people in the last ten years and with Atlanta’s percentage of cost-burdened households nearing 50%, there is an incredible lack of cost-effective housing in the city. Attempting to find a solution for fundamental problems of infrastructure seems daunting, especially when it appears radical legislation is the only way to change the system. But there is hope. Dozens of cities around the country, Atlanta included, have pledged to change some of the laws that prevent Affordable housing projects. However, even before the laws are changed, people’s perception of basic housing rights and what affordable housing really means needs to be amended. That is the end to which my thesis research has been focused. Establishing the need, and inspiring the solution. In the name of proposing a solution to the housing crisis, my thesis will present a model of modular design and efficient use of space that intends to alter the perception of what affordable housing mean

Dynamics of Essentially Unstable Nonlinear Waves

In this thesis we primarily consider the stability of traveling wave solutions to a modified Kuramoto-Sivashinsky Equation equation modeling nanoscale pattern formation and the St. Venant equations modeling shallow water flow down an inclined plane. Numerical evidence suggests that these equations have no unstable spectrum other than λ =0, however they both have unstable essential spectrum. This unstable essential spectrum manifests as a convecting, oscillating perturbation which grows to a certain size independent on the initial perturbation — precluding stability in the regular L^2(R) space. Exponentially weighted spaces are typically used to handle such instabilities, and in Theorem 5.7 we prove asymptotic orbital linear stability in such an exponentially weighted space. We also discuss difficulties with extending this to a nonlinear stability result. In Section 5.5 we discuss another way of obtaining stability, through ad-hoc periodic wave trains. Chapter 6 concerns the general problem of creating a spectral projection to project away unstable essential spectrum. We consider this problem in the context of spatially periodic-coefficient PDE by proposing a candidate spectral projection defined via the Bloch transform and showing that initial perturbations which activate a sufficiently unstable part of the essential spectrum lead to solutions which are not Lyapunov stable. We also extend these results to dissipative systems of conservation laws. Additional chapters of interest are Chapter 3 where we address finding the spectrum and Chapter 4 where we discuss the numerics which lead to many of the figures in this thesis

South Bay Fire Department: Back Deck

Local fire station, Station 15, in the community of Los Osos, was in need of a deck to be built in the rear of the station. There was public support, funding, and a desire for an upgrade. With the help of the firefighters and administrative staff at South Bay Fire Department, a 285 square foot structure was constructed to operate as a relaxing area for on duty staff to use between emergency calls. The plan creation, estimating, and building of the deck took approximately a year to complete. An estimated 305 man hours were spent collectively on the project, with great care given to the needs of the department and attention to detail. On June 11, 2016 the project was completed. The project took longer than scheduled and cost more than originally estimated

Can Attachment Style and Temperament Predict Personality Organization?

Otto Kernberg (1967) developed a psychoanalytic theory of personality organization in which he posited that all individuals operate on one of three levels of personality organization: neurotic, borderline, or psychotic. His theory was developmental in nature and based on the idea that our earliest experiences establish unconscious interpersonal patterns that persist throughout life.The current study examined whether attachment style (anxious or avoidant) and factors of temperament (negative affect, effortful control) would predict personality organization. In particular, we examined identity diffusion and use of primitive defenses as markers of personality functioning. Results revealed that anxious attachment, negative affect, and effortful control significantly predicted identity diffusion and use of primitive defenses. The clinical implications for these findings as well as potential future research directions are discussed

Questing and Defense Against Death Anxiety

In his seminal work The Denial of Death, Ernest Becker suggested that the primary motivation behind human behavior is a fear of dying. This claim has been operationalized into an empirically based theory entitled Terror Management Theory (TMT). TMT outlines how self-esteem and cultural worldviews play an important role in how humans manage death anxiety. One especially important cultural worldview is religion. TMT research suggests that religious beliefs help provide protection again death anxiety. Religious orientation research outlines three orientations to religion: extrinsic, intrinsic, and quest. In the present study, I investigate whether a quest-like state of mind may help buffer the effects of death anxiety

Machine Learning Techniques Applied to the Nevada Geothermal Play Fairway Analysis

This study introduces machine learning techniques to the Nevada geothermal play fairway analysis (PFA), which provided geothermal potential maps for 96,000 km2 of west-central to eastern Nevada. The motivation for this project is to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Great Basin region of Nevada. The previous PFA study succeeded in utilizing the weighted combination of various geologic, geophysical, and geochemical features, indicative of permeability and heat, to both generate detailed geothermal favorability maps and identify several new blind geothermal systems. However, the project faced key limitations and challenges, including robust statistical analyses for the estimation of weights of influence for individual parameters, some incomplete datasets, and a limited number of training sites.To mitigate these challenges, this study incorporates new data developments and innovative machine learning techniques. Data developments include new training data and translating both the play fairway datasets (original and enhanced) and newly available datasets into a form compatible with machine learning techniques. Following the evaluation of various supervised and unsupervised machine learning techniques with the available data, two primary approaches were selected based on their performance and functionality. These techniques include 1) supervised probabilistic Bayesian artificial neural networks to produce detailed geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to both identify spatial patterns in geologic and geophysical feature sets and create new combined feature inputs.The comparative analyses of two principal sets of geological and geophysical input features highlight the potential that machine learning techniques offer to improve on the PFA. The analysis of feature set one, which comprises a set of regional permeability and heat data, illustrates a promising design for supervised Bayesian neural networks modeling to improve on the original regional permeability modeling in the PFA. Results from this feature set are selected to organize spatial patterns for the major structural-hydrologic domains within the study area, including the Walker Lane, western Great Basin, central Nevada seismic belt, and carbonate aquifer. The analysis of feature set two, which includes the same regional feature layers as in feature set one with the addition of local permeability features, illustrates how a model design may find a balance between disparate data types to produce predictive favorability maps that yield similar results to the original fairway map from the PFA. Information presented in this study related to the spatial patterns of elevated geothermal potential may have promising implications for future geothermal exploration efforts in the Great Basin region of Nevada and beyond