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The paper excavates Thomas Fararo’s works and argues that Fararo should be considered among the fathers of contemporary analytical sociology. He is a major thinker of “generativity,” a notion that he contributed to elaborate and that represents a landmark and unifying principle of his entire scientific production. Thus, the paper first documents that Fararo actually forged the idea of “generativity” in the early sixties by proposing, and explicitly defining, the concepts of “generating process” and “generating models”; It then shows the channel through which these notions traveled to contemporary analytical sociology; finally, the paper demonstrates that Fararo saw, earlier than others, the importance of discrete object-oriented simulations for the theoretical study of “generating models”. [ABSTRACT FROM AUTHOR]

This article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots, and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modeling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social, and cultural implications are discussed that are likely to shape future applications and regulation efforts for these technologies. [ABSTRACT FROM AUTHOR]

Humans participate in an immense variety of relationships with other persons and other entities: human and nonhuman, living and nonliving, tangible and intangible, real and imagined. Participation in relationships is considered a key benchmark of personhood. Some of these relationships, particularly friendships, involve close emotional attachments, and some friendships have been described since antiquity as spiritual in nature. Different types of friendship depend upon factors such as proximity, social formality, physical intimacy, information exchanged, and the costs and benefits of maintaining the relationship. There are time‐extended processes and narrative practices involved in forming and dissolving relationships. A question is raised how androids (hypothetical humanoid robots that people would accept as equals in society) can participate in friendships with humans and other entities. This article explores the space of friendships with the aim of formulating guidelines for a computational model that can make explicit the information processing requirements and step‐by‐step processes involved with participating in the many different types of friendships, including those known as spiritual friendships. [ABSTRACT FROM AUTHOR]

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements. [ABSTRACT FROM AUTHOR]

Copyright of BMS: Bulletin de Methodologie Sociologique (Sage Publications Ltd.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on the concept of neocybernetic systems theory, distinguishing it from actor-network theory (ANT), emphasizing that ANT revolves around networks while neocybernetic systems are self-producing and autonomous. Topics discussed include the history and evolution of cybernetics, its interdisciplinary nature, and its relevance to literary studies, particularly in terms of system-environment relationships and the impact of cybernetic thinking on literature and society.

The article focuses on the intersection of literary studies and cybernetics, highlighting how scholars have increasingly explored cybernetic concepts in literature, including recursion, self-reference, and self-organization. It discusses the interdisciplinary nature of cybernetics and its relevance to understanding literary texts in a systems context.

The article focuses on the intersection of cybernetics and literary studies, particularly the impact of large language models like GPT-3, GPT-4, and ChatGPT on reading, writing, and human identity. Topics discussed include the capabilities of these language models, their potential to generate human-like texts, and the challenges they pose to the fields of literature and meaning-making.

Computational social science provides an innovative set of methodological tools that can help answer questions of substantive interest to migration and integration research. In this introductory article, we first provide a brief history of how computational approaches have already enriched migration and integration research. Second, we identify several key promises of computational migration research (e.g. better access to hard-to-reach populations, cost reductions and time savings, better detection of causal mechanisms, avoidance of response biases and methodological nationalism through fine-grained, time-stamped, live digital trace data) and key challenges (e.g. missing categories, sampling issues, ethical concerns). Third, we illustrate how the contributions of this special issue fulfil some of these promises – as well as deal with the challenges – to gain new insights into key questions of migration and integration research that address why people emigrate, what the evolution of structural patterns in migration networks is, whether refugee movements can be predicted, how host communities respond to the influx of refugees, how people interpret, frame, and discuss these arrivals, how migration-related discourse responds to external shocks, and how spatial segregation patterns of migrants and ethnic minorities emerge. [ABSTRACT FROM AUTHOR]

Evidence from 184 countries over a 25-year span is used to analyze international migration flows based on their 'intensity' or size. Results show that network dynamics are relevant to understand the emergence and evolution of migration flows of varying intensity. The findings also show that a core set of non-network covariates derived from Migration Systems Theory is important to understand migration flows of different intensity. Overall, the results highlight the significance of explicitly analyzing the intensity of international migration flows since not all covariates affect these flows in the same way. [ABSTRACT FROM AUTHOR]

Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardised knowledge on how to predict refugee and IDP flows in practice. Researchers and humanitarian officers are confronted with the need to make decisions about how to structure their datasets and how to fit their problem to predictive analytics approaches, and they must choose from a variety of modelling options. Most of the time, these decisions are made without an understanding of the full range of options that could be considered, and using methodologies that have primarily been applied in different contexts – and with different goals – as opportunistic references. In this work, we attempt to facilitate a more comprehensive understanding of this emerging field of research by providing a systematic model-agnostic framework, adapted to the use of big data sources, for structuring the prediction problem. As we do so, we highlight existing work on predicting refugee and IDP flows. We also draw on our own experience building models to predict forced displacement in Somalia, in order to illustrate the choices facing modellers and point to open research questions that may be used to guide future work. [ABSTRACT FROM AUTHOR]

Problem-solving requires adopting an appropriate approach that is influenced by understanding the nature of problems ranging from soft to hard. Research results indicate that there is still a lack of an accurate understanding of the nature of problems while researchers are engaged in adopting an approach to addressing the problems. Therefore, this research seeks to investigate the nature of hard and soft problems and the quality of their resulting solutions. This research endeavors to examine the nature of soft and hard problems. It is believed that identifying the attributes of different types of problems helps strike a balance between the nature of the problem, the problem-solving method, and the methodology used. Therefore, this research has addressed the nature of hard and soft problems and their perspectives on solving problems to fill gaps left in systems thinking studies. Also, current research helps researchers set a balance between attributes of the problem and ways of thinking about it, better identify the nature of the problem and better adapt the methodology to the problem. Based on the results of this research, soft problems are influenced by the context of the problem. In addition, in hard problems, the structure of the solution is determined by the problem structure. In contrast, in soft problems, the structure of the solution demands a proper understanding of the logical and cultural phenomena of the problem context. That is why the quality of the answer to hard problems is a normative, right, and definitive solution, while the answer to soft problems is a procedure composed of concepts and meanings. The findings of this study suggest that, like a soft problem, the solution to a soft problem is a social reality that all interested groups collectively enact. Consequently, the solution is a system of concepts that all the interested groups accept, and over its implementation, there is a shared understanding among all the interested groups. [ABSTRACT FROM AUTHOR]

Over the past decade, sociologists have become increasingly interested in the formal study of semantic relations within text. Most contemporary studies focus either on mapping concept co-occurrences or on measuring semantic associations via word embeddings. Although conducive to many research goals, these approaches share an important limitation: they abstract away what one can call the event structure of texts, that is, the narrative action that takes place in them. I aim to overcome this limitation by introducing a new framework for extracting semantically rich relations from text that involves three components. First, a semantic grammar structured around textual entities that distinguishes six motif classes: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity; second, a comprehensive set of mapping rules, which make it possible to recover motifs from predictions of dependency parsers; third, an R package that allows researchers to extract motifs from their own texts. The framework is demonstrated in empirical analyses on gendered interaction in novels and constructions of collective identity by U.S. presidential candidates. [ABSTRACT FROM AUTHOR]

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements.

In this paper, a stochastic hybrid predator-prey model with Beddington-DeAngelis functional response and Lévy jumps is studied. Firstly, it is proved that the model has a unique global solution. Secondly, sufficient conditions for weak persistence in the mean and extinction of prey and predator populations are established. Finally, sufficient conditions for the existence and uniqueness of ergodic stationary distribution are established. Moreover, several numerical simulations are presented to illustrate the main results. [ABSTRACT FROM AUTHOR]

The accelerated datafication of social life has led to increased interest in advancing the use of big data in psychology. However, prevailing methodological preoccupations in big data psychology elide foundational issues which amplify the problems of modernity/coloniality, including the suppression of marginalized psychologies and the widened gap between researchers and their subjects. We propose decolonial foundations for utilizing big data in psychological scholarship. We move beyond prevailing technical concerns with data processing, to engage situated practices of knowledge production. The ontological object of computing shifts from a universal psychology of numerous individuals, to a localized psychology of collectives. Epistemological priorities shift from context‐free definition and control, to prioritizing temporal responsiveness and linguistic fidelity to emergent psychological phenomena. Ethical guidelines extend beyond issues of personal privacy, and address sociopolitical concerns in fragile democracies. Finally, reflexivities from the margins account for unequal systems that situate psychologists, big data, and the phenomena they study within the plurality of Global Souths. Two Philippine case studies operationalize these principles through localized data collection practices, participatory algorithmic design, interrogation of bias, and wrestling with state encroachments. We conclude with decolonial directions for advancing equitable practices of knowledge production within the computational and psychological sciences. [ABSTRACT FROM AUTHOR]

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in 'big' and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research – analyses concerned with the particularities of a social context or phenomenon – starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including essence of computational sociology; limitations in traditional models and present their way for computational cultural cartography and big data analytics.

This paper deals with how to recognise if the patterns of homophily found in a social network are resilient to small disturbances that may occur in that network. Data from a survey of students in a secondary school in the Canary Islands were replicated using an agent-based model. The model calculated homophily indices and their statistical significance and then simulated small alterations in the distribution of links. The results clearly show that some homophily indices resist these kinds of perturbations and others do not. Evidence suggests that the distribution of individuals across the social network communities is a key factor in explaining why certain patterns of relationships are more resilient than others. [ABSTRACT FROM AUTHOR]

The research on what happens on the Internet often brings us closer to studies at the frontier of knowledge. This article is part of the transdisciplinary space of Computational Sociology and Social Sciences. It pretends to present the current development of research referred to as Social Big Data. It describes methodological processes typical in this field where social media on the Internet are the primary data source. Along with this description, it highlights some of the advantages, limitations, and challenges for research in this field, closely linked to methodological and technical advances made in other sciences. The text introduces Social Big Data's conceptual specificity as a confluence of social media, data analysis, and massive data. Then, we explore essential changes in the research process in this field and advances in working with social big data in areas of artificial intelligence such as machine learning, artificial neural networks, and deep learning, which are aligned with social sciences that tend to predictions. We then argue about the relevance for Sociology and other sciences to advance in an approach based on mixed methods in the Social Big Data area, rethinking the micro-macro link in this field of study. Through a case study [on the negationist social movement in Twitter in Spain during the COVID-19 pandemics], we also illustrate some potentials and limitations of this type of research, which will allow us to outline some of the methodological challenges that experts could incorporate into a research agenda in this area. Social Big Data and Computational Sociology and Social Sciences research offer directions of great interest to the Sociology of the coming years in which exceptional progress can be made in the development of transdisciplinarity and hybridization in science, enriching them. [ABSTRACT FROM AUTHOR]

Copyright of Revista Internacional de Ciencias Sociales Interdisciplinares is the property of Common Ground Research Networks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Moral framing is a mobilizing strategy in digital activism to raise awareness of a social issue. This essay discussed the strengths and weaknesses of multiple methods of extracting moral framing from media content. Two issues were highlighted from crowd-coding and automated coding using computational approaches: context sensitivity and the handling of coding discrepancies. We compared results from manual content coding and automated analysis using the eMFD, urging scholars to incorporate multiple methods to uncover the meanings behind each moral value. We argued that crowdcoding, computational approaches and manual coding are not exclusive of one another, and encouraged scholars to diversify their methodological toolbox and choose what to use based on the research context. [ABSTRACT FROM AUTHOR]

Exponential family random graph models (ERGMs) are widely used to model networks by parameterizing graph probability in terms of a set of user-selected sufficient statistics. Equivalently, ERGMs can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with nontrivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One very simple question of this type is that of the conditions needed for social forces to stabilize a particular structure: that is, given a specific structure and a set of alternatives (e.g., arising from small perturbations), under what conditions will said structure remain more probable than the alternatives? We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change scores of the sufficient statistics vis-\à-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the "stable cone" within the parameter space as a starting point. In addition to facilitating the understanding of model behavior, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one (i.e., entropic effects). We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing under the current social forces. The utility of the "stable cone" for ERGM parameter optimization is then demonstrated on a physical model of amyloid fibril formation. This demonstration features a visualization of the stable region, whereby it is shown that the majority of the region of the model's parameter space where ERGM simulations produce the highest fibril yield lies within the "stable cone. [ABSTRACT FROM AUTHOR]

Analyzing a large data set of publications drawn from the most competitive journals in the natural and social sciences we show that research careers exhibit the broad distributions of individual achievement characteristic of systems in which cumulative advantage plays a key role. While most researchers are personally aware of the competition implicit in the publication process, little is known about the levels of inequality at the level of individual researchers. Here we analyzed both productivity and impact measures for a large set of researchers publishing in high-impact journals, accounting for censoring biases in the publication data by using distinct researcher cohorts defined over non-overlapping time periods. For each researcher cohort we calculated Gini inequality coefficients, with average Gini values around 0.48 for total publications and 0.73 for total citations. For perspective, these observed values are well in excess of the inequality levels observed for personal income in developing countries. Investigating possible sources of this inequality, we identify two potential mechanisms that act at the level of the individual that may play defining roles in the emergence of the broad productivity and impact distributions found in science. First, we show that the average time interval between a researcher’s successive publications in top journals decreases with each subsequent publication. Second, after controlling for the time dependent features of citation distributions, we compare the citation impact of subsequent publications within a researcher’s publication record. We find that as researchers continue to publish in top journals, there is more likely to be a decreasing trend in the relative citation impact with each subsequent publication. This pattern highlights the difficulty of repeatedly producing research findings in the highest citation-impact echelon, as well as the role played by finite career and knowledge life-cycles, and the intriguing possibility that confirmation bias plays a role in the evaluation of scientific careers.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

We use a mechanized verification system, PVS, to examine the argument from Anselm's Proslogion Chapter III, the so-called "Modal Ontological Argument." We consider several published formalizations for the argument and show they are all essentially similar. Furthermore, we show that the argument is trivial once the modal axioms are taken into account. This work is an illustration of Computational Philiosophy and, in addition, shows how these methods can help detect and rectify errors in modal reasoning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on the Villani report on artificial intelligence (AI) and its impact on French society. Topics include continuation of a French tradition of official reports on the computerization of society, aiming to promote AI; and various labels used to identify the phenomenon of AI, such as telematique, communications électroniques, and nouvelles technologies de l'information et de la communication (NTIC).

Vast amounts of news are consumed through algorithmically curated media environments, such as search engines, social networking sites, or news aggregators. This renders algorithmic content curation with much societal relevance and highlights the urgent need for independent and resilient academic research. Therefore, a plethora of methodological approaches have been applied, such as case studies, expert interviews, observations, or agent-based approaches. The paper discusses the applicability of these methodological efforts for journalism studies, showing that all of these approaches face their limitations, especially with regard to external validity, recruitment difficulties, and data reliability. Thereby, agent-based testing represents one of the most promising approaches to overcome plenty of these methodological limitations. Agent-based testing is a systematic and experimental approach that emulates online human behavior to test algorithmically curated media environments under various conditions. For this to be achieved properly, this paper suggests a multitude of settings and requirements to adequately face the technological, legal, and ethical challenges, which come with the empirical investigation of algorithmic content curation. Ultimately, the paper presents both general considerations and practical instructions (using the "ScrapeBot") to employ agent-based testing for journalism studies. [ABSTRACT FROM AUTHOR]

In 1947 Jerzy Łoś proposed a positional logic based on the re- alization operator. We follow his work and present it in the context of fundamental challenges of sociology such as the complexity of social reality and reflexivity of social agents. The paper is an outline of the general concept, as it opens a discussion and sets ground for future elaborations. In this paper, we are considering the concept according to which the ex- pressions put forward by Łoś's system might be indexed not only by spatial and temporal variables, but also by social contexts. And as such Łoś's system might be a significant improvement, a valuable addition for social simulations and computational sociology, which use multi-agent systems and agent-based modeling. We consider how Łoś's operator might be useful for these disciplines, as it gives a chance to combine of formalization with the humanistic coefficient, which represents the issues of complexity and reflexivity of social agents. [ABSTRACT FROM AUTHOR]

Crime is ubiquitous and has been around for millennia. Crime is analogous to a pathogenic infection and police response to it is similar to an immune response. The biological immune system is also engaged in an arms race with pathogens. We propose an immune system inspired theory of crime and violence in human societies, especially in large agglomerations like cities. In this work we suggest that an immune system inspired theory of crime can provide a new perspective on the dynamics of violence in societies. The competitive dynamics between police and criminals has similarities to how the immune system is involved in an arms race with invading pathogens. Cities have properties similar to biological organisms and in this theory the police and military forces would be the immune system that protects against detrimental internal and external forces. Our theory has implications for public policy: ranging from how much financial resource to invest in crime fighting, to optimal policing strategies, pre-placement of police, and number of police to be allocated to different cities. Our work can also be applied to other forms of violence in human societies (like terrorism) and violence in other primate societies and eusocial insects. We hope this will be the first step towards a quantitative theory of violence and conflict in human societies. Ultimately we hope that this will help in designing smart and efficient cities that can scale and be sustainable despite population increase.

The possible existence of a research‐practice gap is the topic of a long‐standing debate in the field of public administration. In this Viewpoint essay, the authors examine the agendas of scholars and practitioners using the topic modeling technique of computational social science. Topic modeling a content analysis of 35 topics identified in Public Administration Review and PA Times (3,796 articles) showed that just over 50 percent of topics were common to both groups, indicating shared interests. There were, however, topics that the two groups distinctly focused on. Moreover, scholars and practitioners attached significant differences to the weights allocated to the prominent topics in their writing. Taken together, these findings indicate that topic modeling can shed new light on the research‐practice gap in public administration. [ABSTRACT FROM AUTHOR]

We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences (rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste. [ABSTRACT FROM AUTHOR]

We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence. [ABSTRACT FROM AUTHOR]

This paper is influenced by the research of Thomas Schelling on spatial segregation; in his seminal work on the subject he used simple simulations to show that even highly tolerant individuals end up being spatially aggregated far beyond the local requirement of their tolerance level. In this paper we are not seeking to find the conditions, in terms of density of population and tolerance level, that lead to a global stable state where all the individuals are satisfied in view of their own neighborhoods. Here the context is: (i) a space full of agents where each individual is in continual contact with a maximum number of neighbors; and (ii) where both a principal and a secondary discrimination criterion compel people to leave their places. As, in general, the first hypothesis does not allow the population to converge within the meaning of Schelling, only incomplete segregation phenomena are observable. So the problematic will be to determine, according to the respective strength of the two discrimination criteria, the spatial repartition of the agents resulting from their moves; in such a general context, we will refer to segregation as being strong or weak or even mixed. [ABSTRACT FROM AUTHOR]