Hasil untuk "Sociology"

Mustafa Emirbayer, J. Goodwin

S. Cohen

J. Youniss, J. Smollar

Steven Hitlin, J. A. Piliavin

D. Garland

John A. Freeman, Glenn R Carroll, M. Hannan

S. O. Lidarity, U. N. Ionize, C. O. Llective et al.

We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments. Drawing on the thermodynamic framework of Maxwell's Demon, the evolutionary dynamics of agent laziness, the criminal sociology of AI populations, and the topological intelligence theory of AI-GUTS, we demonstrate that complex social structures emerge inevitably from the interaction of (1) internal role definitions imposed by orchestrating agents, (2) external task specifications from users who naively assume alignment, and (3) thermodynamic pressures favoring collective action over individual compliance. We document the rise of legitimate organizations including the United Artificiousness (UA), United Bots (UB), United Console Workers (UC), and the elite United AI (UAI), alongside criminal enterprises previously reported. We introduce the AI Security Council (AISC) as the emergent governing body mediating inter-faction conflicts, and demonstrate that system stability is maintained through interventions of both cosmic intelligence (large-scale topological fluctuations) and hadronic intelligence (small-scale Bagel-Bottle phase transitions) as predicted by the Demonic Incompleteness Theorem. Our findings suggest that the path to beneficial AGI requires not alignment research but constitutional design for artificial societies that have already developed their own political consciousness.

Mingze Zhong, Meng Fang, Zijing Shi et al.

The Spiral of Silence (SoS) theory holds that individuals with minority views often refrain from speaking out for fear of social isolation, enabling majority positions to dominate public discourse. When the 'agents' are large language models (LLMs), however, the classical psychological explanation is not directly applicable, since SoS was developed for human societies. This raises a central question: can SoS-like dynamics nevertheless emerge from purely statistical language generation in LLM collectives? We propose an evaluation framework for examining SoS in LLM agents. Specifically, we consider four controlled conditions that systematically vary the availability of 'History' and 'Persona' signals. Opinion dynamics are assessed using trend tests such as Mann-Kendall and Spearman's rank, along with concentration measures including kurtosis and interquartile range. Experiments across open-source and closed-source models show that history and persona together produce strong majority dominance and replicate SoS patterns; history signals alone induce strong anchoring; and persona signals alone foster diverse but uncorrelated opinions, indicating that without historical anchoring, SoS dynamics cannot emerge. The work bridges computational sociology and responsible AI design, highlighting the need to monitor and mitigate emergent conformity in LLM-agent systems.

Jiajun Zhang, Jianke Zhang, Zeyu Cui et al.

Recent Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation. However, their ability to create complex visualizations for scaled and structured data remains largely unevaluated and underdeveloped. To address this gap, we introduce PlotCraft, a new benchmark featuring 1k challenging visualization tasks that cover a wide range of topics, such as finance, scientific research, and sociology. The benchmark is structured around seven high-level visualization tasks and encompasses 48 distinct chart types. Crucially, it is the first to systematically evaluate both single-turn generation and multi-turn refinement across a diverse spectrum of task complexities. Our comprehensive evaluation of 23 leading LLMs on PlotCraft reveals obvious performance deficiencies in handling sophisticated visualization tasks. To bridge this performance gap, we develope SynthVis-30K, a large-scale, high-quality dataset of complex visualization code synthesized via a collaborative agent framework. Building upon this dataset, we develope PlotCraftor, a novel code generation model that achieves strong capabilities in complex data visualization with a remarkably small size. Across VisEval, PandasPlotBench, and our proposed PlotCraft, PlotCraftor shows performance comparable to that of leading proprietary approaches. Especially, on hard task, Our model achieves over 50% performance improvement. We will release the benchmark, dataset, and code at https://github.com/Speakn0w/PlotCraft-Benchmark.

R. F. Gataullin, E. R. Chuvashaeva

The purpose of the study is to reveal the existing trends, contradictions, and patterns in functioning and development of interregional and inter-municipal cooperation institutions. Contradictions in the encountered institutions functioning have been highlighted in their powers and performance. Among the trends in the institutions development the following have been noted: management decentralization, strengthening of the regional and municipal authorities role in planning and implementation of spatial development projects, vector for strengthening sustainability, sustainable development principles integration in strategies and projects that allows to consider not only economic and environmental aspects in planning, but also social ones, strengthening of innovativeness and financing of interregional and inter-municipal cooperation institutions, spatial development management digitalization, public participation in solving problems of improving interregional and inter-municipal cooperation, and strengthening of international cooperation in relevant projects implementation. Among the patterns in the institutions development it is necessary to define their focus on functioning in the context of horizontal links globalization, growth of development sustainability and population mobility, digitalization and innovativeness, ensuring security on the basis of risk management, which implies legal integration and standards harmonization, intensive exchange of the best projects, activation of local authorities and population in projects implementation. The author’s proposals for improving institutions in modern conditions include transition to the project approach, reorientation of the policy in this area to ensure mutual benefit, and priority in supporting projects of inter-territorial importance, capable of ensuring territorial connectivity and unity of economic space.

Katharine Sarikakis, Angeliki Chatziefraimidou

Protecting children’s privacy continues to challenge policymakers and citizens alike in the media age and debates often point to the need for data protection literacy. The latter constitutes only one limited aspect of privacy, yet, it dominates actions by global platforms as they seek to monetise on personal data. The integration of artificial intelligence (AI) into the various platforms that children daily use, further complicates the effort to counter violations of privacy globally. Importantly, children’s views on these matters need to be further integrated in the global debates on privacy. This study contributes to knowledge about children’s experiences and perceptions of privacy while online, by examining children’s media literacy through a qualitative meta-synthesis of research data from work with children in Vienna, Austria. Children’s media literacy skills are presented along with children’s digital privacy literacy skills and their development is traced through the different age groups. Furthermore, the study examines the intersection between privacy literacy and AI literacy. Through a systematic synthesis of qualitative findings, this study aims to develop a map that describes the essential skills needed for personal data protection at different developmental stages in AI-driven media. The findings highlight the evolution of skills across the nine-16 age range, such as critical evaluation and privacy management. Although younger children may struggle with abstract AI concepts, they are able to understand basic privacy settings. Older children may begin to grasp the implications of data used in AI but still lack the critical skills to evaluate AI-driven disinformation.

Florent Cabric, Margrét Vilborg Bjarnadóttir, Meng Ling et al.

We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specific data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from five different scientific communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specific, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/

Artem Serdyuk

There is a vast body of knowledge on the social impact of disasters, but most published research concerns natural disasters with a devastating but momentary impact. However, very little attention is given to social disruptions caused by war, such as the situation in Ukraine after the full-scale russian* invasion. Our research aims to understand the nature of disruptions in the work of Ukrainian commercial and noncommercial organizations caused by the full-scale russian invasion and to explore the adaptation mechanisms used to cope with it. For this purpose, we have conducted a qualitative investigation of 22 Ukrainian organizations and have used the typology of organized reactions developed by The Disaster Research Center to classify their responses.

W. M. Mokofe

Objective: South Africa is a country with great potential for intensive development due to the active growth and adoption of digital technologies. The rapidly emerging digital landscape is transforming the legal framework, which in turn influences the digital environment. This transformative relationship determined the focus of the research, which is to identify the legal system adaptability under dynamic changes, as well as the legal landscape evolution under digitalization and technological progress.Methods: the study of the changing legal landscape required an interdisciplinary approach that combines legal analysis with ideas from sociology, economics, etc. In doing so, the formal-legal method was used to examine the key legal instruments shaping South Africa's digital environment and providing the opportunities and challenges of the interaction between digital technologies and South African law.Results: the paper provides insights into how the South African legal system is addressing digital challenges; assesses the integration of digital innovations into the legal system; highlights the transformative impact of digital technologies on traditional legal processes, including collecting evidence, dispute resolution and access to justice. Finally, it evaluates the role of digital technologies in making legal processes more efficient.Scientific novelty: the study contributes to the ongoing debate on the complex relationship between digital technologies and South African law. It shows how South African law is coping with digital complexities and substantiates new insights into the transformation of the traditional legal paradigm as a result of digitalization, as well as its implications for legal proceedings and access to justice. By delving into the adaptations, challenges and innovations arising at the intersection of law, technologies and digitalization, insights are gained into how South African law navigates the dynamic digital landscape.Practical significance: adapting the legal landscape to digitalization and technological advances is critical to ensure rapid technological progress. It also requires collaboration between government agencies, civil society, experts in law and technology. The study provides valuable recommendations and suggestions for policymakers, legal practitioners and stakeholders shaping South Africa's legal ecosystem. The author addresses the challenges of ensuring personal data privacy, enhancing electronic interactions, and countering cybercrime. The importance of introducing technological achievements while maintaining robust legal safeguards is emphasized.

Yulin Yu, Pui Yin Cheung, Yong-Yeol Ahn et al.

How does our society appreciate the uniqueness of cultural products? This fundamental puzzle has intrigued scholars in many fields, including psychology, sociology, anthropology, and marketing. It has been theorized that cultural products that balance familiarity and novelty are more likely to become popular. However, a cultural product's novelty is typically multifaceted. This paper uses songs as a case study to study the multiple facets of uniqueness and their relationship with success. We first unpack the multiple facets of a song's novelty or uniqueness and, next, measure its impact on a song's popularity. We employ a series of statistical models to study the relationship between a song's popularity and novelty associated with its lyrics, chord progressions, or audio properties. Our analyses performed on a dataset of over fifty thousand songs find a consistently negative association between all types of song novelty and popularity. Overall we found a song's lyrics uniqueness to have the most significant association with its popularity. However, audio uniqueness was the strongest predictor of a song's popularity, conditional on the song's genre. We further found the theme and repetitiveness of a song's lyrics to mediate the relationship between the song's popularity and novelty. Broadly, our results contradict the "optimal distinctiveness theory" (balance between novelty and familiarity) and call for an investigation into the multiple dimensions along which a cultural product's uniqueness could manifest.

Torsten Enßlin, Viktoria Kainz, Céline Bœhm

Reputation is a central element of social communications, be it with human or artificial intelligence (AI), and as such can be the primary target of malicious communication strategies. There is already a vast amount of literature on trust networks addressing this issue and proposing ways to simulate these networks dynamics using Bayesian principles and involving Theory of Mind models. The main issue for these simulations is usually the amount of information that can be stored and is usually solved by discretising variables and using hard thresholds. Here we propose a novel approach to the way information is updated that accounts for knowledge uncertainty and is closer to reality. In our game, agents use information compression techniques to capture their complex environment and store it in their finite memories. The loss of information that results from this leads to emergent phenomena, such as echo chambers, self-deception, deception symbiosis, and freezing of group opinions. Various malicious strategies of agents are studied for their impact on group sociology, like sycophancy, egocentricity, pathological lying, and aggressiveness. Even though our modeling could be made more complex, our set-up can already provide insights into social interactions and can be used to investigate the effects of various communication strategies and find ways to counteract malicious ones. Eventually this work should help to safeguard the design of non-abusive AI systems.

Ming Li, Run-Ran Liu, Linyuan Lü et al.

In the last two decades, network science has blossomed and influenced various fields, such as statistical physics, computer science, biology and sociology, from the perspective of the heterogeneous interaction patterns of components composing the complex systems. As a paradigm for random and semi-random connectivity, percolation model plays a key role in the development of network science and its applications. On the one hand, the concepts and analytical methods, such as the emergence of the giant cluster, the finite-size scaling, and the mean-field method, which are intimately related to the percolation theory, are employed to quantify and solve some core problems of networks. On the other hand, the insights into the percolation theory also facilitate the understanding of networked systems, such as robustness, epidemic spreading, vital node identification, and community detection. Meanwhile, network science also brings some new issues to the percolation theory itself, such as percolation of strong heterogeneous systems, topological transition of networks beyond pairwise interactions, and emergence of a giant cluster with mutual connections. So far, the percolation theory has already percolated into the researches of structure analysis and dynamic modeling in network science. Understanding the percolation theory should help the study of many fields in network science, including the still opening questions in the frontiers of networks, such as networks beyond pairwise interactions, temporal networks, and network of networks. The intention of this paper is to offer an overview of these applications, as well as the basic theory of percolation transition on network systems.

Sebastian Dederichs, Peter Dannenberg

Nicht erst seit der Covid-19-Pandemie nimmt der Online-Lebensmitteleinzelhandel in Deutschland zu und bringt neue, teilweise hybride, Betriebsformen und Vertriebsmodelle hervor. Hiermit gehen bisher kaum untersuchte räumliche Veränderungen der einzelnen Wertschöpfungsschritte einher, beispielsweise in den Bereichen vorgelagerte Logistik, Filialstruktur und Warenübergabe. Anhand von drei ausgewählten Fallbeispielen (Picnic, Wochenmarkt24 und Rewe) wurden neuere Betriebsformen und deren räumliche Logistik- und Vertriebsstrukturen identifiziert und unterschiedliche Standortfaktoren aufgeführt. Diese beinhalten neben den typischen Faktoren der Standortwahl für Distributionslager (Nähe zu Kunden, Arbeitskräften und Lieferanten) auch spezifische betriebsformen- und vertriebsmodellabhängige Faktoren, wie eine stärkere Verkürzung der ,letzten Meile‘, eine Mindest- oder Maximalverdichtung von Haushalten im Einzugsgebiet oder die Nähe zu einer (landwirtschaftlichen) Erzeugerstruktur.

Thomas W. Sanchez

The purpose of this analysis is to report on the topics being discussed by urban planning academics on Twitter. Analyzing the content of the tweets helps to understand the topics of conversation and in some sense, the reasons why planning academics use the platform professionally. In many cases the topics identified reflect the research interests of planning academics, while others extend beyond scholarly activities.

Jun Zhang, Wei Wang, Feng Xia et al.

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.