This article uses the work of David Hume, J. S. Mill and Émile Durkheim to trace the ambiguous and difficult relations between the concepts of the moral and the social within the development of Sociology. The works of Hume and Mill will be examined in terms of their respective attempts to develop a Moral Science, and the limitations and legacy of their respective approaches will be analyzed. The article will then demonstrate how Durkheim signals a shift away from a notion of the “moral sciences” based on an analysis of human nature to the invocation of the realm of the social which operates at the level of the group or collective. The moral becomes a matter of what is considered “obligatory” within a society. The article suggests that the difference between the moral [la morale] and morality [moralité] has been lost in English translations of Durkheim’s texts and that a recognition of how he deployed this distinction throughout his work sheds new light on his theoretical position. By asking whether Sociology is a Moral Science, it is possible to shed new light both on the specific history and development of Sociology, and the article points to the importance for Sociology and sociologists of renewing their engagement with matters of the moral. The article will suggest that an ongoing consideration of the relation between systematic knowledge and what ought to be done is core to the project of what Sociology is, or could be. It is in this sense that Sociology is (or should be) a moral science.
We present an LLM-mediated role-playing game that supports reflection on socialization, moral responsibility, and educational role positioning. Grounded in socialization theory, the game follows a four-season structure in which players guide a child prince through morally charged situations and compare the LLM-mediated NPC's differentiated responses across stages, helping them reason about how educational guidance shifts with socialization. To approximate real educational contexts and reduce score-chasing, the system hides real-time evaluative scores and provides delayed, end-of-stage growth feedback as reflective prompts. We conducted a user study (N=12) with gameplay logs and post-game interviews, analyzed via reflexive thematic analysis. Findings show how players negotiated responsibility and role positioning, and reveal an entry-load tension between open-ended expression and sustained engagement. We contribute design knowledge on translating sociological models of socialization into reflective AI-mediated game systems.
Abstract Research on the socioeconomic outcomes of migrants and their children in destination societies has long been a central focus for sociologists and economists worldwide. However, this body of work is shaped by two dominant approaches. First, most studies focus on South-North migration; second, they often compare migrants with natives in destination countries. Building on the growing multi-sited and dissimilation approaches, this study uses large-scale harmonized census microdata to enhance our understanding of migration outcomes by comparing migrants across both southern and northern destinations, as well as comparing migrants to natives in their countries of origin. The study examines the labour market outcomes of two South American migrant groups—Bolivian and Peruvian—who have emigrated to two key destinations: Argentina and Spain. Three key takeaways emerge. First, migration can reshape women’s relationship to the labour market. Second, not all migration results in an occupational status downgrade, contrary to expectations from classic assimilation theories. Third, although South–North moves may involve greater legal and cultural challenges, they do not necessarily entail greater labour market disadvantages or lower returns to education compared to South–South moves.
Michelle Nashla Turcios, Alicia E. Boyd, Angela D. R. Smith
et al.
Machine learning (ML) has become a go-to solution for improving how we use, experience, and interact with technology (and the world around us). Unfortunately, studies have repeatedly shown that machine learning technologies may not provide adequate support for societal identities and experiences. Intersectionality is a sociological framework that provides a mechanism for explicitly considering complex social identities, focusing on social justice and power. While the framework of intersectionality can support the development of technologies that acknowledge and support all members of society, it has been adopted and adapted in ways that are not always true to its foundations, thereby weakening its potential for impact. To support the appropriate adoption and use of intersectionality for more equitable technological outcomes, we amplify the foundational intersectionality scholarship--Crenshaw, Combahee, and Collins (three C's), to create a socially relevant preliminary framework in developing machine-learning solutions. We use this framework to evaluate and report on the (mis)alignments of intersectionality application in machine learning literature.
Workplace toxicity is widely recognized as detrimental to organizational culture, yet quantifying its direct impact on operational efficiency remains methodologically challenging due to the ethical and practical difficulties of reproducing conflict in human subjects. This study leverages Large Language Model (LLM) based Multi-Agent Systems to simulate 1-on-1 adversarial debates, creating a controlled "sociological sandbox". We employ a Monte Carlo method to simulate hundrets of discussions, measuring the convergence time (defined as the number of arguments required to reach a conclusion) between a baseline control group and treatment groups involving agents with "toxic" system prompts. Our results demonstrate a statistically significant increase of approximately 25\% in the duration of conversations involving toxic participants. We propose that this "latency of toxicity" serves as a proxy for financial damage in corporate and academic settings. Furthermore, we demonstrate that agent-based modeling provides a reproducible, ethical alternative to human-subject research for measuring the mechanics of social friction.
Large language models have been used to simulate human society using multi-agent systems. Most current social simulation research emphasizes interactive behaviors in fixed environments, ignoring information opacity, relationship variability, and diffusion diversity. In this paper, we first propose a general framework for exploring multi-agent information diffusion. We identified LLMs' deficiency in the perception and utilization of social relationships, as well as diverse actions. Then, we designed a dynamic attention mechanism to help agents allocate attention to different information, addressing the limitations of the LLM attention mechanism. Agents start by responding to external information stimuli within a five-agent group, increasing group size and forming information circles while developing relationships and sharing information. Additionally, we explore the information diffusion features in the asymmetric open environment by observing the evolution of information gaps, diffusion patterns, and the accumulation of social capital, which are closely linked to psychological, sociological, and communication theories.
Large language models (LLMs) increasingly serve as human-like decision-making agents in social science and applied settings. These LLM-agents are typically assigned human-like characters and placed in real-life contexts. However, how these characters and contexts shape an LLM's behavior remains underexplored. This study proposes and tests methods for probing, quantifying, and modifying an LLM's internal representations in a Dictator Game, a classic behavioral experiment on fairness and prosocial behavior. We extract ``vectors of variable variations'' (e.g., ``male'' to ``female'') from the LLM's internal state. Manipulating these vectors during the model's inference can substantially alter how those variables relate to the model's decision-making. This approach offers a principled way to study and regulate how social concepts can be encoded and engineered within transformer-based models, with implications for alignment, debiasing, and designing AI agents for social simulations in both academic and commercial applications, strengthening sociological theory and measurement.
Katja Petrowski, Laura Bindila, Benedict Herhaus
et al.
Abstract The present study investigates the concentrations of the endocannabinoids under standardized psychosocial stress induction (TSST) and a resting condition in healthy males. Hereby, all endocannabinoids were analyzed under a standardized laboratory procedure (arachidonic acid (AA), arachidonoylethanolamide (AEA), isomeres 2-AG arachidonoylglycerol (2-AG) and palitoylethanolamide (PEA)). A total of n = 32 healthy controls (HC) were included in the study. The participants were exposed to the Trier Social Stress Test (TSST) for reliable laboratory stress induction and under rest. Blood samples were taken during the TSST by an intravenous catheter to examine the endocannabinoid (eCB) stress response. There were no significant differences in baseline levels of the parameters between the TSST and the resting condition (p´s > 0.28). ANOVA results indicated a significant effect of time over the six measurements points in all parameters. In the parameter 2-AG and AA a strongly, and in AEA a slightly, significant effect of condition*time could be unveiled. In conclusion, the present study showed that acute psychosocial stress increases plasma endocannabinoids. Further research is required to evaluate the endocannabinoid system in different anxiety disorders to elucidate which patients might benefit from eCB-based therapy.
Carmengelys Cordova, Joaquin Taverner, Elena Del Val
et al.
Multi-agent systems (MAS) have gained relevance in the field of artificial intelligence by offering tools for modelling complex environments where autonomous agents interact to achieve common or individual goals. In these systems, norms emerge as a fundamental component to regulate the behaviour of agents, promoting cooperation, coordination and conflict resolution. This article presents a systematic review, following the PRISMA method, on the emergence of norms in MAS, exploring the main mechanisms and factors that influence this process. Sociological, structural, emotional and cognitive aspects that facilitate the creation, propagation and reinforcement of norms are addressed. The findings highlight the crucial role of social network topology, as well as the importance of emotions and shared values in the adoption and maintenance of norms. Furthermore, opportunities are identified for future research that more explicitly integrates emotional and ethical dynamics in the design of adaptive normative systems. This work provides a comprehensive overview of the current state of research on norm emergence in MAS, serving as a basis for advancing the development of more efficient and flexible systems in artificial and real-world contexts.
Giovanni Mauro, Nicola Pedreschi, Renaud Lambiotte
et al.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.
Over the past decade, an explosion in the availability of education-related datasets has enabled new computational research in education. Much of this work has investigated digital traces of online learners in order to better understand and optimize their cognitive learning processes. Yet cognitive learning on digital platforms does not equal education. Instead, education is an inherently social, cultural, economic, and political process manifesting in physical spaces, and educational outcomes are influenced by many factors that precede and shape the cognitive learning process. Many of these are social factors like children's connections to schools (including teachers, counselors, and role models), parents and families, and the broader neighborhoods in which they live. In this article, we briefly discuss recent studies of learning through large-scale digital platforms, but largely focus on those exploring sociological aspects of education. We believe computational social scientists can creatively advance this emerging research frontier-and in doing so, help facilitate more equitable educational and life outcomes.
The effect that different police protest management methods have on protesters' physical and mental trauma is still not well understood and is a matter of debate. In this paper, we take a two-pronged approach to gain insight into this issue. First, we perform statistical analysis on time series data of protests provided by ACLED and spanning the period of time from January 1, 2020, until March 13, 2021. We observe that the use of kinetic impact projectiles is associated with more protests in subsequent days and is also a better predictor of the number of deaths in subsequent deaths than the number of protests, concluding that the use of non-lethal weapons seems to have an inflammatory rather than suppressive effect on protests. Next, we provide a mathematical framework to model modern, but well-established psychological and sociological research on compliance theory and crowd dynamics. Our results show that understanding the heterogeneity of the crowd is key for protests that lead to a reduction of social tension and minimization of physical and mental trauma in protesters.
The regions are the product of regional studies that researchers exchange in their studies in the natural and human sciences of geography, where the applications of the regions have extended in many sciences, such as sociology, economics, soil, planning, soil, plant environmental sciences until a special science of the region emerged called (regional science). The idea of division entered the field of social planning such as planning and development regions with the aim of achieving regional justice between cities. The applications of the regions varied among researchers according to the goal and need for them. They were distinguished by the stability of the natural regions and the mobility of the human regions. To achieve this, the research relied on the descriptive and analytical methods as well as the desk survey.
Received 6/10/2023
, Accepted 26/11/2023
, Published 31/12/2023
Андрей Владимирович Резаев, Наталья Дамировна Трегубова
Статья рассматривает два примера информационных технологий ― API и ChatGPT, ― стремительно набравших популярность в последние несколько лет. Авторы описывают каждый из кейсов и анализируют, какие проблемы распространение этих технологий ставит перед социальными учеными. Отдельно рассматривается влияние ChatGPT и сходных технологий на сферу образования в рамках более общей тенденции ― развития универсальных технологий искусственного интеллекта. В заключении обсуждается взаимосвязь между рассмотренными технологиями, которая определяется развитием онлайн-культуры и взаимозависимости «человек — алгоритм». Авторы формулируют тезис о необходимости перехода к новым исследовательским вопросам об искусственной социальности в рамках социальной аналитики и иллюстрируют его на примере анализа использования ChatGPT в разных обществах и культурах.
Благодарность. Исследование выполнено при финансовой поддержке РФФИ и Министерства по науке и технологиям Тайваня в рамках научного проекта № 21-511-52002. Авторы выражают искреннюю благодарность двум анонимным рецензентам за внимательное отношение к работе, ценные замечания и рекомендации.
Residential segregation in metropolitan areas is a phenomenon that can be observed all over the world. Recently, this was investigated via game-theoretic models. There, selfish agents of two types are equipped with a monotone utility function that ensures higher utility if an agent has more same-type neighbors. The agents strategically choose their location on a given graph that serves as residential area to maximize their utility. However, sociological polls suggest that real-world agents are actually favoring mixed-type neighborhoods, and hence should be modeled via non-monotone utility functions. To address this, we study Swap Schelling Games with single-peaked utility functions. Our main finding is that tolerance, i.e., agents favoring fifty-fifty neighborhoods or being in the minority, is necessary for equilibrium existence on almost regular or bipartite graphs. Regarding the quality of equilibria, we derive (almost) tight bounds on the Price of Anarchy and the Price of Stability. In particular, we show that the latter is constant on bipartite and almost regular graphs.
Opinion formation is one of the most fascinating phenomena observed in human communities, and the ability to predict and to control the dynamics of this process is interesting from the theoretical as well as practical point of view. Although there are many sophisticated models of opinion formation, they often lack the connection with real life data, and there are still sociological processes that need to be explained. To address this, we propose a model describing the dynamics of opinion formation which mimics the process of the virus or disease spreading in the population. The introduced model is motivated by the model of disease spread with three possible channels - direct contact, indirect contact, and contact with "contaminated" elements. We demonstrate that the presence of "contaminated" elements, which in the case of on-line communities can be represented as the content published on the Internet, has considerable impact on the process of opinion formation. We argue that by using a simple mechanism of opinion spreading via passive elements, the introduced model captures the meaningful elements of opinion formation in complex communities. The presented work provides a step towards formulating universal laws governing social as well as physical or technical systems.
En este artículo se presentan los resultados de una tesis doctoral que analizó las prácticas discursivas que inciden en la vivencia de la democracia en la escuela, según el punto de vista de estudiantes de educación media, buscando identificar las prácticas cotidianas que estos consideran democráticas, así como comprender las concepciones que tienen sobre política, ciudadanía y democracia. Desde un enfoque cualitativo, fundamentado en la Investigación Acción, se reconocieron como prácticas democráticas la expresión de la pluralidad, la participación, el respeto, la existencia de procedimientos electorales transparentes y la necesidad de un cambio en la organización de las escuelas.
Javier Arroyo, Javier Arroyo, Javier Arroyo
et al.
Optimal controllers can enhance buildings’ energy efficiency by taking forecast and uncertainties into account (e.g., weather and occupancy). This practice results in energy savings by making better use of energy systems within the buildings. Even though the benefits of advanced optimal controllers have been demonstrated in several research studies and some demonstration cases, the adoption of these techniques in the built environment remains somewhat limited. One of the main reasons is that these novel control algorithms continue to be evaluated individually. This hampers the identification of best practices to deploy optimal control widely in the building sector. This paper implements and compares variations of model predictive control (MPC), reinforcement learning (RL), and reinforced model predictive control (RL-MPC) in the same optimal control problem for building energy management. Particularly, variations of the controllers’ hyperparameters like the control step, the prediction horizon, the state-action spaces, the learning algorithm, or the network architecture of the value function are investigated. The building optimization testing (BOPTEST) framework is used as the simulation benchmark to carry out the study as it offers standardized testing scenarios. The results reveal that, contrary to what is stated in previous literature, model-free RL approaches poorly perform when tested in building environments with realistic system dynamics. Even when a model is available and simulation-based RL can be implemented, MPC outperforms RL for an equivalent formulation of the optimal control problem. The performance gap between both controllers reduces when using the RL-MPC algorithm that merges elements from both families of methods.
Engineering (General). Civil engineering (General), City planning
Emilia Klein Malacarne, Rodrigo Ghiringhelli de Azevedo
Por meio de um estudo empírico comparativo, a presente pesquisa propõe-se a comparar as práticas judiciais para apuração de autoria de ato infracional e os discursos legitimadores em Porto Alegre (RS) e no Rio de Janeiro (RJ). Verificou-se, em ambos os locais, um abismo entre teoria e prática. Percebeu-se que o sistema de justiça juvenil fluminense não confere maiores garantias aos adolescentes, em comparação com o gaúcho. Observou-se a permanência de resquícios da lógica tutelar nas decisões judiciais e nas manifestações dos atores processuais em sentido contrário às conquistas do Estatuto da Criança e do Adolescente (ECA).
Social history and conditions. Social problems. Social reform, Sociology (General)
Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.