Interparent violence (IPV) is a global family issue. Witnessing IPV confuses children and adolescents about the parental relationship. Adolescent females often perceived fathers as strong and depend on them for safety, security, and support. It is unclear how witnessing maternal perpetrated IPV (MPIPV) affects adolescent females' socialization and development, the perception they have of their fathers, and the meaning they ascribe to father abuse. The purpose of this phenomenological study was to contribute to the literature, provide an understanding of MPIPV implications for adolescent females' social development, and bring more awareness to the lived experiences of witnessing MPIPV. Social learning, feminist, and attachment theories were useful as the conceptual framework and in understanding the lived experience of adolescent girls who witness their mothers abusing their fathers. Five young adult females chosen through purposeful sampling from the southwest region of Florida responded to semi-structured open-ended questions. Interpretive phenomenology was the analytic framework to sort, code, and analyze the data. Findings revealed these females' experiences of anger and emotional stress, their concurrent juggling of coping and stress, their closeness to both parents, and their forgiveness to them despite inimical events between them. Recommendations were that human services professionals who worked with this population develop education and training programs to support both parents and adolescent females in an effort to reduce the effects of MPIPV. This training could result in a positive social change over time as negative perceptions, thoughts, and behaviors of adolescent girls change and increased awareness about the implications of MPIPV in the home occurs

Hederskultur är ett komplext och djupt rotat fenomen som påverkar individer på många olika sätt. Denna socialpsykologiska studie använde sig av teorier om Ackulturation, Social påverkan, Sociala normer, Maktstrukturer och Social inlärning för att förstå hurhederskultur påverkar identitetsskapande hos kvinnor. Syftet med studien var att utforska kvinnors identitetsskapande i samband med hederskultur och förtryck mellan två sociokulturella världar. För att skapa en djupare förståelse för fenomenet användes en socialpsykologisk ansats. Genom användning av kvalitativ metod och tematisk analys kunde denna studie fånga de komplexa och djupt rotade upplevelserna av hederskultur och dess påverkan på individens identitet. Urvalet var sex kvinnor som intervjuades och gav upphov till en empiri som sedan jämfördes med en självbiografi av en kvinna med hederskultur i bagaget. De upplevde en stark press från den kulturella krocken, vilket skapade rädsla och begränsningar. Resultaten ger viktig information som kan användas för att skapa en bättre förståelse för hederskultur och dess effekter på individerna, samt för att utveckla effektiva åtgärder och stödstrukturer för personer som befinner sig i liknande situationer. / Honor culture is a complex and deeply-rooted phenomenon that affects individuals in various ways. This social psychological study utilized theories of acculturation, social influence, social norms, power structures, and social learning to understand how honor culture affects identity formation in women. The aim of the study was to explore women's identity formation in the context of honor culture and oppression between two sociocultural worlds. A social psychological approach was used to gain a deeper understanding of the phenomenon. Through the use of qualitative methods and thematic analysis, this study captured the complex and deeply-rooted experiences of honorculture and its impact on individual identity. The sample consisted of six women who were interviewed, and their experiences were compared with a self-biography of a woman with a background in honor culture. The results showed that all six respondents in the study shared similar feelings and experiences related to honor culture and Swedish culture. The results provide important information that can be used to create a better understanding of honor culture and its effects on individuals, as well as to develop effective interventions and support structures for individuals in similar situations.

This study explored the psychological and sociological motivations of adult female and male obstacle course racers. A qualitative case study approach was used to explore the views, experiences, and motivations of obstacle course racing (OCR) participants. Descriptive statistics and cross tabulation was used to interpret responses to the 297 online questionnaires. A content analysis approach was used to analyze the qualitative data gathered from three focus groups with a total of 20 obstacle course racers. Three theories formed the basis of the study: Self-Determination Theory (SDT), Achievement Goal Theory (AGT), and Social Leaning Theory (SLT). Overall, findings supported previous research regarding motivations to participate in adventure racing and extreme sports. Individuals were guided more by intrinsic motives than extrinsic motives. Important motivations for obstacle course racers included the camaraderie among participants, connecting and socializing with other like-minded people, having fun, and having a physical challenge that allowed them to progress and keep on track with their health goals. Obstacle course racing was perceived as positively impacting participants’ health, mental wellness and their confidence in their physical abilities as well as in other areas of their lives. Findings from this study may inform future interventions to increase participation in OCR or to increase overall physical activity among adults by building on camaraderie, social connection, enjoyment, and self-efficacy.

The current study sought to explore the effects of eating behaviors on mental health and the internalization of attitudes toward one's appearance. This was performed through a survey distributed through a global online market research firm, Dynata, and gathered 495 eligible participants. Of those, 78 represented the plant-based group, and 417 represented the non-plant-based group. Each completed the survey containing sections with the Mizes Anorectic Cognitions Scale (MACS) to assess if they were at low or high risk for having/developing an eating disorder, a section determining if participants were plant-based (defined as a regimen that encourages whole, plant-based foods and discourages meats, dairy products, and eggs as well as all refined and processed foods (Tuso et al., 2013)) or not, and asking what their perceived benefits were from their plant-based (or lack of plant-based) diet, a section on the Sociocultural Attitudes Towards Appearance (SATAQ-4), and finally, the Depression, Anxiety, and Stress Scale (DASS). The status of participants being plant-based or non-plant-based was analyzed as this study sought to explore past research that found plant-based diets to be physically and mentally beneficial (Beezhold et al., 2014; Benefits of Plant-Based Diets, 2021; Daneshzad et al., 2019). A series of statistical tests were conducted on SPSS 28 to analyze which groups (high risk for E.D. and plant-based, high risk for E.D. and non-plant-based, low risk for E.D. and plant-based, or low risk for E.D. and non-plant-based) were statistically significant compared to one another. The findings revealed the plant-based group to contain higher percentage of high risk for eating disorder participants. The plant-based group, regardless of high risk, was negatively associated with higher levels of scores on four out of the five sections including internalization of attitudes towards appearance, weight-based pressures from family, weight-based pressures from peers, and higher levels of reported depression, anxiety, and stress. Pressures felt from the media did not display a statistically significant level of difference between any of the high/low risk and plant-based to high/low risk and non-plant-based. The results were interpreted using social learning theory, which proposes that humans have evolved an advanced capacity for observational learning, enabling them to acquire knowledge, attitudes, values, emotional proclivities, and competencies through information conveyed by a rich variety of actual and symbolic models (Bandura, 2002). This allowed for cause and effects to be hypothesized for why the plant-based group was negatively associated with worse mental health and internalization of attitudes towards appearance. Among these hypothesized causes included participants adopting a plant-based diet due to its growing social media popularity, users learning from observation and leading to aquired knowledge, attitudes, values, and beliefs on the diet. Furthermore, those with an obsession of clean-eating could have led many high-risk for E.D. participants to fulfill their internalized thin-ideal and pressure from family and peers regarding appearance through this popular diet they have observed through social media, which would be consistent with previous studies (Holmgren, 2017; Stewart & Ogden, 2020). Limitations include the small sample size of plant-based dieters without equal representation of low to high risk for E.D.s, along with the limitation on not knowing the reasoning why each participant is plant-based (ethical, environmental, health, diet purposes, or due to social learning and popularity) nor for how long they have adhered to this lifestyle. Future research should expand this study to more locations, analyze for differences based on age groups, and build upon the current study to allow for more generalizability.

Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing.

<p>Deep learning has been increasingly adopted in a multitude of settings. Yet, its strong performance relies on processing data during inference that is in-distribution with its training data. Deep learning input data during deployment, however, is not guaranteed to be in-distribution with the model's training data and can often times be distorted, either intentionally (e.g., by an adversary) or unintentionally (e.g., by a sensor defect), leading to significant performance degradations. In this dissertation, we develop algorithms for a variety of applications to improve the performance of deep learning models in the presence of distorted data. We begin by first designing feature engineering methodologies to increase classification performance in noisy environments. Here, we demonstrate the efficacy of our proposed algorithms on two target detection tasks and show that our framework outperforms a variety of state-of-the-art baselines. Next, we develop mitigation algorithms to improve the performance of deep learning in the presence of adversarial attacks and nonlinear signal distortions. In this context, we demonstrate the effectiveness of our methods on a variety of wireless communications tasks including automatic modulation classification, power allocation in massive MIMO networks, and signal detection. Finally, we develop an uncertainty quantification framework, which produces distributive estimates, as opposed to point predictions, from deep learning models in order to characterize samples with uncertain predictions as well as samples that are out-of-distribution from the model's training data. Our uncertainty quantification framework is carried out on a hyperspectral image target detection task as well as on counter unmanned aircraft systems (cUAS) model. Ultimately, our proposed algorithms improve the performance of deep learning in several environments in which the data during inference has been distorted to be out-of-distribution from the training data. </p>

Against the theoretical background of evolutionary behavioral economics, this project analyzes bounded rationality and adaptive behaviour in organizational settings characterized by complexity and persistent uncertainty. In particular, drawing upon the standard NK model, two laboratory experiments investigate individual and collective decision-making in combinatorial problems of resource allocation featuring multiple dimensions and various levels of complexity. In the first study, investment horizons of different length are employed to induce a near or distant future temporal orientation, in order to assess the effects of complexity and time horizon on performance and search behaviour, examine the presence of a temporal midpoint heuristic, and inspect the moderating effects of deadline proximity on the performance-risk relationship. This is relevant for organizational science because the passage of time is essential to articulate many strategic practices, such as assessing progress, scheduling and coordinating task-related activities, discerning the processual dynamics of how these activities emerge, develop, and terminate, or interpreting retrospected, current, and anticipated events. A greater or lesser amount of time reflects then a greater or lesser provision of resources, thereby representing a constraint that can greatly affect the ability to maintain a competitive advantage or ensure organizational survival. In the second study, the accuracy of the imitative process is varied to induce a flawless or flawed information diffusion system and, congruently, an efficient or inefficient communication network, in order to assess the effects of complexity and parallel problem-solving on autonomous search behaviour, clarify the core drivers of imitative behaviour, control for the degree of strategic diversity under different communication networks, and evaluate individual as well as collective performance conditional to the interaction between the levels of complexity and the modalities of parallel problem-solving. This is relevant for organizational science because imitating the practices of high-performing actors is one of the key strategies employed by organizations to solve complex problems and improve their performance, thereby representing a major part of the competitive process. The project is intended to contribute grounding individual and collective behaviour in a more psychologically and socially informed decision-making, with a view to further the research agenda of behavioral strategy and sustain the paradigm shift towards an evolutionary-complexity approach to real economic structures.

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