178 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    "They're the reason I am still here. Alive" : Experiences of online communities amongst young adults who are LGBTQ+.

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    A heteronormative culture in UK schools that normalises discrimination towards the LGBTQ+ community resulting in ongoing challenges to mental wellbeing and access to school is well documented. However, there is limited research exploring protective factors that young people who are LGBTQ+ employ. I adopted a critical realist positionality alongside a narrative methodology. I utilised Queer and transgender theories as a critical lens, which privileges the voices of individuals who are LGBTQ+ or resist cis/heteronormative ideals. I sought to explore the experiences of online communities amongst young adults who are LGBTQ+ during the period they attended an educational setting, considering the ecological systems that may impact a young person’s lived experiences. My hope was that participants would feel empowered through sharing their narratives, which may provide implications for educational professionals to better understand the role of online communities for young people who are LGBTQ+. I used the ‘Listening Guide’ to analyse the participant’s narratives (Gilligan, 2015). I identified four common themes across all three narratives; the use of specific labels, the conflict between how participants wanted to be viewed and were viewed, belonging, and the stigma present in engaging in online communities. My findings suggest that online communities provided a powerful source of acceptance, belonging and information when participants were experiencing isolation, fear and prejudice in educational settings. Having access to a secure sense of belonging and acceptance was highly valued by the participants. Implications for educational professionals are presented, relating to the identified common themes. There is a specific focus on consulting with young people who are LGBTQ+ alongside the need for systematic approaches to supporting young people who are LGBTQ+

    Fictional Practices of Spirituality I: Interactive Media

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    "Fictional Practices of Spirituality" provides critical insight into the implementation of belief, mysticism, religion, and spirituality into worlds of fiction, be it interactive or non-interactive. This first volume focuses on interactive, virtual worlds - may that be the digital realms of video games and VR applications or the imaginary spaces of life action role-playing and soul-searching practices. It features analyses of spirituality as gameplay facilitator, sacred spaces and architecture in video game geography, religion in video games and spiritual acts and their dramaturgic function in video games, tabletop, or LARP, among other topics. The contributors offer a first-time ever comprehensive overview of play-rites as spiritual incentives and playful spirituality in various medial incarnations

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    Essays in High Frequency Trading and Market Structure

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    High Frequency Trading (HFT) is the use of algorithmic trading technology to gain a speed advantage when operating in financial markets. The increasing gap between the fastest and the slowest players in financial markets raises questions around the efficiency of markets, the strategies players must use to trade effectively and the overall fairness of markets which regulators must maintain. This research explores markets affected by HFT activity from three perspectives. Firstly an updated microstructure model is proposed to allow for empirical exploration of current levels of noise in financial markets, this illustrates current noise levels are not disruptive to dominant trading strategies. Second, a ARCH type model is used to de-compose market data into a series of traders working price levels to demonstrate that in cases of suspected market abuse, regulators can assess the impact individual traders make on price even in fast markets. Finally, a review of various HFT control measures are examined in terms of effectiveness and in light of an ordoliberal benchmark of fairness. The work illustrates the extents to which HFT activity is not yet disruptive, but also shows where HFT can be a conduit for market abuse and provides a series of recommendations around use of circuit breakers, algorithmic governance standards and additional considerations where assets are dual listed in different countries

    Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization

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    We propose the first, to our knowledge, loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy standard non-convex stochastic optimization techniques for approximating Nash equilibria, resulting in novel algorithms with provable guarantees. We complement our theoretical analysis with experiments demonstrating that stochastic gradient descent can outperform previous state-of-the-art approaches

    Social Optimum Equilibrium Selection for Distributed Multi-Agent Optimization

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    We study the open question of how players learn to play a social optimum pure-strategy Nash equilibrium (PSNE) through repeated interactions in general-sum coordination games. A social optimum of a game is the stable Pareto-optimal state that provides a maximum return in the sum of all players' payoffs (social welfare) and always exists. We consider finite repeated games where each player only has access to its own utility (or payoff) function but is able to exchange information with other players. We develop a novel regret matching (RM) based algorithm for computing an efficient PSNE solution that could approach a desired Pareto-optimal outcome yielding the highest social welfare among all the attainable equilibria in the long run. Our proposed learning procedure follows the regret minimization framework but extends it in three major ways: (1) agents use global, instead of local, utility for calculating regrets, (2) each agent maintains a small and diminishing exploration probability in order to explore various PSNEs, and (3) agents stay with the actions that achieve the best global utility thus far, regardless of regrets. We prove that these three extensions enable the algorithm to select the stable social optimum equilibrium instead of converging to an arbitrary or cyclic equilibrium as in the conventional RM approach. We demonstrate the effectiveness of our approach through a set of applications in multi-agent distributed control, including a large-scale resource allocation game and a hard combinatorial task assignment problem for which no efficient (polynomial) solution exists.Comment: Appears at the 5th Games, Agents, and Incentives Workshop (GAIW 2023). Held as part of the Workshops at the AAMAS 2023 Conferenc

    ‘Born this way’? Prenatal exposure to testosterone may determine behavior in competition and conflict

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    Fetal exposure to sex hormones can have long lasting effects on human behavior. The second-to-fourth digit ratio (DR) is considered a putative marker for prenatal exposure to testosterone (vs. estrogens), with higher exposure resulting in lower DR. Even though testosterone is theoretically related to competition, the role of DR in human behavior is debated; and in situations such as bilateral conflict is unknown. We investigate this through a laboratory experiment using a repeated 2-person Tullock contest played in fixed same-gender pairs. Based on a previously obtained large sample of student subjects, we selectively invited participants to the laboratory if their right-hand DR was in the top (High-DR) or bottom (Low-DR) tercile for their gender. Unbeknownst to the subjects, we performed a controlled match of the DR types (Low-Low, Low-High, High-High). This novel methodology allows us to analyze the causal effect of DR on behavior for the first time in the literature. We find that Low-DR (vs. High-DR) males compete more aggressively regardless of the counterpart’s type. For females’ conflict behavior, the counterpart’s type matters more than the decision-maker’s type: Low-DRs are non-significantly more aggressive but everyone is more aggressive against High-DRs. Limitations due to sample size are discussed

    Social Networks and Health Inequalities

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    This open access book applies insights from the network perspective in health research to explain the reproduction of health inequalities. It discusses the extant literature in this field that strongly correlates differences in social status with health behaviours and outcomes, and add to this literature by providing a coherent theoretical explanation for the causes of these health inequalities. It also shows that much research is needed on the precise factors and the social and socio-psychological mechanisms that are at play in creating and cementing social inequalities in health behaviours. While social support and social relations have received considerable attention within social and behavioural science research on health inequalities, this book considers the whole network of interpersonal relations, structures and influence mechanisms. This is the perspective of the social network analytical approach which has recently gained much attention in health research. The chapters of this book cover state-of-the-art research, open research questions, and perspectives for future research. The book provides network analyses on health inequalities from the perspective of sociology, psychology, and public health and is of interest to a wide range of scholars, students and practitioners trying to understand how health inequalities are reproduced across generations
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