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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]

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]

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]

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 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]

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]

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]

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]

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]

Abstract One approach to voting on several interrelated issues consists in using a language for compact preference representation, from which the voters' preferences are elicited and aggregated. Such a language can usually be seen as a domain restriction. We consider a well-known restriction, namely, conditionally lexicographic preferences , where both the relative importance between issues and the preference between the values of an issue may depend on the values taken by more important issues. The naturally associated language consists in describing conditional importance and conditional preference by trees together with conditional preference tables. In this paper, we study the aggregation of conditionally lexicographic preferences for several common voting rules and several classes of lexicographic preferences. We address the computation of the winning alternative for some important rules, both by identifying the computational complexity of the relevant problems and by showing that for several of them, computing the winner reduces in a very natural way to a maxsat problem. [ABSTRACT FROM AUTHOR]

Abstract Human agency has been a focus of philosophical and sociological concern from early debates about "free will" to recent themes in poststructuralism. Debates over the proper understanding of structure, agency, and constraint are hindered by the fact that few if any empirical measures of these concepts have been proposed. As sociologists have long recognized, the total results of the decisions of a group's members can be viewed as a distribution, and parameters can be fit to obtain a description of observed distributions. Here we propose the use of negative binomial curve to model population survival outcomes, and suggest that the parameters of such a curve represent reasonable surrogates for measures of agency, opportunity, and constraint when the decision process can be thought of as akin to a Bernoulli process. To provide an illustration of this approach, we discuss participation of legal minors in commercial sex (commonly referred to as victims of domestic minor sex trafficking (VDMST) or commercially sexually exploited children (CSEC)). In popular and advocacy-based accounts, considerable focus has been placed on the relative powerlessness of female VDMST. Using the proposed modeling technique, we test the extent to which male versus female VDMST appear to possess greater agency (or function under more limiting constraint) when deciding whether to remain in sex work or "leave the life". Contrary to existing literature, our results indicate that male and female underage sex workers are experiencing similar levels of agency, and differ mainly in opportunity, and constraint. Other individual circumstances are shown to contribute to varying levels of agency and constraint among sex workers, including street work status, community trouble, drug use, and the availability of an alternative income. Guildenstern: Inside where nothing shows, I am the essence of a manspinning double-headed coins, and betting against himself in privateatonement for an unremembered past. Tom Stoppard, "Rosencrantz and Guildenstern are Dead" [ABSTRACT FROM AUTHOR]

We propose a computational model for identifying emotional state of a facial expression from appraisal scores given by human observers utilizing their differences in perception. The appraisal model of human emotion is adopted as the basis of this evaluation process with appraisal variables as output. We investigated the performance for both categorical and continuous representation of the variables appraised by human observers. Analysis of the data exhibits higher degree of agreement between estimated Indian ratings and the available reference when these are rated through continuous domain. We also observed that emotional state with negative valence are influential in the perception of hybrid emotional state like ‘Surprise’, only when appraisal variables are labeled through categories of emotions. Thus, the proposed method has implications in developing software to detect emotion using appraisal variables in continuous domain, perceived from facial expression of an agent (or human subject). Further, this model can be customized to include cultural variability in recognizing emotions. [ABSTRACT FROM AUTHOR]

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include “every publicly available Reddit comment” which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit. [ABSTRACT FROM AUTHOR]

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. [ABSTRACT FROM AUTHOR]

In this article we consider some low-tech approaches to text mining. Our goal is to articulate a RiCH (Reader in Control of Hermeneutics) style of text analysis that takes advantage of the digital affordances of modern reading practices and easily deployable computational tools while also preserving the primacy of the interpretive lens of the human reader. In the article we offer three analytical interventions that are suitable to the low-tech formalizations we propose: the first and most developed intervention tracks the (normally computationally ignored) “stop” words; the second identifies the use of strategic anxiety terms in the texts; and the third (less developed in this article) introduces the grammatical features of modality (including modalization statements of probability and usuality, and modulation statements regarding degrees of obligation and inclination). All three analytical interventions provide a productive tracking of various modes and degrees of strategic decisiveness, contradiction, uncertainty and indeterminacy in a corpus of recent U.S. National Security Strategy reports. [ABSTRACT FROM AUTHOR]

The explanatory power of structures in analytical sociologists’ agent-based models brings into question methodological individualism. We defend that (a) from an explanatory point of view, the syntactic properties of models require semantic conditions of interpretation drawn from a conceptual research framework; (b) in such a framework, social/relational structures have only partial, explanatory power (counterfactual); and (c) taking the explanation further through generative mechanism modeling necessitates calling upon methodological individualism’s generic framework of interpretation that relies on social actors’ rational capacity. According to this interpretive framework, forces in action in society are governed by the subjective meaning of/the reasons for individual actions. [ABSTRACT FROM AUTHOR]

Water engineering is an amalgam of engineering (e.g., hydraulics, hydrology, irrigation, ecosystems, environment, water resources) and non-engineering (e.g., social, economic, political) aspects that are needed for planning, designing and managing water systems. These aspects and the associated issues have been dealt with in the literature using different techniques that are based on different concepts and assumptions. A fundamental question that still remains is: Can we develop a unifying theory for addressing these? The second law of thermodynamics permits us to develop a theory that helps address these in a unified manner. This theory can be referred to as the entropy theory. The thermodynamic entropy theory is analogous to the Shannon entropy or the information theory. Perhaps, the most popular generalization of the Shannon entropy is the Tsallis entropy. The Tsallis entropy has been applied to a wide spectrum of problems in water engineering. This paper provides an overview of Tsallis entropy theory in water engineering. After some basic description of entropy and Tsallis entropy, a review of its applications in water engineering is presented, based on three types of problems: (1) problems requiring entropy maximization; (2) problems requiring coupling Tsallis entropy theory with another theory; and (3) problems involving physical relations. [ABSTRACT FROM AUTHOR]

This article describes some of the theoretical approaches used by social scientists as well as those used by computer scientists to study the team and group phenomena. The purpose of this article is to identify ways in which these different fields can share and develop theoretical models and theoretical approaches, in an effort to gain a better understanding and further develop team and group research. [ABSTRACT FROM AUTHOR]

As the concluding article of the special issue from the Lorentz, Netherlands workshop, “New Frontiers in Analyzing Dynamic Group Interactions: Bridging Social and Computer Science,” the authors describe ways in which computer scientists and social scientists can integrate their work to pursue interdisciplinary that can satisfy the research agendas of both sets of scholars. [ABSTRACT FROM AUTHOR]

In this article, a team of authors from the Geeks and Groupies workshop, in Leiden, the Netherlands, compare prototypical approaches to studying group interaction in social science and computer science disciplines, which we call workflows. To help social and computer science scholars understand and manage these differences, we organize workflow into three major stages: research design, data collection, and analysis. For each stage, we offer a brief overview on how scholars from each discipline work. We then compare those approaches and identify potential synergies and challenges. We conclude our article by discussing potential directions for more integrated and mutually beneficial collaboration that go beyond the producer–consumer model. [ABSTRACT FROM AUTHOR]

As part of the Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” held in Leiden, Netherlands, this article describes how Geeks and Groupies (computer and social scientists) may benefit from interdisciplinary collaboration toward the development of killer apps in team contexts that are meaningful and challenging for both. First, we discuss interaction processes during team meetings as a research topic for both Groupies and Geeks. Second, we highlight teamwork in health care settings as an interdisciplinary research challenge. Third, we discuss how an automated solution for optimal team design could benefit team effectiveness and feed into team-based interventions. Fourth, we discuss team collaboration in massive open online courses as a challenge for both Geeks and Groupies. We argue for the necessary integration of social and computational research insights and approaches. In the hope of inspiring future interdisciplinary collaborations, we develop criteria for evaluating killer apps—including the four proposed here—and discuss future research challenges and opportunities that potentially derive from these developments. [ABSTRACT FROM AUTHOR]

In this article of the “Interdisciplinary Insights Into Group and Team Dynamics” special issue, we provide guidance for computer scientists and social scientists who seek an interdisciplinary approach to group research. We include how-to guidelines for researchers interested in initiating and maintaining collaborations, and discuss opportunities and pitfalls of interdisciplinary group research. Last, we include a brief case study that portrays some of the complications of creating shared understanding. [ABSTRACT FROM AUTHOR]

This special issue on advancing interdisciplinary collaboration between computer scientists and social scientists documents the joint results of the international Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” which took place in Leiden, The Netherlands, July 2016. An equal number of scholars from social and computer science participated in the workshop and contributed to the papers included in this special issue. In this introduction, we first identify interaction dynamics as the core of group and team models and review how scholars in social and computer science have typically approached behavioral interactions in groups and teams. Next, we identify key challenges for interdisciplinary collaboration between social and computer scientists, and we provide an overview of the different articles in this special issue aimed at addressing these challenges. [ABSTRACT FROM AUTHOR]

In philosophy of action, we typically aim to explain action by appealing to conative attitudes whose contents are either logically consistent propositions or can be rendered as such. Call this “the logical criterion.” This is especially difficult to do with clear-minded, intentional incontinence since we have to explain how two judgments can have non-contradicting contents yet still aim at contradictory outcomes. Davidson devises an innovative way of doing this but compromises his ability to explain how our better judgments can cause our continent behaviors. In this essay, I preserve Davidson’s approach to the logical criterion but deviate from his broader theory of action by developing a default-interventionist dual systems theory of action. To do this, I focus on the dynamical relationship between System 1 and System 2: (1) the logical construction of value judgments in System 2 from System 1 and (2) the imaginative construction of non-propositional conative attitudes in System 1 from System 2. I draw on Street’s Humean constructivism and Peacocke’s theory of imagination for logical and imaginative construction, respectively. Within this framework, I provide a new definition of continence and incontinence that satisfies the logical criterion and explains how our better judgments can cause our continent behaviors. [ABSTRACT FROM PUBLISHER]

This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems. [ABSTRACT FROM AUTHOR]

Tackling complex environmental challenges requires the capacity to understand how relationships and interactions between parts result in dynamic behavior of whole systems. There has been convincing research that these “systems thinking” skills can be learned. However, there is little research on methods for teaching these skills to children or assessing their impact. The Environmental Dashboard is a technology that uses “sociotechnical” feedback–information feedback designed to affect thought and behavior. Environmental Dashboard (ED) combines real-time information on community resource use with images and words that reflect pro-environmental actions of community members. Prior research indicates that ED supports the development of systems thinking in adults. To assess its impact on children, the technology was installed in a primary school and children were passively exposed to ED displays. This resulted in no measurable impact on systems thinking skills. The next stage of this research examined the impact of actively integrating ED into lessons on electricity in 4th and 5th grade. This active integration enhanced both content-related systems thinking skills and content retention. [ABSTRACT FROM AUTHOR]

We consider three problems for discrete-time switched systems with autonomous, general nonlinear modes. The first is optimal control of the switching rule so as to optimize the infinite-horizon discounted cost. The second and third problems occur when the switching rule is uncontrolled, and we seek either the worst-case cost when the rule is unknown, or respectively the expected cost when the rule is stochastic. We use optimistic planning (OP) algorithms that can solve general optimal control with discrete inputs such as switches. We extend the analysis of OP to provide certification (upper and lower) bounds on the optimal, worst-case, or expected costs, as well as to design switching sequences that achieve these bounds in the deterministic case. In this case, since a minimum dwell time between switching instants is often required, we introduce a new OP variant to handle this constraint, and analyze its convergence rate. We provide consistency and closed-loop performance guarantees for the sequences designed, and illustrate that the approach works well in simulations. [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. [ABSTRACT FROM AUTHOR]

This essay argues for a paradigm shift in the study of the Internet and digital/social media. Big data analytics is the dominant paradigm. It receives large amounts of funding, is administrative and a form of digital positivism. Critical social media research is an alternative approach that combines critical social media theory, critical digital methods and critical-realist social media research ethics. Strengthening the second approach is a material question of power in academia. [ABSTRACT FROM AUTHOR]

We propose a computational model of 'Fermionic Society'. It is based on the fermionic Hubbard model, which is used for computational study of the Socioeconomic Polarization. For this purpose, a dynamical mean-field theory of the Hubbard model has been implemented in C#. The simulation has been performed for a range of values of the socioeconomic repulsion potential, which forms the polarization vector-field. A brief review of the range of methods used for social science simulation shows other methods currently commonly in use. Newer methods based on or inspired by quantum physics are also briefly reviewed. A Socio-Cognitive sciences perspective is presented to ground the current work in its sociological roots, leading to a discussion on Socioeconomic Polarization with respect to an archetypal socioeconomic problem to illustrate how polarization and depolarization applies to concrete problems. [ABSTRACT FROM AUTHOR]

Although several models in the literature analyze the dynamics of opinion formation, less attention has been paid to explain how the structure of social networks and their contextual circumstances can influence the course of minority public opinions. This work aims to pose three basic questions: (1) how the structure of social networks can affect the spread of minority opinion, (2) how committed agents influence this process, and (3) how mass media action, as a contextual factor, can vary different agents’ opinions and network composition. Agent-based modeling is used to create a network model of preferential attachment to explore how phenomena of minority opinion spreading can evolve under different simulated scenarios. This study shows that the success of minority opinions depends on network structure and composition and thus on external factors such as mass media action that can mediate the strength of these internal determinants. Although people tend to remain silent when they feel that their opinions are in the minority, our findings suggest that prevailing majority opinion may be promptly replaced by what was formerly minority opinion if core agents in the network structure and/or external sources support this view. [ABSTRACT FROM AUTHOR]

Drawing on Bayesian probability theory, we propose a generalization of affect control theory (BayesACT) that better accounts for the dynamic fluctuation of identity meanings for self and other during interactions, elucidates how people infer and adjust meanings through social experience, and shows how stable patterns of interaction can emerge from individuals’ uncertain perceptions of identities. Using simulations, we illustrate how this generalization offers a resolution to several issues of theoretical significance within sociology and social psychology by balancing cultural consensus with individual deviations from shared meanings, balancing meaning verification with the learning processes reflective of change, and accounting for noise in communicating identity. We also show how the model speaks to debates about core features of the self, which can be understood as stable and yet malleable, coherent and yet composed of multiple identities that may carry competing meanings. We discuss applications of the model in different areas of sociology, implications for understanding identity and social interaction, as well as the theoretical grounding of computational models of social behavior. [ABSTRACT FROM AUTHOR]

This paper presents some models and algorithms for social choice in agent networks. Agent networks are graphical models used to represent systems involving multiple, locally interacting, agents. They allow the representation of complex decision-making situations where the utility function of every agent depends on the actions of its neighbors. In this context, coordination requires some optimization method able to determine a combination of individual actions that maximizes social efficiency. We study here the maximization of different social welfare functions covering various attitudes ranging from utilitarianism to egalitarianism. For all these models we propose graph-based algorithms as well as MIP formulations to find optimal sets of actions. We also provide numerical tests to assess their practical efficiency. [ABSTRACT FROM AUTHOR]

We continue previous work by Mattei et al. (Ann. Math. Artif. Intell. 1042 68(1-3), 135-160 2013) in which they study the computational complexity of bribery schemes when voters have conditional preferences modeled as CP-nets. For most of the cases they considered, they showed that the bribery problem is solvable in polynomial time. Some cases remained open-we solve several of them and extend the previous results to the case that voters are weighted. Additionally, we consider negative (weighted) bribery in CP-nets, when the briber is not allowed to pay voters to vote for his preferred candidate. [ABSTRACT FROM AUTHOR]

The paper presents a focused survey about the presence and the use of the concept of 'preferences' in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic. [ABSTRACT FROM AUTHOR]

We study the complexity of priced control in elections. Naturally, if a given control type is NP-hard for a given voting system ɛ then its priced variant is NP-hard for this rule as well. It is, however, interesting what effect introducing prices has on the complexity of those control problems that without prices are tractable. We show that for four prominent voting rules (plurality, approval, Condorcet, and Copeland) introducing prices does not increase the complexity of control by adding/deleting candidates/voters. However, we do show an example of a scoring rule for which such an effect takes place. [ABSTRACT FROM AUTHOR]