How and Why People Synchronize: An Integrated Perspective

Academic AbstractInterpersonal synchrony, the alignment of behavior and/or physiology during interactions, is a pervasive phenomenon observed in diverse social contexts. Here we synthesize across contexts and behaviors to classify the different forms and functions of synchrony. We provide a concise framework for classifying the manifold forms of synchrony along six dimensions: periodicity, discreteness, spatial similarity, directionality, leader-follower dynamics, and observability. We also distill the various proposed functions of interpersonal synchrony into four interconnected functions: reducing complexity and improving understanding, accomplishing joint tasks, strengthening social connection, and influencing partners' behavior. These functions derive from first principles, emerge from each other, and are accomplished by some forms of synchrony more than others. Effective synchrony flexibly adapts to social goals and more synchrony is not always better. Our synthesis offers a shared framework and language for the field, allowing for better cross-context and cross-behavior comparisons, generating new hypotheses, and highlighting future research directions.

Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review

PURPOSE

Social network analysis (SNA) is a theoretical framework and analytical approach used to study relationships among individuals and groups. While SNA has been employed by many disciplines to understand social structures and dynamics of interpersonal relationships, little is known about its use in medical education. Mapping and synthesizing the scope of SNA in undergraduate and postgraduate medical education can inform educational practice and research.

METHOD

This scoping review was based on searches conducted in Medline, Embase, Scopus, and ERIC in December 2020 and updated in March 2022. After removal of duplicates, the search strategy yielded 5,284 records, of which 153 met initial inclusion criteria. Team members conducted full-text reviews, extracted relevant data, and conducted descriptive and thematic analyses to determine how SNA has been used as a theoretical and analytical approach in undergraduate and postgraduate medical education.

RESULTS

Thirty studies, from 11 countries, were retained. Most studies focused on undergraduate medical students, primarily in online environments, and explored students' friendships, information sharing, and advice seeking through SNA. Few studies included residents and attending staff. Findings suggested that SNA can be a helpful tool for monitoring students' interactions in online courses and clinical clerkships. SNA can also be used to examine the impact of social networks on achievement, the influence of social support and informal learning outside the classroom, and the role of homophily in learning. In clinical settings, SNA can help explore team dynamics and knowledge exchange among medical trainees.

CONCLUSIONS

While SNA has been underutilized in undergraduate and postgraduate medical education, findings indicate that SNA can help uncover the structure and impact of social networks in the classroom and the clinical setting. SNA can also be used to help design educational experiences, monitor learning, and evaluate pedagogical interventions. Future directions for SNA research in medical education are described.

Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.

Increasing health facility childbirth in Ghana: the role of network and community norms

BACKGROUND

Reducing pregnancy-related deaths in Sub-Saharan Africa through increases in health facility births may be achieved by promoting community norms and network norms favoring health facility births. However, the process of how both norms shift attitudes and actions towards facility delivery is little studied. We examined the association of network and community norms with facility birth, following a quality improvement intervention to improve facility births in Ghana.

METHODS

A 2015 mixed methods evaluation of a Maternal and Newborn Health Referral (MNHR) project in Ghana included a cross-sectional survey of women (N = 508), aged 15-49 years; in-depth interviews (IDIs) with mothers (n = 40), husbands (n = 20) and healthcare improvement collaborative leaders (n = 8); and focus group discussions (FGDs) with mothers-in-law (n = 4) and collaborative members (n = 7). Multivariable logistic regression was used to examine the association of network and community norms with facility birth. Thematic analysis of the qualitative data was conducted to explain this relationship.

RESULTS

The network norm of perceived family approval of facility delivery (AOR: 5.54, CI: 1.65-18.57) and the community norm of perceived number of women in the community that deliver in a facility (AOR: 3.00, CI: 1.66-5.43) were independently associated with facility delivery. In qualitative IDIs and FGDs both norms were also collectively perceived as influencing facility delivery. However, network norms were more influential in women's utilization of facility-based pregnancy-related care. Healthcare improvement collaboratives were important in swaying both network and community norms toward facility-based delivery by offering pregnancy-related health information, antenatal care, and support for facility delivery.

CONCLUSION

Quality improvement initiatives impact both community and network norms. To be most impactful in advancing facility-based pregnancy-related care, these initiatives should focus on highlighting the shifting trend toward facility delivery in rural communicates and promoting support for facility delivery among women's personal networks.

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Flows of Substances in Networks and Network Channels: Selected Results and Applications

This review paper is devoted to a brief overview of results and models concerning flows in networks and channels of networks. First of all, we conduct a survey of the literature in several areas of research connected to these flows. Then, we mention certain basic mathematical models of flows in networks that are based on differential equations. We give special attention to several models for flows of substances in channels of networks. For stationary cases of these flows, we present probability distributions connected to the substance in the nodes of the channel for two basic models: the model of a channel with many arms modeled by differential equations and the model of a simple channel with flows of substances modeled by difference equations. The probability distributions obtained contain as specific cases any probability distribution of a discrete random variable that takes values of 0,1,…. We also mention applications of the considered models, such as applications for modeling migration flows. Special attention is given to the connection of the theory of stationary flows in channels of networks and the theory of the growth of random networks.

Person-to-person opinion dynamics: An empirical study using an online game

A model needs to make verifiable predictions to have any scientific value. In opinion dynamics, the study of how individuals exchange opinions with one another, there are many theoretical models which attempt to model opinion exchange, one of which is the Martins model, which differs from other models by using a parameter that is easier to control for in an experiment. In this paper, we have designed an experiment to verify the Martins model and contribute to the experimental design in opinion dynamic with our novel method.

Factors affecting young customers' smartphone purchase intention during Covid-19 pandemic

Purchase intention has become a critical issue to the marketers of smartphones as the market has become very competitive, volatile, uncertain and dynamic during Covid-19 than ever before. For sustaining in the competitive market, every marketer is trying to upgrade its product appearance, product quality, service quality, attractive features, and latest version of software as a whole. This study has investigated the effects of product features, brand image, product price, and social influences on young customers' purchase intention of smartphone during this Covid-19 pandemic time. Survey was conducted using structured questionnaire by collecting data from 305 respondents by using convenience sampling technique. Statistical Package for the Social Sciences (SPSS) integrated with AMOS was employed for data analysis. Cronbach's alpha, composite reliability and average variance extracted (AVE) were used to test the reliability and validity of the collected data while hypotheses were tested by using Structural equation modeling (SEM). The findings of the study shows that, there is a significant effect of product features, brand image, and product price on purchase intention of a smartphone but social influences has no significant impact on young customers' purchase intention. The study results will help the smartphone marketers to redesign their pandemic and post pandemic segmenting, targeting, differentiation and positioning strategies. Practical and managerial implications along with the future research directions have been discussed at the end of this paper also.

The Dual Challenge of Search and Coordination for Organizational Adaptation: How Structures of Influence Matter

Organizations increasingly need to adapt to challenges in which search and coordination cannot be decoupled. In response, many have experimented with “agile” and “flat” designs that dismantle traditional forms of hierarchy to harness the distributed knowledge of specialized individuals. Despite the popularity of such practices, there is considerable variation in their implementation as well as conceptual ambiguity about the underlying premise. Does effective rapid experimentation necessarily imply the repudiation of hierarchical structures of influence? We use computational models of multiagent reinforcement learning to study the effectiveness of coordinated search in groups that vary in how they influence each other’s beliefs. We compare the behavior of flat and hierarchical teams with a baseline structure without any influence on beliefs (a “crowd”) when all three are placed in the same task environments. We find that influence on beliefs—whether it is hierarchical or not—makes it less likely that agents stabilize prematurely around their own experiences. However, flat teams can engage in excessive exploration, finding it difficult to converge on good alternatives, whereas hierarchical influence on beliefs reduces simultaneous uncoordinated exploration, introducing a degree of rapid exploitation. As a result, teams that need to achieve agility (i.e., rapid satisfactory results) in environments that require coordinated search may benefit from a hierarchical structure of influence—even when the apex actor has no superior knowledge, foresight, or capacity to control subordinates’ actions.

Social determinants of panic buying behaviour amidst COVID-19 pandemic: The role of perceived scarcity and anticipated regret

Panic buying behaviour is inherently undesirable due to its detrimental impact on community's resources and disruptions to supply chain systems. The prevailing COVID-19 pandemic has seen a resurgence of this phenomenon across the world, leaving supermarkets in stockout situations. While panic buying is largely reasoned as a psychological reaction to an extreme event, it is also a socially relevant behaviour as our perception of a crisis can be shaped by our observations and interactions within the society. The social determinants of panic buying behaviour, particularly on how these factors heighten one's perception of scarcity, and trigger panic buying behaviour, are studied. A theoretical model is developed to explain panic buying behaviour in a social context by synthesizing various social and behavioural theories, and the inter-relationship among the latent constructs is analysed using the structural equation modelling approach. Accordingly, an online survey was administered and analysis of the data confirmed that non-coercive social influence, social norm and observational learning directly influence one's perception of scarcity. Additionally, perceived scarcity can motivate panic buying behaviour directly or indirectly through feelings of anticipated regret. This study has contributed to the limited literature on panic buying. Understanding the underlying mechanisms of panic buying will aid policymakers and businesses in developing intervention or support strategies to cope with such behaviour.

The acceptance and use of smartphones among older adults: differences in UTAUT determinants before and after training

Purpose

This article aims at a Unified Theory of Acceptance and Use of Technology (UTAUT) model framework that was used to investigate the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults.

Design/methodology/approach

A total of 208 participants aged 60–78 (mean: 65.4) years completed a questionnaire that collected information on demographic variables and the frequency and duration of smartphone use as well as the answers to questions on the six UTAUT constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention and usage behavior. The data were analyzed using partial least squares analysis.

Findings

This study was the first to compare post-training changes in the correlations among UTAUT constructs. The results revealed significant post-training changes in all construct correlations. Behavioral intention and facilitating conditions were shown to significantly impact usage behavior both before and after training and performance expectancy was shown to impact behavioral intention before training. After training, both effort expectancy and social influence were found to impact behavioral intention significantly. Moreover, the impact of facilitating conditions on usage behavior was significantly increased after training.

Originality/value

To date, no study published in the literature has investigated the impact of technological training on the technology-use intentions and behaviors of older adults. The findings of this study suggest that, for older adults, the results of the acceptance and use model for smartphones change significantly and positively between pre-smartphone training and post-smartphone training time points. The findings support that technology training has a positive impact on smartphone use in older adults.

Determinants of emissions pathways in the coupled climate-social system

The ambition and effectiveness of climate policies will be essential in determining greenhouse gas emissions and, as a consequence, the scale of climate change impacts^1,2. However, the socio-politico-technical processes that will determine climate policy and emissions trajectories are treated as exogenous in almost all climate change modelling^3,4. Here we identify relevant feedback processes documented across a range of disciplines and connect them in a stylized model of the climate-social system. An analysis of model behaviour reveals the potential for nonlinearities and tipping points that are particularly associated with connections across the individual, community, national and global scales represented. These connections can be decisive for determining policy and emissions outcomes. After partly constraining the model parameter space using observations, we simulate 100,000 possible future policy and emissions trajectories. These fall into 5 clusters with warming in 2100 ranging between 1.8 °C and 3.6 °C above the 1880-1910 average. Public perceptions of climate change, the future cost and effectiveness of mitigation technologies, and the responsiveness of political institutions emerge as important in explaining variation in emissions pathways and therefore the constraints on warming over the twenty-first century.

Multiple social network influences can generate unexpected environmental outcomes

Understanding the function of social networks can make a critical contribution to achieving desirable environmental outcomes. Social-ecological systems are complex, adaptive systems in which environmental decision makers adapt to a changing social and ecological context. However, it remains unclear how multiple social influences interact with environmental feedbacks to generate environmental outcomes. Based on national-scale survey data and a social-ecological agent-based model in the context of voluntary private land conservation, our results suggest that social influences can operate synergistically or antagonistically, thereby enabling behaviors to spread by two or more mechanisms that amplify each other's effects. Furthermore, information through social networks may indirectly affect and respond to isolated individuals through environmental change. The interplay of social influences can, therefore, explain the success or failure of conservation outcomes emerging from collective behavior. To understand the capacity of social influence to generate environmental outcomes, social networks must not be seen as 'closed systems'; rather, the outcomes of environmental interventions depend on feedbacks between the environment and different components of the social system.

New Network Models for the Analysis of Social Contagion in Organizations: An Introduction to Autologistic Actor Attribute Models

Autologistic actor attribute models (ALAAMs) provide new analytical opportunities to advance research on how individual attitudes, cognitions, behaviors, and outcomes diffuse through networks of social relations in which individuals in organizations are embedded. ALAAMs add to available statistical models of social contagion the possibility of formulating and testing competing hypotheses about the specific mechanisms that shape patterns of adoption/diffusion. The main objective of this article is to provide an introduction and a guide to the specification, estimation, interpretation and evaluation of ALAAMs. Using original data, we demonstrate the value of ALAAMs in an analysis of academic performance and social networks in a class of graduate management students. We find evidence that both high and low performance are contagious, that is, diffuse through social contact. However, the contagion mechanisms that contribute to the diffusion of high performance and low performance differ subtly and systematically. Our results help us identify new questions that ALAAMs allow us to ask, new answers they may be able to provide, and the constraints that need to be relaxed to facilitate their more general adoption in organizational research.

Can gender inequality be created without inter-group discrimination?

Understanding human societies requires knowing how they develop gender hierarchies, which are ubiquitous. We test whether a simple agent-based dynamic process could create gender inequality. Relying on evidence of gendered status concerns, self-construals, and cognitive habits, our model included a gender difference in how responsive male-like and female-like agents are to others' opinions about the level of esteem for someone. We simulate a population who interact in pairs of randomly selected agents to influence each other about their esteem judgments of self and others. Half the agents are more influenced by their relative status rank during the interaction than the others. Without prejudice, stereotypes, segregation, or categorization, our model produces inter-group inequality of self-esteem and status that is stable, consensual, and exhibits characteristics of glass ceiling effects. Outcomes are not affected by relative group size. We discuss implications for group orientation to dominance and individuals' motivations to exchange.

A model for the competition between political mono-polarization and bi-polarization

We investigate the phenomena of political bi-polarization in a population of interacting agents by means of a generalized version of the model introduced by Vazquez et al. [Phys. Rev. E 101, 012101 (2020)] for the dynamics of voting intention. Each agent has a propensity p in [0,1] to vote for one of two political candidates. In an iteration step, two randomly chosen agents i and j with respective propensities p_i and p_j interact, and then p_i either increases by an amount h>0 with a probability that is a nonlinear function of p_i and p_j or decreases by h with the complementary probability. We assume that each agent can interact with any other agent (all-to-all interactions). We study the behavior of the system under variations of a parameter q≥0 that measures the nonlinearity of the propensity update rule. We focus on the stability properties of the two distinct stationary states: mono-polarization in which all agents share the same extreme propensity (0 or 1), and bi-polarization where the population is divided into two groups with opposite and extreme propensities. We find that the bi-polarized state is stable for q<q_c, while the mono-polarized state is stable for q>q_c, where q_c(h) is a transition value that decreases as h decreases. We develop a rate equation approach whose stability analysis reveals that q_c vanishes when h becomes infinitesimally small. This result is supported by the analysis of a transport equation derived in the continuum h→0 limit. We also show by Monte Carlo simulations that the mean time τ to reach mono-polarization in a system of size N scales as τ∼N^α at q_c , where α is a nonuniversal exponent that depends on h.

Moderating Effects of Social Norms and Alcohol Consumption on Message Framing in Responsible Drinking Campaigns: Value from Deviance Regulation Theory

This study examined how the interplay between message framing and social norms affected college students' responses to advertisements and their intentions to drink responsibly, using the lens of deviance regulation theory. The results showed more favorable responses to gain-framed messaging than loss-framed messaging, especially among college students who believed that most of their peers use alcohol irresponsibly (i.e., they observed an unhealthy social norm). This study also investigated how the moderating effects of social norms on message framing differ depending on the level of individual alcohol consumption, and found that the deviance regulation effects on intention to drink responsibly were mitigated among heavy drinkers. The findings suggest strategic potential for using messaging, social context, and individual factors to develop effective campaigns that promote responsible drinking.

Promoting rational antibiotic prescribing for non-complicated infections: understanding social influence in primary care networks in Germany

Background

Primary care networks in Germany are formalized regional collaborations of physicians and other healthcare providers. Common goals are optimized healthcare processes and services for patients, enhanced communication, agency for professional concerns and strengthened economic power. In the ARena study (Sustainable reduction of antibiotic-induced antimicrobial resistance), 14 primary care networks in two federal German states aimed to promote appropriate antibiotics use for acute non-complicated infections by fostering awareness and understanding. Factors related to the role of primary care networks were to be identified.

Methods

For this study, audio-recorded telephone interviews were conducted with physicians, non-physician health professionals and stakeholder representatives. Pseudonymized verbatim transcripts were coded using thematic analysis. In-depth analysis was based on the inductive categories ‘social support’, ‘social learning’, ‘social normative pressures’ and ‘social contagion’ to reflect social influence processes. Data generated through a survey with physicians and non-physician health professionals were analyzed descriptively to foster understanding of the networks’ potential impact on antibiotic prescribing.

Results

Social influence processes proved to be relevant regarding knowledge transfer, manifestation of best-practice care and self-reflection. Peer communication was seen as a great asset, the main reason for membership and affirmative for own perspectives. All interviewed physicians (n = 27) considered their network to be a strong support factor for daily routines, introduction of new routines, and continuity of care. They utilized network-offered training programs focusing on best practice guideline-oriented use of antibiotics and considered their networks supportive in dealing with patient expectations. A shared attitude combined with ARena intervention components facilitated reflective management of antibiotic prescribing. Non-physician health professionals (n = 11) also valued network peer exchange. They assumed their employers joined networks to offer improved and continuous care. Stakeholders (n = 7) expected networks and their members to be drivers for care optimization.

Conclusion

Primary care networks play a crucial role in providing a platform for professional peer exchange, social support and reassurance. With regards to their impact on antibiotic prescribing for acute non-complicated infections, networks seem to facilitate and amplify quality improvement programs by providing a platform for refreshing awareness, knowledge and self-reflection among care providers. They are well suited to promote a rational use of antibiotics.

Trial registration

ISRCTN, ISRCTN58150046. Registered 24 August 2017.

Social Influence in the Uptake and Use of Electronic Cigarettes: A Systematic Review

CONTEXT

E-cigarettes were introduced to support smoking cessation, but their popularity has increased among nonsmokers, challenging current perspectives on their safety and effectiveness as a public health intervention. The objective of this systematic review was to identify and synthesize current evidence on the influence of social factors on e-cigarette intentions and use.

EVIDENCE ACQUISITION

MEDLINE, CINAHL, PsycINFO, and Embase were searched for studies of the effects of social factors on e-cigarette intention or use in February 2019. Studies were included if they used experimental, longitudinal, qualitative, or mixed methods designs. Advertising, social interactions, and social norms were considered as social factors; social media was considered a conduit for other social factors. Two reviewers screened all studies; bias risk was evaluated for all RCTs using the Cochrane risk of bias tool.

EVIDENCE SYNTHESIS

This review included 43 studies: 9 experimental, 11 longitudinal, 22 qualitative, and 1 mixed method. All experimental studies examined advertising and consistently showed that exposure increased intentions to use e-cigarettes. Evidence of the influence of social interactions and social norms came from longitudinal and qualitative studies, suggesting that these factors could increase e-cigarette use. Most participants were nonsmokers (81%; 22,233 of 27,303). Studies rarely considered differences in the effects of social factors on smokers and nonsmokers.

CONCLUSIONS

Given the increased popularity among nonsmokers and the potential for advertising to increase e-cigarette use, closer public health monitoring of e-cigarette uptake by nonsmokers is warranted. Future primary research should be designed to measure how social factors affect smokers and nonsmokers differently.

Are Collectivistic Cultures More Prone to Rapid Transformation? Computational Models of Cross-Cultural Differences, Social Network Structure, Dynamic Social Influence, and Cultural Change

Societies differ in susceptibility to social influence and in the social network structure through which individuals influence each other. What implications might these cultural differences have for changes in cultural norms over time? Using parameters informed by empirical evidence, we computationally modeled these cross-cultural differences to predict two forms of cultural change: consolidation of opinion majorities into stronger majorities, and the spread of initially unpopular beliefs. Results obtained from more than 300,000 computer simulations showed that in populations characterized by greater susceptibility to social influence, there was more rapid consolidation of majority opinion and also more successful spread of initially unpopular beliefs. Initially unpopular beliefs also spread more readily in populations characterized by less densely connected social networks. These computational outputs highlight the value of computational modeling methods as a means to specify hypotheses about specific ways in which cross-cultural differences may have long-term consequences for cultural stability and cultural change.

The rippling dynamics of valenced messages in naturalistic youth chat

Even though human behavior is largely driven by real-time feedback from others, this social complexity is underrepresented in psychological theory, largely because it is so difficult to isolate. In this work, we performed a quasi-experimental analysis of hundreds of millions of chat room messages between young people. This allowed us to reconstruct how-and on what timeline-the valence of one message affects the valence of subsequent messages by others. For the highly emotionally valenced chat messages that we focused on, we found that these messages elicited a general increase of 0.1 to 0.4 messages per minute. This influence started 2 s after the original message and continued out to 60 s. Expanding our focus to include feedback loops-the way a speaker's chat comes back to affect him or her-we found that the stimulating effects of these same chat events started rippling back from others 8 s after the original message, to cause an increase in the speaker's chat that persisted for up to 8 min. This feedback accounted for at least 1% of the bulk of chat. Additionally, a message's valence affects its dynamics, with negative events feeding back more slowly and continuing to affect the speaker longer. By reconstructing the second-by-second dynamics of many psychosocial processes in aggregate, we captured the timescales at which they collectively ripple through a social system to drive system-level outcomes.

The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers

Filter bubbles and echo chambers have both been linked recently by commentators to rapid societal changes such as Brexit and the polarization of the US American society in the course of Donald Trump's election campaign. We hypothesize that information filtering processes take place on the individual, the social, and the technological levels (triple‐filter‐bubble framework). We constructed an agent‐based modelling (ABM) and analysed twelve different information filtering scenarios to answer the question under which circumstances social media and recommender algorithms contribute to fragmentation of modern society into distinct echo chambers. Simulations show that, even without any social or technological filters, echo chambers emerge as a consequence of cognitive mechanisms, such as confirmation bias, under conditions of central information propagation through channels reaching a large part of the population. When social and technological filtering mechanisms are added to the model, polarization of society into even more distinct and less interconnected echo chambers is observed. Merits and limits of the theoretical framework, and more generally of studying complex social phenomena using ABM, are discussed. Directions for future research such as ways of comparing our simulations with actual empirical data and possible measures against societal fragmentation on the three different levels are suggested.

Dynamical networks of influence in small group discussions

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Structural and functional network characteristics and facility delivery among women in rural Ghana

Background

Health facility births contribute to the prevention of maternal deaths. Although theoretical and empirical evidence suggest that social network characteristics influence facility delivery, examination of this relationship in sub-Saharan Africa is limited. We determined whether network structural and functional characteristics were associated with, or had an interactive effect on health facility delivery in rural Ghana.

Methods

Data on mothers (n = 783) aged 15–49 years came from a Maternal and Newborn Health Referral (MNHR) project in Ghana, and included egocentric network data on women’s social network characteristics. Using multivariate logistic regression we examined the relationship between facility delivery and women’s network structure and functions, as well as the interaction between network characteristics and facility delivery.

Results

Higher levels of instrumental support (e.g. help with daily chores or seeking health care [OR: 1.60, CI: 1.10–2.34]) and informational support (OR: 1.66, CI: 1.08–2.54) were significantly associated with higher odds of facility delivery. Social norms, such as knowing more women who had received pregnancy-related care in a facility, were significantly associated with higher odds of facility delivery (OR: 2.20, CI: 1.21–4.00). The number of network members that respondents lived nearby moderated the positive relationship between informational support and facility delivery. Additionally, informational support moderated the positive relationship between facility delivery and the number of women the respondents knew who had utilized a facility for pregnancy-related care.

Conclusions

Social support from network members was critical to facilitating health facility delivery, and support was further enhanced by women’s network structure and norms favoring facility delivery. Maternal health interventions to increase facility delivery uptake should target women’s social networks.

Gender Differences in Resistance to Schooling: The Role of Dynamic Peer-Influence and Selection Processes

Boys engage in notably higher levels of resistance to schooling than girls. While scholars argue that peer processes contribute to this gender gap, this claim has not been tested with longitudinal quantitative data. This study fills this lacuna by examining the role of dynamic peer-selection and influence processes in the gender gap in resistance to schooling (i.e., arguing with teachers, skipping class, not putting effort into school, receiving punishments at school, and coming late to class) with two-wave panel data. We expect that, compared to girls, boys are more exposed and more responsive to peers who exhibit resistant behavior. We estimate hybrid models on 5448 students from 251 school classes in Sweden (14-15 years, 49% boys), and stochastic actor-based models (SIENA) on a subsample of these data (2480 students in 98 classes; 49% boys). We find that boys are more exposed to resistant friends than girls, and that adolescents are influenced by the resistant behavior of friends. These peer processes do not contribute to a widening of the gender gap in resistance to schooling, yet they contribute somewhat to the persistence of the initial gender gap. Boys are not more responsive to the resistant behavior of friends than girls. Instead, girls are influenced more by the resistant behavior of lower status friends than boys. This explains to some extent why boys increase their resistance to schooling more over time. All in all, peer-influence and selection processes seem to play a minor role in gender differences in resistance to schooling. These findings nuance under investigated claims that have been made in the literature.

Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models

This paper investigates the issues associated with choosing appropriate models of choice for demographic agent-based models. In particular, we discuss the importance of context, time preference, and dealing with uncertainty in decision modelling, as well as the heterogeneity between agents in their decision-making strategies. The paper concludes by advocating empirically driven, modular, and multi-model approaches to designing simulations of human decision-making, given the lack of an agreed strategy for dealing with any of these issues. Furthermore, we suggest that an iterative process of data collection and simulation experiments, with the latter informing future empirical data collection, should form the basis of such an endeavour. The discussion is illustrated with reference to selected demographic agent-based models, with a focus on migration.

An Empirical Agent-Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large-scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision-making process. Based on the social amplification of risk framework, our agent-based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the "risk publics" model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community-level parameters-including social groups, relationships, and communication variables, also from survey data-are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks.

Mandevillian intelligence

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success.

Collective Dynamics of Belief Evolution under Cognitive Coherence and Social Conformity

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework may offer explanations for how social transitions can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We argue that the inclusion of cognitive factors into a social model could provide a more complete picture of collective human dynamics.

A dynamic framework on travel mode choice focusing on utilitarian walking based on the integration of current knowledge

Recently, research on utilitarian walking has gained momentum due to its benefits on both health and the environment. However, our overall understanding of how built and social environments affect travel mode choice (walking or not) is still limited, and most existing frameworks on travel mode choice lack dynamic processes. After a review of several mainstream theories and a number of frameworks, we propose an integrated framework. The basic constructs in the travel mode choice function are utilities, constraints, attitudes, and habits. With a hierarchical structure and heuristic rules, the travel mode choice function is modified by individual characteristics and travel characteristics. The framework explicitly presents several dynamic processes, including the perception process on the environment, attitude formation process, habit formation process, interactions among an individual's own behaviors, interactions among travelers, feedback from travel to the built and social environments, and feedback from other behaviors to the built and social environments. For utilitarian walking, the framework may contribute to the study design, data collection, adoption of new research methods, and provide indications for policy interventions.

Peer review and competition in the Art Exhibition Game

To investigate the effect of competitive incentives under peer review, we designed a novel experimental setup called the Art Exhibition Game. We present experimental evidence of how competition introduces both positive and negative effects when creative artifacts are evaluated and selected by peer review. Competition proved to be a double-edged sword: on the one hand, it fosters innovation and product diversity, but on the other hand, it also leads to more unfair reviews and to a lower level of agreement between reviewers. Moreover, an external validation of the quality of peer reviews during the laboratory experiment, based on 23,627 online evaluations on Amazon Mechanical Turk, shows that competition does not significantly increase the level of creativity. Furthermore, the higher rejection rate under competitive conditions does not improve the average quality of published contributions, because more high-quality work is also rejected. Overall, our results could explain why many ground-breaking studies in science end up in lower-tier journals. Differences and similarities between the Art Exhibition Game and scholarly peer review are discussed and the implications for the design of new incentive systems for scientists are explained.

Representation and Incorporation of Close Others’ Responses

We propose a new model of social influence, which can occur spontaneously and in the absence of typically assumed motives. We assume that perceivers routinely construct representations of other people’s experiences and responses (beliefs, attitudes, emotions, and behaviors), when observing others’ responses or simulating the responses of unobserved others. Like representations made accessible by priming, these representations may then influence the process that generates perceivers’ own responses, without intention or awareness, especially when there is a strong social connection to the other. We describe evidence for the basic properties and important moderators of this process, which distinguish it from other mechanisms such as informational, normative, or social identity influence. The model offers new perspectives on the role of others’ values in producing cultural differences, the persistence and power of stereotypes, the adaptive reasons for being influenced by others’ responses, and the impact of others’ views about the self.

Discrepancy and Disliking Do Not Induce Negative Opinion Shifts

Both classical social psychological theories and recent formal models of opinion differentiation and bi-polarization assign a prominent role to negative social influence. Negative influence is defined as shifts away from the opinion of others and hypothesized to be induced by discrepancy with or disliking of the source of influence. There is strong empirical support for the presence of positive social influence (a shift towards the opinion of others), but evidence that large opinion differences or disliking could trigger negative shifts is mixed. We examine positive and negative influence with controlled exposure to opinions of other individuals in one experiment and with opinion exchange in another study. Results confirm that similarities induce attraction, but results do not support that discrepancy or disliking entails negative influence. Instead, our findings suggest a robust positive linear relationship between opinion distance and opinion shifts.

Neighbourhood social capital: measurement issues and associations with health outcomes

We compared ecometric neighbourhood scores of social capital (contextual variation) to mean neighbourhood scores (individual and contextual variation), using several health-related outcomes (i.e. self-rated health, weight status and obesity-related behaviours). Data were analysed from 5,900 participants in the European SPOTLIGHT survey. Factor analysis of the 13-item social capital scale revealed two social capital constructs: social networks and social cohesion. The associations of ecometric and mean neighbourhood-level scores of these constructs with self-rated health, weight status and obesity-related behaviours were analysed using multilevel regression analyses, adjusted for key covariates. Analyses using ecometric and mean neighbourhood scores, but not mean neighbourhood scores adjusted for individual scores, yielded similar regression coefficients. Higher levels of social network and social cohesion were not only associated with better self-rated health, lower odds of obesity and higher fruit consumption, but also with prolonged sitting and less transport-related physical activity. Only associations with transport-related physical activity and sedentary behaviours were associated with mean neighbourhood scores adjusted for individual scores. As analyses using ecometric scores generated the same results as using mean neighbourhood scores, but different results when using mean neighbourhood scores adjusted for individual scores, this suggests that the theoretical advantage of the ecometric approach (i.e. teasing out individual and contextual variation) may not be achieved in practice. The different operationalisations of social network and social cohesion were associated with several health outcomes, but the constructs that appeared to represent the contextual variation best were only associated with two of the outcomes.

Stopping the Drama

Drawing on theories of social norms, we study the relative influence of female and male students using a year-long, network-based field experiment of an anti-harassment intervention program in a high school. A randomly selected subset of highly connected students participated in the intervention. We test whether these highly connected females and males influenced other students equally when students and teachers considered the problem of “drama”—peer conflict and harassment—to be associated with girls more than with boys. Exposure to male, but not female, intervention students caused decreased perceptions of the acceptability of harassment and decreased participation in negative behavior. Status beliefs became activated through the intervention program: gender differences in influence stem from higher levels of respect afforded to highly connected males in the program. The results support an account of social influence as it occurs across time in conjunction with other group processes.

Modeling spatial segregation and travel cost influences on utilitarian walking: Towards policy intervention

We develop an agent-based model of utilitarian walking and use the model to explore spatial and socioeconomic factors affecting adult utilitarian walking and how travel costs as well as various educational interventions aimed at changing attitudes can alter the prevalence of walking and income differentials in walking. The model is validated against US national data. We contrast realistic and extreme parameter values in our model and test effects of changing these parameters across various segregation and pricing scenarios while allowing for interactions between travel choice and place and for behavioral feedbacks. Results suggest that in addition to income differences in the perceived cost of time, the concentration of mixed land use (differential density of residences and businesses) are important determinants of income differences in walking (high income walk less), whereas safety from crime and income segregation on their own do not have large influences on income differences in walking. We also show the difficulty in altering walking behaviors for higher income groups who are insensitive to price and how adding to the cost of driving could increase the income differential in walking particularly in the context of segregation by income and land use. We show that strategies to decrease positive attitudes towards driving can interact synergistically with shifting cost structures to favor walking in increasing the percent of walking trips. Agent-based models, with their ability to capture dynamic processes and incorporate empirical data, are powerful tools to explore the influence on health behavior from multiple factors and test policy interventions.

Delinquency and association with behavioral disorders and substance abuse

Objective: to determine the incidence and associations of attention deficit-hyperactivity disorder (ADHD), conduct disorder (CD), and substance abuse disorder (SAD) in adolescents in conflict with the law in a Brazilian cohort. Methods: the Brazilian version of the Schedule for Affective Disorders and Schizophrenia for School Aged-Children (K-SADS-PL) was administered to 69 adolescent boys who were incarcerated for 45 days in the city of Curitiba, Brazil. Results: mean age was 15.5 years (range, 12-16.9 years) and most adolescents originated from disadvantaged social classes (87%). They resided in neighborhoods on the outskirts of the city or towns in the greater metropolitan area. Truancy and low educational achievement were common, with 73.9% not currently attending school and 43.4% not having finished the 5th grade. The great majority lived in single-parent families and many had relatives who themselves had problems with the law. Psychiatric disorders were apparent in 81.1% of the subjects, with the most common disorders being CD (59.4%), SAD (53.6%), and ADHD (43.5%). Both ADHD (p <0.001) and CD (p <0.01) had significant associations with substance abuse. Conclusion: in male adolescents in conflict with the law, ADHD, CD, and SAD were all found to be associated with delinquency.