As Large Language Models (LLM) based multi-agent systems become increasingly prevalent, the collective behaviors, e.g., collective intelligence, of such artificial communities have drawn growing attention. This work aims to answer a fundamental question: How does diversity of values shape the collective behavior of AI communities? Using naturalistic value elicitation grounded in the prevalent Schwartz's Theory of Basic Human Values, we constructed multi-agent simulations where communities with varying numbers of agents engaged in open-ended interactions and constitution formation. The results show that value diversity enhances value stability, fosters emergent behaviors, and brings more creative principles developed by the agents themselves without external guidance. However, these effects also show diminishing returns: extreme heterogeneity induces instability. This work positions value diversity as a new axis of future AI capability, bridging AI ability and sociological studies of institutional emergence.
Delphi studies have established themselves in the health sciences as a means to systematically and, ideally, synthesize expert opinions into a consensus on concrete issues. As participatory health research increases in relevance, lifeworld experts (e.g., patients, caregiving relatives) are being included in Delphi surveys and their opinions placed alongside those of professional and scientific experts. Looking at the theory and methodology, we discuss the opportunities and challenges concerning result quality and derive practical implications for conducting Delphi studies involving lifeworld experts alongside scientific and/or professional experts.Delphi techniques are understood here to be social interaction processes whose outcomes are a result of the participating experts’ conscious, cognitive judgment processes, and also shaped by individual, situational and cultural factors. The more heterogeneous the expert panel, in particular when lifeworld experts are participating, the more these influences vary. Expert panel composition and how diversity is handled prove significant to Delphi study results.Our argument is based on an in-depth analysis of a systematic review of Delphi studies with lifeworld experts. We found that the inclusion of lifeworld experts in Delphi studies usually occurs relatively unsystematically and, furthermore, that results are not analysed separately according to expert group, although there would be good reasons for this. We have oriented the reporting here on PRISMA. To enhance the outcomes of Delphi studies that incorporate lifeworld experts alongside scientific and/or professional experts, we put forward specific recommendations that address potential biases arising from the participation of lifeworld experts.
This case study relates to the Women and Law in Southern Africa – Zambia (WLSA – Zambia), an NGO with its headquarters in Lusaka. This organisation is engaged in fighting strenuously and successfully against discrimination, inequalities and gender violence, mainly resulting from the persistence of customary laws and its practices which sometimes jeopardises women and delays their empowerment and emancipation. Since in Zambia, as well as in many other African countries, land is still the primary source of wealth and livelihood, WLSA-Zambia and its members have been highlighting the significant legal, social and political problems caused by of gender inequalities in accessing land. Accordingly, they demand remedial legal reforms, develop citizens’ legal awareness and support women’s struggle to secure land rights. By presenting individual experiences and different viewpoints, and adopting a wealth of qualitative methodologies, my research work is a contribution, in anthropological perspective, to a better understanding of the multiple ways in which gender and law can interact.
This insight paper examines embodied interaction in interactive art, focusing on body embodiment, bodily sensation (i.e., somaesthetic), and audience-artwork interaction. The authors propose a new three-dimensional descriptive model of interactive art based on literature and apply to analyze a curated corpus of 49 award-winning artworks from the Prix Ars Electronica between 2009 and 2023. The analysis reveals emergent patterns of interactive art that deepen the understanding of interactive art from an embodied perspective and prepare the ground for future research and art practices. This paper has discovered that embodied interaction remains under-explored in interactive art rather than an inflation of interactivity. Notable research gaps persist in exploring virtual embodiment within sociocultural contexts using immersive technologies. Furthermore, it also underscores the need to revisit the sociological and etymological roots of interaction to enhance interpersonality and relationality and advocates for a paradigm shift in future research and practice in interactive art.
Lorenzo Betti, Paolo Bajardi, Gianmarco De Francisci Morales
The interaction between social norms and gender roles prescribes gender-specific behaviors that influence moral judgments. Here, we study how moral judgments are biased by the gender of the protagonist of a story. Using data from r/AITA, a Reddit community with 17 million members who share first-hand experiences seeking community judgment on their behavior, we employ machine learning techniques to match stories describing similar situations that differ only by the protagonist's gender. We find no direct causal effect of the protagonist's gender on the received moral judgments, except for stories about ``friendship and relationships'', where male protagonists receive more negative judgments. Our findings complement existing correlational studies and suggest that gender roles may exert greater influence in specific social contexts. These results have implications for understanding sociological constructs and highlight potential biases in data used to train large language models.
Chloé Arson, Dominic Balog-Way, Koenraad Beckers
et al.
To review the lessons learnt from recent deep geothermal case studies and plan strategically the research, development, regulation, and communication work required for the implementation of an Enhanced Geothermal System (EGS) at Cornell University, a group of engineers and scholars convened a two-day workshop on the Ithaca campus, on October 23-24, 2024. The event was funded by Cornell Atkinson Center for Sustainability. This report is a summary of the content of the presentations and discussions that took place during the workshop. The first section focuses on philosophical, sociological, economic, and regulatory questions posed by EGS deployment as a means to mitigate climate change. The second section tackles the scientific and technological research areas associated with EGS. The third section aims to assess the feasibility of developing EGS for heat direct use at Cornell University, based on results and information available to date. The report concludes with a summary of the most salient technological and scientific breakthroughs, and a plan for future technological and academic engagement in EGS projects at Cornell.
Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo
et al.
The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance constraints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization problem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered.
This paper proposes a social regulation model for dynamic adaptation according to user characteristics in virtual interactive environments, namely the tribal theater model. The model focuses on organizational regulation and builds an interaction scheme with more resilient user performance by improving the subjectivity of the user. This paper discusses the sociological theoretical basis of this model and how it was migrated to an engineering implementation of a virtual interactive environment. The model defines user interactions within a field that are regulated by a matrix through the allocation of resources. To verify the effectiveness of the tribal theater model, we designed an experimental scene using a chatroom as an example. We trained the matrix as an AI model using a temporal transformer and compared it with an interaction field with different levels of control. The experimental results showed that the tribal theater model can improve users' interactive experience, enhance resilient user performance, and effectively complete environmental interaction tasks under rule-based interaction.
The study of population dynamics originated with early sociological works but has since extended into many fields, including biology, epidemiology, evolutionary game theory, and economics. Most studies on population dynamics focus on the problem of prediction rather than control. Existing mathematical models for control in population dynamics are often restricted to specific, noise-free dynamics, while real-world population changes can be complex and adversarial. To address this gap, we propose a new framework based on the paradigm of online control. We first characterize a set of linear dynamical systems that can naturally model evolving populations. We then give an efficient gradient-based controller for these systems, with near-optimal regret bounds with respect to a broad class of linear policies. Our empirical evaluations demonstrate the effectiveness of the proposed algorithm for control in population dynamics even for non-linear models such as SIR and replicator dynamics.
Peer review is fundamental to the integrity and advancement of scientific publication. Traditional methods of peer review analyses often rely on exploration and statistics of existing peer review data, which do not adequately address the multivariate nature of the process, account for the latent variables, and are further constrained by privacy concerns due to the sensitive nature of the data. We introduce AgentReview, the first large language model (LLM) based peer review simulation framework, which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. Our study reveals significant insights, including a notable 37.1% variation in paper decisions due to reviewers' biases, supported by sociological theories such as the social influence theory, altruism fatigue, and authority bias. We believe that this study could offer valuable insights to improve the design of peer review mechanisms. Our code is available at https://github.com/Ahren09/AgentReview.
Katharina Luttenberger, Beat Baggenstos, Charbel Najem
et al.
Abstract Background Mental health and psychosocial support (MHPSS) is increasingly considered vital for addressing the needs of displaced communities. The mental health of young people in Lebanon, including members of the host community and refugees, has been severely affected by multiple crises. Physical activity (PA) is an effective means for enhancing mental health, but evidence of PA’s impact on mental health among forcibly displaced populations is still emerging and often varies widely across studies. Method In this waitlist-controlled study, we examined the effectiveness of an 8-week psychosocial bouldering group intervention offered by the nonprofit organization ClimbAID on psychological well-being, distress, self-efficacy, and social cohesion in a group of mostly Syrian refugee adolescents residing in the Bekaa Valley, Lebanon. The intervention consisted of 8 sessions and took place once a week for 2 h in a group of up to 12 adolescents with 2 trained facilitators and up to 2 volunteers, supervised by a climbing instructor and a social worker. Multilevel analyses were performed for all outcomes. Results 233 people were included in the study. The dropout rate was approximately 33%. The IG improved significantly more than the waitlist group in terms of overall mental well-being and psychological distress. Group allocation was a significant predictor of improvements in mental well-being and psychological distress and showed a trend toward predicting self-efficacy. There was no positive impact of the intervention on social cohesion. Conclusions Even in complex humanitarian settings of forced displacement, a psychosocial bouldering intervention reduces psychological distress and increases well-being in a mixed group of host and refugee youth in Bekaa, Lebanon. Trial registration Prospectively registered with ISRCTN 13005983, registered April 1st, 2022.
Special situations and conditions, Medical emergencies. Critical care. Intensive care. First aid
Black young adults participate in activism to challenge and transform oppressive systems. In this qualitative study, we employed thematic analysis and used the framework of sociopolitical development (SPD) to explore their motivations and challenges to participation amid the COVID-19 pandemic and the summer of 2020 in the United States. Semi-structured interviews with 22 Black young adults in early 2022 revealed that social identities, sense of legacy, impact, and morals drove their participation. Further, contending with systemic oppression, impact, harm, and working with others challenged their participation. This study holds valuable insights for stakeholders as they support and empower young Black activists navigating social justice efforts in our dynamic and evolving sociopolitical landscape. Further, this work highlights the enduring tradition of activism within the Black community and emphasizes the need to empower young Black activists as change agents in the pursuit of a more equitable society.
A singular attribute of humankind is our ability to undertake novel, cooperative behavior, or teamwork. This requires that we can communicate goals, plans, and ideas between the brains of individuals to create shared intentionality. Using the information processing model of David Marr, I derive necessary characteristics of basic mechanisms to enable shared intentionality between prelinguistic computational agents and indicate how these could be implemented in present-day AI-based robots. More speculatively, I suggest the mechanisms derived by this thought experiment apply to humans and extend to provide explanations for human rationality and aspects of intentional and phenomenal consciousness that accord with observation. This yields what I call the Shared Intentionality First Theory (SIFT) for rationality and consciousness. The significance of shared intentionality has been recognized and advocated previously, but typically from a sociological or behavioral point of view. SIFT complements prior work by applying a computer science perspective to the underlying mechanisms.
George-Rafael Domenikos, Tyler Laurie, Sahar Awaji
et al.
This work investigates a modified Schelling model within the scope and aims of Social Physics. The main purpose is to see if how the concepts of potential and kinetic energy can be represented within a computational sociological system. A monetary value is assigned to all the agents in the Monetary Schelling model and a set of dynamics for how the money is spent upon agent position changes and gradual loss. The introduction of the potential and kinetic energy allows for the entropy to be calculated based upon the distribution of the agent energies and as well as the internal energy of the system at each time point. The results show how the movements of the agents produce identity satisfactions with their neighbors decreasing the internal energy of the system along with the decay in the monetary holdings. Simulations are run where agents are provided monetary values at fixed intervals and this causes a subset of the agents to mobilize and explore new positions for satisfaction and increases the entropy with the internal energy removing the system from the fixed point.
Introduction: This study investigated the influence of indoor lighting environments on paper reading efficiency and brain fatigue to explore lighting parameters that benefit users during various reading durations.Methods: The study was conducted in the Smart Lighting Lab, where 12 participants were tested under different illuminance levels and correlated color temperatures (CCT) for three distinct reading durations. Reading efficiency during the task tests and objective measures of brain activity by monitoring participants’ electroencephalograms (EEGs) were used as key factors to assess participants’ fatigue levels.Results: By analyzing the subjective and objective results, we found that paper reading efficiency was significantly affected by changes in the lighting environment. Also, based on the results of this study, we propose lighting recommendations for paper reading tasks of different durations. For a 15 min reading task, the lighting condition of 500 lux-6,500 K were the most efficient for reading; for a 30 min reading task, 500 lux-4,000 K lighting environments were found to be the most effective; and 750 lux-6,500 K was the best lighting environment for a 60 min reading duration.Discussion: These suggestions can serve as a reference for designing indoor lighting environment. In addition, they provide guidance to researchers and reviewers conducting similar studies.
Engineering (General). Civil engineering (General), City planning
A key challenge in Machine Learning is class imbalance, where the sample size of some classes (majority classes) are much higher than that of the other classes (minority classes). If we were to train a classifier directly on imbalanced data, it is more likely for the classifier to predict a new sample as one of the majority classes. In the extreme case, the classifier could completely ignore the minority classes. This could have serious sociological implications in healthcare, as the minority classes are usually the disease classes (e.g., death or positive clinical test result). In this paper, we introduce a software that uses Generative Adversarial Networks to oversample the minority classes so as to improve downstream classification. To the best of our knowledge, this is the first tool that allows multi-class classification (where the target can have an arbitrary number of classes). The code of the tool is publicly available in our github repository (https://github.com/yuxiaohuang/research/tree/master/gwu/working/cigan/code).
In romantic relationships, three forms of infidelity exist: emotional (flirting, developing feelings, etc.), physical (touching, hand-holding, etc.), and sexual (intercourse; Spitzburg and Cupach 203). However, individuals hold differing perceptions of infidelity in a romantic relationship based on past life experiences, perceptions of the relationship, relational quality and among others. In dyadic romantic relationships, cheating occurs when one partner seeks attention/affection from someone outside the partnership. With the advent of new communication technologies, sexting (i.e. the sharing of sexually suggestive photos/messages through mobile devices) has become part of the dating process (Lenhart 1). Yet research suggests sexting outside of a committed relationship has negative implications for relationship quality (Wysocki and Childers 217). Our research explores sexting in Consensual Non-Monogamy (CNM) relationships and what constitutes “crossing the line” when sexting outside of the CNM relationship occurs. Furthermore, our research also explores what may motivate us as individuals in CNM relationships to sext outside of their relationship? Using an open-ended questionnaire, our study furthers sexting research by highlighting individuals' experiences and exploring emergent themes regarding the nature of sexting in CNM relationships.