Social consensus through the influence of committed minorities

Resisting harmful social norms change using social inoculation

Social norms are thought to spread through processes of collective contagion, requiring multiple social contacts for diffusion. The spread of harmful social norms is heightened with the spread of misinformation online, especially as falsehoods spread faster than truth. Social inoculation, an intervention approach developed in the 1960s, is an effective prophylactic against harmful social norms spread. Using the analogy of a medical vaccine to develop resistance to viruses encountered in the wild, the approach exposes individuals to weakened forms of arguments they will encounter in naturalistic settings, in order to psychologically inoculate them against falsehoods and harmful social influence. Inoculation interventions have demonstrated effectiveness in the short-term and treatment effects can persist with regular 'booster' sessions.

Development of a Global Physical Literacy (GloPL) Action Framework: Study protocol for a consensus process

BACKGROUND

The holistic concept of physical literacy (PL) has gained growing attention in recent research, policy, and practice. Many important policy documents of the physical activity and educational fields (e.g., Global Action Plan on Physical Activity 2018-2030 by the World Health Organization, UNESCO's Quality Physical Education guidelines for policymakers) have specified PL. However, a clear framework for action is needed, as most initiatives across the world are fragmented, lack a prospective orientation, can benefit from conceptual clarification, and are not linked to effective translation into practice. Therefore, we aim to consensually develop a Global Physical Literacy (GloPL) Action Framework to define goals and principles (asking what is needed) as well as actions and ways (asking how these can be achieved) to move PL forward.

MATERIALS AND METHODS

We apply a three-stage group Delphi technique involving three representation groups: (a) geographical representatives to achieve global coverage of perspectives; (b) representatives of special thematic interest reflecting prominent gaps of current PL activities; and (c) representatives of societies from the broad field of physical activity and health to facilitate dissemination. The process will begin with an individual pre-Delphi exercise, in which experts generate initial ideas for the framework, followed by a four-eye document analysis to derive themes for the discussion. Subsequently, the experts will meet face-to-face in three online rounds to discuss and prioritize the themes. Interspersed formal voting with pre-defined agreement thresholds (via descriptive statistics) will inform the inclusion of themes within the final framework.

CONCLUSIONS

A global consensus on goals, principles, actions, and ways for the development of PL has the potential to provide a largely accepted roadmap for future activities in research, policy, and practice. The co-production approach will help disseminate the GloPL Action Framework and benefit work in relevant application fields of physical activity and health worldwide.

Spontaneous opinion swings in the voter model with latency

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture this feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling the phenomenon of opinion swings, often observed in diverse social contexts.

Audience segmentation approach to conservation messaging for transforming the exotic pet trade

Advancing transformative change for sustainability requires population-wide behavior change. Yet, many behavioral interventions tackling environmental problems only examine average effects on the aggregate, overlooking the heterogeneous effects in a population. We developed and preregistered a novel audience segmentation approach to test the diverse impact of conservation messaging on reducing demand for exotic pets (private action - i.e., desire to own exotic pets or visit wildlife entertainment places) and fostering citizen engagement for system-wide change (civic action - e.g., signing a petition or participating in a protest against the exotic pet trade). Through an online survey with US participants (n = 2953), we identified 4 population segments (early adopters, early majority, late majority, and laggards), representing varying levels of commitment to wildlife conservation and then randomly assigned each segment to one of 3 messaging conditions. Messages highlighting negative consequences of the exotic pet trade and the power of collective action for system change effectively promoted private action among all segments except early adopters (ηp 2 = 0.005). Among civic actions, only the collective action message motivated early adopters and the early majority to sign petitions (φC = 0.193 and φC = 0.097, respectively). Furthermore, the 4 segments showed distinct reasoning for action and inaction on wildlife conservation, with certain relational values, such as care, serving as both motivations and barriers to action. These findings highlight the need for targeted behavioral interventions across diverse populations.

Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

Shaping new norms for AI

As artificial intelligence (AI) becomes increasingly integrated into our lives, the need for new norms is urgent. However, AI evolves at a much faster pace than the characteristic time of norm formation, posing an unprecedented challenge to our societies. This paper examines possible criticalities of the processes of norm formation surrounding AI. It focuses on how new norms can be established, rather than on what these norms should be. It distinguishes different scenarios based on the centralization or decentralization of the norm formation process, analysing the cases where new norms are shaped by formal authorities or informal institutions, or emerge spontaneously in a bottom-up fashion. On the latter point, the paper reports a conversation with ChatGPT in which the LLM discusses some of the emerging norms it has observed. Far from seeking exhaustiveness, this article aims to offer readers interpretive tools to frame society's response to the growing pervasiveness of AI. An outlook on how AI could influence the formation of future social norms emphasizes the importance for open societies to anchor their formal deliberation process in an open, inclusive and transparent public discourse. This article is part of the theme issue 'Social norm change: drivers and consequences'.

Implementing adaptive e-learning for newborn care in Tanzania: an observational study of provider engagement and knowledge gains

INTRODUCTION

To improve healthcare provider knowledge of Tanzanian newborn care guidelines, we developed adaptive Essential and Sick Newborn Care (aESNC), an adaptive e-learning environment. The objectives of this study were to (1) assess implementation success with use of in-person support and nudging strategy and (2) describe baseline provider knowledge and metacognition.

METHODS

6-month observational study at one zonal hospital and three health centres in Mwanza, Tanzania. To assess implementation success, we used the Reach, Efficacy, Adoption, Implementation and Maintenance framework and to describe baseline provider knowledge and metacognition we used Howell's conscious-competence model. Additionally, we explored provider characteristics associated with initial learning completion or persistent activity.

RESULTS

aESNC reached 85% (195/231) of providers: 75 medical, 53 nursing and 21 clinical officers; 110 (56%) were at the zonal hospital and 85 (44%) at health centres. Median clinical experience was 4 years (IQR 1-9) and 45 (23%) had previous in-service training for both newborn essential and sick newborn care. Efficacy was 42% (SD ±17%). Providers averaged 78% (SD ±31%) completion of initial learning and 7% (SD ±11%) of refresher assignments. 130 (67%) providers had ≥1 episode of inactivity >30 day, no episodes were due to lack of internet access. Baseline conscious-competence was 53% (IQR: 38%-63%), unconscious-incompetence 32% (IQR: 23%-42%), conscious-incompetence 7% (IQR: 2%-15%), and unconscious-competence 2% (IQR: 0%-3%). Higher baseline conscious-competence (OR 31.6 (95% CI 5.8 to 183.5)) and being a nursing officer (aOR: 5.6 (95% CI 1.8 to 18.1)), compared with medical officer, were associated with initial learning completion or persistent activity.

CONCLUSION

aESNC reach was high in a population of frontline providers across diverse levels of care in Tanzania. Use of in-person support and nudging increased reach, initial learning and refresher assignment completion, but refresher assignment completion remains low. Providers were often unaware of knowledge gaps, and lower baseline knowledge may decrease initial learning completion or activity. Further study to identify barriers to adaptive e-learning normalisation is needed.

Facilitating cooperation in human-agent hybrid populations through autonomous agents

Cooperative AI has shown its effectiveness in solving the conundrum of cooperation. Understanding how cooperation emerges in human-agent hybrid populations is a topic of significant interest, particularly in the realm of evolutionary game theory. In this article, we scrutinize how cooperative and defective Autonomous Agents (AAs) influence human cooperation in social dilemma games with a one-shot setting. Focusing on well-mixed populations, we find that cooperative AAs have a limited impact in the prisoner's dilemma games but facilitate cooperation in the stag hunt games. Surprisingly, defective AAs can promote complete dominance of cooperation in the snowdrift games. As the proportion of AAs increases, both cooperative and defective AAs have the potential to cause human cooperation to disappear. We then extend our investigation to consider the pairwise comparison rule and complex networks, elucidating that imitation strength and population structure are critical for the emergence of human cooperation in human-agent hybrid populations.

Mathematical modeling of disinformation and effectiveness of mitigation policies

Disinformation is spread to manipulate public opinion for malicious purposes. Mathematical modeling was used to examine and optimize several strategies for combating disinformation-content moderation, education, and counter-campaigns. We implemented these strategies in a modified binary agreement model and investigated their impacts on properties of the tipping point. Social interactions were described by weighted, directed, and heterogeneous networks. Real social network data was examined as well. We find that content moderation achieved by removing randomly selected agents who spread disinformation is comparable to that achieved by removing highly influential agents; removing disinformation anywhere in a network could be an effective way to counter disinformation. An education strategy that increases public skepticism was more effective than one that targets already biased agents. Successful counter-campaign strategies required a substantial population of agents to influence other agents to oppose disinformation. These results can be used to inform choices of effective strategies for combating disinformation.

Chaos, periodic structures, and multistability: Complex dynamical behaviors of an eco-epidemiological model in parameter planes

The environmental characteristics of a biological system are imbibed in some particular parameters of that system. Significant changes in any system parameter exert influence on the system dynamics as well as the persistence of interacting species. In this article, we explore the rich and tangled dynamics of an eco-epidemiological system by studying different parametric planes of the system. In the parameter planes, we find a variety of complex and subtle properties of the system, like the presence of a variety of intricate regular structures within irregular regimes, that cannot be found through a single parameter variation. Also, we find a new type of structure like an "eye" in a parametric plane. We notice the bistability between distinct pairs of attractors and also identify the coexistence of three periodic attractors. The most notable observation of this study is the coexistence of three periodic attractors and a chaotic attractor, which is a rare occurrence in biological systems. We also plot the basins for each set of coexisting attractors and see the existence of fractal basins in the system, which look like a "conch." The appearance of fractal basins in a system causes enormous complications in predicting the system's state in the long run. Variations in initial conditions and changes in parameters in parametric planes are key to managing the behavior of a system.

Feasibility of an Adaptive E-Learning Environment to Improve Provider Proficiency in Essential and Sick Newborn Care in Mwanza, Tanzania

Introduction

To improve healthcare provider knowledge of Tanzanian newborn care guidelines, we developed adaptive Essential and Sick Newborn Care (aESNC), an adaptive e-learning environment (AEE). The objectives of this study were to 1) assess implementation success with use of in-person support and nudging strategy and 2) describe baseline provider knowledge and metacognition.

Methods

6-month observational study at 1 zonal hospital and 3 health centers in Mwanza, Tanzania. To assess implementation success, we used the RE-AIM framework and to describe baseline provider knowledge and metacognition we used Howell's conscious-competence model. Additionally, we explored provider characteristics associated with initial learning completion or persistent activity.

Results

aESNC reached 85% (195/231) of providers: 75 medical, 53 nursing, and 21 clinical officers; 110 (56%) were at the zonal hospital and 85 (44%) at health centers. Median clinical experience was 4 years [IQR 1,9] and 45 (23%) had previous in-service training for both newborn essential and sick newborn care. Efficacy was 42% (SD±17%). Providers averaged 78% (SD±31%) completion of initial learning and 7%(SD±11%) of refresher assignments. 130 (67%) providers had ≥1 episode of inactivity >30 day, no episodes were due to lack of internet access. Baseline conscious-competence was 53% [IQR:38-63%], unconscious-incompetence 32% [IQR:23-42%], conscious-incompetence 7% [IQR:2-15%], and unconscious-competence 2% [IQR:0-3%]. Higher baseline conscious-competence (OR 31.6 [95%CI:5.8, 183.5) and being a nursing officer (aOR: 5.6 [95%CI:1.8, 18.1]), compared to medical officer) were associated with initial learning completion or persistent activity.

Conclusion

aESNC reach was high in a population of frontline providers across diverse levels of care in Tanzania. Use of in-person support and nudging increased reach, initial learning, and refresher assignment completion, but refresher assignment completion remains low. Providers were often unaware of knowledge gaps, and lower baseline knowledge may decrease initial learning completion or activity. Further study to identify barriers to adaptive e-learning normalization is needed.

Small bots, big impact: solving the conundrum of cooperation in optional Prisoner's Dilemma game through simple strategies

Cooperation plays a crucial role in both nature and human society, and the conundrum of cooperation attracts the attention from interdisciplinary research. In this study, we investigated the evolution of cooperation in optional Prisoner's Dilemma games by introducing simple bots. We focused on one-shot and anonymous games, where the bots could be programmed to always cooperate, always defect, never participate or choose each action with equal probability. Our results show that cooperative bots facilitate the emergence of cooperation among ordinary players in both well-mixed populations and a regular lattice under weak imitation scenarios. Introducing loner bots has no impact on the emergence of cooperation in well-mixed populations, but it facilitates the dominance of cooperation in regular lattices under strong imitation scenarios. However, too many loner bots on a regular lattice inhibit the spread of cooperation and can eventually result in a breakdown of cooperation. Our findings emphasize the significance of bot design in promoting cooperation and offer useful insights for encouraging cooperation in real-world scenarios.

Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs

Although ubiquitous, interactions in groups of individuals are not yet thoroughly studied. Frequently, single groups are modeled as critical-mass dynamics, which is a widespread concept used not only by academics but also by politicians and the media. However, less explored questions are how a collection of groups will behave and how their intersection might change the dynamics. Here, we formulate this process as binary-state dynamics on hypergraphs. We showed that our model has a rich behavior beyond discontinuous transitions. Notably, we have multistability and intermittency. We demonstrated that this phenomenology could be associated with community structures, where we might have multistability or intermittency by controlling the number or size of bridges between communities. Furthermore, we provided evidence that the observed transitions are hybrid. Our findings open new paths for research, ranging from physics, on the formal calculation of quantities of interest, to social sciences, where new experiments can be designed.

We are preventing some dementias now-But how? The Potamkin lecture

Most dementias are untreatable and their prevalence is increasing around the world. However, the incidence of dementia is declining in some countries. We need to find out urgently why and how and apply the lessons promptly and widely. Given the multiplicity and variability of environmental, socioeconomic, and individual risk and protective factors, the approach needs to be comprehensive, customized to work in a particular setting, and cost effective, to justify the needed funding. Stroke, heart disease, and dementia share the same major preventable risk and protective factors and pose risks for each other. Preventing them together might result in efficiencies and economies of scale. Prevention can best occur in existing actionable population health units through established leaders in government, non-governmental organizations, and the community, around a positive message of promoting brain health as the key to health, productivity, and well-being.

Breaking the cycles of violence with narrative exposure: Development and feasibility of NETfacts, a community-based intervention for populations living under continuous threat

BACKGROUND

Interpersonal violence damages mental health and frequently leads to aggressive defence strategies. If survivors are subsequently blamed for the events, both consequences worsen. Stigma flourishes, especially when survivors are silenced so that details of the trauma remain unknown. Breaking the secrecy both at the individual and collective level is key to enable the healing and reconciliation of individuals and communities living under continuous threat.

METHOD

The NETfacts health system is a stepped care model with three components: (1) Narrative Exposure Therapy (NET), an evidence-based trauma therapy that includes survivor testimony (2) NET for Forensic Offender Rehabilitation (FORNET) acknowledges that perpetrators are frequently also victims and assists in reducing aggression and the attraction to violence, and (3) a community intervention disseminating and discussing Facts derived from NET treatment (NETfacts) to challenge the collective avoidance of atrocities and other traumatic material. The intervention was piloted in a community with 497 adult residents in Eastern Democratic Republic of Congo. The willingness of clients to consent to sharing their anonymised testimonies (with a focus on sexual violence survivors and ex-combatants) was investigated, together with other components of feasibility including security and clinical safety, extent of support of respected local authorities and participation rates. As secondary outcomes, clinical and social measures were assessed before and post NETfacts among 200 village residents of whom 160 self-enrolled and 40 had not participated in any form of treatment.

RESULTS

Implementation was feasible with 248 clients from a partner project giving consent to use their testimonies and high support of respected local authorities and participation rates (56% of residents self-enrolled in NETfacts). Immediate beneficial effects were shown for posttraumatic stress and rejection of rape myths among NETfacts participants who experienced multiple traumatic events in their own past. Attitudes towards ex-combatants improved and the perceived lack of social acknowledgement after trauma increased independent from participation. No significant change was observed for depressive symptoms.

CONCLUSION

NETfacts is a feasible and promising approach to challenge the culture of secrecy surrounding trauma, suppression and social exclusion. Long term effectiveness requires further evaluation.

Facilitating innovation diffusion in social networks using dynamic norms

Dynamic norms have recently emerged as a powerful method to encourage individuals to adopt an innovation by highlighting a growing trend in its uptake. However, there have been no concrete attempts to understand how this individual-level mechanism might shape the collective population behavior. Here, we develop a framework to examine this by encapsulating dynamic norms within a game-theoretic mathematical model for innovation diffusion. Specifically, we extend a network coordination game by incorporating a probabilistic mechanism where an individual adopts the action with growing popularity, instead of the standard best-response update rule; the probability of such an event captures the population's "sensitivity" to dynamic norms. Theoretical analysis reveals that sensitivity to dynamic norms is key to facilitating social diffusion. Small increases in sensitivity reduces the advantage of the innovation over status quo or the number of initial innovators required to unlock diffusion, while a sufficiently large sensitivity alone guarantees diffusion.

Love Is Blind! Exploring the Impact of Brand Love on eWOM in Chinese Hospitality Industry

The rising penetration and value of online information reviews in the hospitality industry have been extensively examined. People are increasingly interacting on interactive online media, prompting firms to create online communities to share electronic word-of-mouth (eWOM) with them and with one another in order to increase brand love. This study seeks to discover what drives consumers to participate in these online brand communities. To examine the research model, an online survey was conducted on 508 consumers who had shared eWOM on social networking sites (SNS). Structural equation modeling (SEM) was employed to test all hypotheses. The findings show that each of the variables has a high impact on brand love, with information quality and virtual interaction having the most significant impact. As a result of brand love, eWOM is significantly increased. Additionally, findings show that the gender gap in the online setting is increasing, as the influence of all characteristics then reward of online brand communities on brand love was adaptable among both male and female associates. Considering the effects of online features (information quality, system quality, virtual interaction, and reward) on online brand communities' impact on brand love and eWOM. Online hotels manager is advised to carefully develop their marketing strategies to retain and attract new consumers. Furthermore, online hotels manager can use the findings from this study to understand the consequences when eWOM is strongly influenced by online brand communities' features. This study is one of the first to illustrate and empirically confirm insight into how online features affect brand lovers in online brand communities. The study adds to the body of knowledge concerning the effectiveness of social media marketing in the Chinese hotel sector.

The New Brain Age

The pandemic is transforming neurology. Long Covid will linger, neurological diseases will increase, and technology, artificial intelligence and new virtual worlds will usher a new age of the brain and new roles for neurologists.The pandemic has compelled international collaboration, greatly increased communications and accelerated drug and vaccines approvals. It also dramatized the close interconnection of cognitive, mental, and social health and their relevance to building back better health, education, work and leisure. Brain health is the key to health, productivity, and well-being.Neurologists are best placed to lead brain knowledge integration and application through the unifying theme of brain health by becoming advocates, healers, and guardians of the brain.

A research agenda for the study of social norm change

Social norms have been investigated across many disciplines for many years, but until recently, studies mainly provided indirect, implicit and correlational support for the role of social norms in driving behaviour. To understand how social norms, and in particular social norm change, can generate a large-scale behavioural change to deal with some of the most pressing challenges of our current societies, such as climate change and vaccine hesitancy, we discuss and review several recent advances in social norm research that enable a more precise underpinning of the role of social norms: how to identify their existence, how to establish their causal effect on behaviour and when norm change may pass tipping points. We advocate future research on social norms to study norm change through a mechanism-based approach that integrates experimental and computational methods in theory-driven, empirically calibrated agent-based models. As such, social norm research may move beyond unequivocal praising of social norms as the missing link between self-interested behaviour and observed cooperation or as the explanation for (the lack of) social tipping. It provides the toolkit to understand explicitly where, when and how social norms can be a solution to solve large-scale problems, but also to recognize their limits. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Temporal network epistemology: On reaching consensus in a real-world setting

This work develops the concept of the temporal network epistemology model enabling the simulation of the learning process in dynamic networks. The results of the research, conducted on the temporal social network generated using the CogSNet model and on the static topologies as a reference, indicate a significant influence of the network temporal dynamics on the outcome and flow of the learning process. It has been shown that not only the dynamics of reaching consensus is different compared to baseline models but also that previously unobserved phenomena appear, such as uninformed agents or different consensus states for disconnected components. It has also been observed that sometimes only the change of the network structure can contribute to reaching consensus. The introduced approach and the experimental results can be used to better understand the way how human communities collectively solve both complex problems at the scientific level and to inquire into the correctness of less complex but common and equally important beliefs' spreading across entire societies.

From Reddit to Wall Street: the role of committed minorities in financial collective action

In January 2021, retail investors coordinated on Reddit to target short-selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalize the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalize the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit pre-dated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance.

An integrative framework for transformative social change: a case in global wildlife trade

To achieve a sustainable future, it is imperative to transform human actions collectively and underlying social structures. Decades of research in social sciences have offered complementary insights into how such transformations might occur. However, these insights largely remain disjunct and of limited scope, such that strategies for solving global environmental challenges remain elusive. There is a need to integrate approaches focusing on individuals and social structures to understand how individual actions influence and are in turn influenced by social structures and norms. In this paper, we synthesize a range of insights across different schools of thought and integrate them in a novel framework for transformative social change. Our framework explains the relationships among individual behaviors, collective actions, and social structures and helps change agents guide societal transitions toward environmental sustainability. We apply this framework to the global wildlife trade-which presents several distinct challenges of human actions, especially amidst the Covid-19 pandemic-and identify pathways toward transformative change. One key distinction we make is between different individual actions that comprise the practice itself (e.g., buying wildlife products; private action) and those that push for a broader system change in practice (e.g., signaling (dis)approval for wildlife consumption; social-signaling action, and campaigning for policies that end unsustainable wildlife trade; system-changing action). In general, transformative change will require an integrative approach that includes both structural reforms and all three classes of individual action.

Robot swarm democracy: the importance of informed individuals against zealots

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

Collective patterns of social diffusion are shaped by individual inertia and trend-seeking

Social conventions change when individuals collectively adopt an alternative over the status quo, in a process known as social diffusion. Our repeated trials of a multi-round experiment provided data that helped motivate the proposal of an agent-based model of social diffusion that incorporates inertia and trend-seeking, two behavioural mechanisms that are well documented in the social psychology literature. The former causes people to stick with their current decision, the latter creates sensitivity to population-level changes. We show that such inclusion resolves the contradictions of existing models, allowing to reproduce patterns of social diffusion which are consistent with our data and existing empirical observations at both the individual and population level. The model reveals how the emergent population-level diffusion pattern is critically shaped by the two individual-level mechanisms; trend-seeking guarantees the diffusion is explosive after the diffusion process takes off, but inertia can greatly delay the time to take-off.

Dynamical system model predicts when social learners impair collective performance

In collective decision-making systems, such as committees and governments, many individuals follow others instead of evaluating the options on their own. Can a group settle on the option with higher merit when social learners prevail? Previous research has reached mixed conclusions because collective decisions emerge from a complex interaction of cognitive and social factors, which are rarely studied together. This paper develops a simple yet general mathematical framework to study this interaction and predicts a critical threshold for the proportion of social learners, above which an option may prevail regardless of its merit. The results suggest predictable limits to the proportion of social learners in collective situations from teamwork to democratic elections, beyond which the collective performance is affected negatively.

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

The Institute for Health care Quality, Safety, and Efficiency: A Comprehensive Approach to Improving Organization-Level Quality Performance

Despite decades of effort to drive quality improvement, many health care organizations still struggle to optimize their performance on quality metrics. The advent of publicly reported quality rankings and ratings allows for greater visibility of overall organizational performance, but has not provided a roadmap for sustained improvement in these assessments. Most quality training programs have focused on developing knowledge and skills in pursuit of individual and project-level improvements. To date, no training program has been associated with improvements in overall organization-level, publicly reported measures. In 2012, the Institute for Health care Quality, Safety, and Efficiency was launched, which is an integrated set of quality and safety training programs, with a focus on leadership development and support of performance improvement through data analytics and intensive coaching. This effort has trained nearly 2000 individuals and has been associated with significant improvement in organization-level quality rankings and ratings, offering a framework for organizations seeking systematic, long-term improvement.

Structure Reversal of Online Public Opinion for the Heterogeneous Health Concerns under NIMBY Conflict Environmental Mass Events in China

Public opinions play an important role in the formation of Not in My Back Yard (NIMBY) conflict environmental mass events. Due to the continual interactions between affected groups and the corresponding government responses surrounding the public interests related to health, online public opinion structure reversal arises frequently in NIMBY conflict events, which pose a serious threat to social public security. To explore the underlying mechanism, this paper introduces an improved dynamic model which considers multiple heterogeneities in health concerns and social power of individuals and in government's ability. The experimental results indicate that the proposed model can provide an accurate description of the entire process of online public opinion structure reversal in NIMBY conflict environmental mass incidents on the Internet. In particular, the proportion of the individual agents without health interest appeals will delay the online public opinion structure reversal, and the upper threshold remains within regulatory limits from 0.4 to 0.5. Unlike some previous results that show that the guiding powers of the opinion leaders varied over its ratio in a fixed-sized group, our results suggest that the impact of opinion leaders is of no significant difference for the time of structure reversal after it increased to about 6%. Furthermore, a double threshold effect of online structure reversal during the government's response process was observed. The findings are beneficial for understanding and explaining the process of online public opinion structure reversal in NIMBY conflict environmental mass incidents, and provides theoretical and practical implications for guiding public or personal health opinions on the Internet and for a governments' effective response to them.

A bird's-eye view of naming game dynamics: From trait competition to Bayesian inference

The present contribution reviews a set of different versions of the basic naming game model, differing in the underlying topology or in the mechanisms regulating the interactions between agents. We include also a Bayesian naming game model recently introduced, which merges the social dynamics of the basic naming game model with the Bayesian learning framework introduced by Tenenbaum and co-workers. The latter model goes beyond the fixed nature of names and concepts of standard semiotic dynamics models and the corresponding one-shot learning process by describing dynamically how agents can generalize a concept from a few examples, according to principles of Bayesian inference.

Social contagion models on hypergraphs

Our understanding of the dynamics of complex networked systems has increased significantly in the last two decades. However, most of our knowledge is built upon assuming pairwise relations among the system's components. This is often an oversimplification, for instance, in social interactions that occur frequently within groups. To overcome this limitation, here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. Our analyses show that the model has a vast parameter space, with first- and second-order transitions, bistability, and hysteresis. Phenomenologically, we also extend the concept of latent heat to social contexts, which might help understanding oscillatory social behaviors. Our work unfolds the research line of higher-order models and the analytical treatment of hypergraphs, posing new questions and paving the way for modeling dynamical processes on higher-order structures.

Nonuniversal opinion dynamics driven by opposing external influences

We study the opinion dynamics of a generalized voter model in which N voters are additionally influenced by two opposing news sources whose effect is to promote political polarization. As the influence of these news sources is increased, the mean time to reach consensus scales nonuniversally as N^{α}. The parameter α quantifies the influence of the news sources and increases without bound as the news sources become increasingly influential. The time to reach a politically polarized state, in which roughly equal fractions of the populations are in each opinion state, is generally short, and the steady-state opinion distribution exhibits a transition from near consensus to a politically polarized state as a function of α.

Efficient spread-size approximation of opinion spreading in general social networks

In contemporary society, understanding how information, such as trends and viruses, spreads in various social networks is an important topic in many areas. However, it is difficult to mathematically measure how widespread the information is, especially for a general network structure. There have been studies on opinion spreading, but many studies are limited to specific spreading models such as the susceptible-infected-recovered model and the independent cascade model, and it is difficult to apply these studies to various situations. In this paper, we first suggest a general opinion spreading model (GOSM) that generalizes a large class of popular spreading models. In this model, each node has one of several states, and the state changes through interaction with neighboring nodes at discrete time intervals. Next, we show that many GOSMs have a stable property that is a GOSM version of a uniform equicontinuity. Then, we provide an approximation method to approximate the expected spread size for stable GOSMs. For the approximation method, we propose a concentration theorem that guarantees that a generalized mean-field theorem calculates the expected spreading size within small error bounds for finite time steps for a slightly dense network structure. Furthermore, we prove that a "single simulation" of running the Monte Carlo simulation is sufficient to approximate the expected spreading size. We conduct experiments on both synthetic and real-world networks and show that our generalized approximation method well predicts the state density of the various models, especially in graphs with a large number of nodes. Experimental results show that the generalized mean-field approximation and a single Monte Carlo simulation converge as shown in the concentration theorem.

"Tipping point" concept analysis in the family caregiving context

AIM

Analyze the concept "tipping point" in the older adult family caregiving context to further knowledge about caregiving families, enhancing transdisciplinary theory, research, and practice.

BACKGROUND

While used commonly in some disciplines, how "tipping point" has been used in health care, generally, and in relation to caregiving families, specifically, is less clear. This project was conducted to offer conceptual clarity to tipping point.

DESIGN

Walker and Avant's framework.

DATA SOURCE

Searches of scholarly literature in PsycINFO, CINAHL, and PubMed using the search term "tipping point" in either title or abstract.

REVIEW METHODS

Definitions used were extracted; instances when the concept was implied but the actual term "tipping point" was not used and contexts where the term was used or implied were identified.

RESULTS

The composite definition of a caregiving tipping point is a seemingly abrupt, severe, and absolute change event involving either the older adult or caregiver(s), or both that indicates a breakdown in the status quo of the caregiving system.

CONCLUSIONS

Transdisciplinary research, care, and policy should treat caregiving families as complex systems, use longitudinal assessments, and include colloquial communication. Early detection of impending tipping points will provide family-centered decisional support and enhance families' quality of life and safety.

Influence and seepage: An evidence-resistant minority can affect public opinion and scientific belief formation

Some well-established scientific findings may be rejected by vocal minorities because the evidence is in conflict with political views or economic interests. For example, the tobacco industry denied the medical consensus on the harms of smoking for decades, and the clear evidence about human-caused climate change is currently being rejected by many politicians and think tanks that oppose regulatory action. We present an agent-based model of the processes by which denial of climate change can occur, how opinions that run counter to the evidence can affect the scientific community, and how denial can alter the public discourse. The model involves an ensemble of Bayesian agents, representing the scientific community, that are presented with the emerging historical evidence of climate change and that also communicate the evidence to each other. Over time, the scientific community comes to agreement that the climate is changing. When a minority of agents is introduced that is resistant to the evidence, but that enter into the scientific discussion, the simulated scientific community still acquires firm knowledge but consensus formation is delayed. When both types of agents are communicating with the general public, the public remains ambivalent about the reality of climate change. The model captures essential aspects of the actual evolution of scientific and public opinion during the last 4 decades.

Experimental evidence for tipping points in social convention

Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions. When minority groups reached the critical mass-that is, the critical group size for initiating social change-they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.

Does the Mexican sugar-sweetened beverage tax have a signaling effect? ENSANUT 2016

OBJECTIVE

To evaluate the potential signaling effect of the Mexican tax on sugar-sweetened beverages (SSBs) by analyzing the association between awareness of and opinions about its effectiveness with current consumption of taxed SSBs and with a self-reported change in consumption of SSBs since the implementation of the tax. We also examined the association between psychosocial and environmental determinants of SSB consumption with current consumption of taxed SSBs and with a reported change in consumption of SSBs.

METHODS

Cross-sectional analyses of survey and food-frequency questionnaire data from the Mexican National Health and Nutrition Survey 2016. Participants were Mexican adults (20-59 years, N = 6,650). Logistic regression was used to evaluate the probability of a person reporting a decrease in SSB consumption, given their awareness of the tax, opinion about its effectiveness, psychosocial (SSB health-related beliefs, self-efficacy, and liking of SSBs) and environmental (availability of potable water) determinants. Multiple linear regression analysis was utilized to examine the association between the aforementioned factors and current consumption of taxed SSBs.

RESULTS

Compared with adults not aware, adults who were aware of the SSB tax were more likely (OR = 1.30) to report a decrease in SSB consumption (p = .012). In urban areas, adults aware of the tax drank a significantly lower amount of taxed SSBs (-15.7%; p = .023) than those not aware. Self-efficacy and liking of SSBs were significantly associated with a reported decrease in consumption and with current consumption (p < .001), while health beliefs and availability of potable water were not significantly associated with either reported change in SSB consumption or current consumption of taxed SSBs.

CONCLUSIONS

Implementation of an SSB tax accompanied by highly visible campaigns may further influence the impact of taxes on SSBs consumption. Future public health and nutrition education campaigns designed to increase knowledge and enhance motivation should be complemented by programs to assist individuals develop self-efficacy and self-regulation skills.

Meat as a Pharmakon: An Exploration of the Biosocial Complexities of Meat Consumption

In contemporary dietary advice, meat is depicted as a pharmakon: it is believed to either heal or poison the human body (and mind). Often, it also serves as a scapegoat for a wide range of public health issues and other societal problems. Related attitudes, practices, and beliefs pertain to a demarcated mode of thinking or episteme that is characteristic for the so-called post-domestic or industrialized societies. The latter are not only typified by an abundant yet largely concealed production of meat, but increasingly also by moral crisis and confusion about its nutritional meaning. For an improved appreciation of the ambiguous position of meat in human health and disease, as well as the concomitant scattering into different subject positions (e.g., the omnivore, flexitarian, vegetarian, vegan, permaculturalist, and carnivore position), an interdisciplinary approach is required. To this end, the current study tentatively combines food research with a selection of (post-structuralist) concepts from the humanities. The aim is to outline a historical and biosocial need for meat (as well as its rejection) and to analyze how its transformative effects have contributed to a polarized discourse on diet and well-being in academia and society at large. Excessive categorization (for instance with respect to meat's alleged naturalness, normalness, necessity, and niceness) and Manichean thinking in binary opposites are among the key factors that lead to impassioned yet often sterile debates between the advocates and adversaries of meat eating in a post-truth context.

The dynamics of norm change in the cultural evolution of language

What happens when a new social convention replaces an old one? While the possible forces favoring norm change-such as institutions or committed activists-have been identified for a long time, little is known about how a population adopts a new convention, due to the difficulties of finding representative data. Here, we address this issue by looking at changes that occurred to 2,541 orthographic and lexical norms in English and Spanish through the analysis of a large corpora of books published between the years 1800 and 2008. We detect three markedly distinct patterns in the data, depending on whether the behavioral change results from the action of a formal institution, an informal authority, or a spontaneous process of unregulated evolution. We propose a simple evolutionary model able to capture all of the observed behaviors, and we show that it reproduces quantitatively the empirical data. This work identifies general mechanisms of norm change, and we anticipate that it will be of interest to researchers investigating the cultural evolution of language and, more broadly, human collective behavior.

Opinion modeling on social media and marketing aspects

We introduce and discuss kinetic models of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion formation model is subsequently coupled with a kinetic model describing the spreading of popularity of a product on the Web through a social network. Numerical experiments on the underlying kinetic models show a good qualitative agreement with some measured trends of hashtags on social media websites and illustrate how companies can take advantage of the network structure to obtain at best the advertisement of their products.

Consensus and clustering in opinion formation on networks

Ideas that challenge the status quo either evaporate or dominate. The study of opinion dynamics in the socio-physics literature treats space as uniform and considers individuals in an isolated community, using ordinary differential equation (ODE) models. We extend these ODE models to include multiple communities and their interactions. These extended ODE models can be thought of as being ODEs on directed graphs. We study in detail these models to determine conditions under which there will be consensus and pluralism within the system. Most of the consensus/pluralism analysis is done for the case of one and two cities. However, we numerically show for the case of a symmetric cycle graph that an elementary bifurcation analysis provides insight into the phenomena of clustering. Moreover, for the case of a cycle graph with a hub, we discuss how having a sufficient proportion of zealots in the hub leads to the entire network sharing the opinion of the zealots.This article is part of the theme issue 'Stability of nonlinear waves and patterns and related topics'.

Zealots in the mean-field noisy voter model

The influence of zealots on the noisy voter model is studied theoretically and numerically at the mean-field level. The noisy voter model is a modification of the voter model that includes a second mechanism for transitions between states: Apart from the original herding processes, voters may change their states because of an intrinsic noisy-in-origin source. By increasing the importance of the noise with respect to the herding, the system exhibits a finite-size phase transition from a quasiconsensus state, where most of the voters share the same opinion, to one with coexistence. Upon introducing some zealots, or voters with fixed opinion, the latter scenario may change significantly. We unveil new situations by carrying out a systematic numerical and analytical study of a fully connected network for voters, but allowing different voters to be directly influenced by different zealots. We show that this general system is equivalent to a system of voters without zealots, but with heterogeneous values of their parameters characterizing herding and noisy dynamics. We find excellent agreement between our analytical and numerical results. Noise and herding or zealotry acting together in the voter model yields a nontrivial mixture of the scenarios with the two mechanisms acting alone: It represents a situation where the global-local (noise-herding) competition is coupled to a symmetry breaking (zealots). In general, the zealotry enhances the effective noise of the system, which may destroy the original quasiconsensus state, and can introduce a bias towards the opinion of the majority of zealots, hence breaking the symmetry of the system and giving rise to new phases. In the most general case we find two different transitions: a discontinuous transition from an asymmetric bimodal phase to an extreme asymmetric phase and a second continuous transition from the extreme asymmetric phase to an asymmetric unimodal phase.

The emergence of consensus: a primer

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and widely scattered across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges 'spontaneously' in the absence of centralized institutions and covers topics that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

Zealotry effects on opinion dynamics in the adaptive voter model

The adaptive voter model has been widely studied as a conceptual model for opinion formation processes on time-evolving social networks. Past studies on the effect of zealots, i.e., nodes aiming to spread their fixed opinion throughout the system, only considered the voter model on a static network. Here we extend the study of zealotry to the case of an adaptive network topology co-evolving with the state of the nodes and investigate opinion spreading induced by zealots depending on their initial density and connectedness. Numerical simulations reveal that below the fragmentation threshold a low density of zealots is sufficient to spread their opinion to the whole network. Beyond the transition point, zealots must exhibit an increased degree as compared to ordinary nodes for an efficient spreading of their opinion. We verify the numerical findings using a mean-field approximation of the model yielding a low-dimensional set of coupled ordinary differential equations. Our results imply that the spreading of the zealots' opinion in the adaptive voter model is strongly dependent on the link rewiring probability and the average degree of normal nodes in comparison with that of the zealots. In order to avoid a complete dominance of the zealots' opinion, there are two possible strategies for the remaining nodes: adjusting the probability of rewiring and/or the number of connections with other nodes, respectively.

Solution to urn models of pairwise interaction with application to social, physical, and biological sciences

We investigate a family of urn models that correspond to one-dimensional random walks with quadratic transition probabilities that have highly diverse applications. Well-known instances of these two-urn models are the Ehrenfest model of molecular diffusion, the voter model of social influence, and the Moran model of population genetics. We also provide a generating function method for diagonalizing the corresponding transition matrix that is valid if and only if the underlying mean density satisfies a linear differential equation and express the eigenvector components as terms of ordinary hypergeometric functions. The nature of the models lead to a natural extension to interaction between agents in a general network topology. We analyze the dynamics on uncorrelated heterogeneous degree sequence networks and relate the convergence times to the moments of the degree sequences for various pairwise interaction mechanisms.

On Economic Complexity and the Fitness of Nations

Complex economic systems can often be described by a network, with nodes representing economic entities and edges their interdependencies, while network centrality is often a good indicator of importance. Recent publications have implemented a nonlinear iterative Fitness-Complexity (FC) algorithm to measure centrality in a bipartite trade network, which aims to represent the ‘Fitness’ of national economies as well as the ‘Complexity’ of the products being traded. In this paper, we discuss this methodological approach and conclude that further work is needed to identify stable and reliable measures of fitness and complexity. We provide theoretical and numerical evidence for the intrinsic instability in the nonlinear definition of the FC algorithm. We perform an in-depth evaluation of the algorithm’s rankings in two real world networks at the country level: the global trade network, and the patent network in different technological domains. In both networks, we find evidence of the instabilities predicted theoretically, and show that ‘complex’ products or patents tend often to be those that countries rarely produce, rather than those that are intrinsically more difficult to produce.

Competing opinion diffusion on social networks

Opinion competition is a common phenomenon in real life, such as with opinions on controversial issues or political candidates; however, modelling this competition remains largely unexplored. To bridge this gap, we propose a model of competing opinion diffusion on social networks taking into account degree-dependent fitness or persuasiveness. We study the combined influence of social networks, individual fitnesses and attributes, as well as mass media on people's opinions, and find that both social networks and mass media act as amplifiers in opinion diffusion, the amplifying effect of which can be quantitatively characterized. We analytically obtain the probability that each opinion will ultimately pervade the whole society when there are no committed people in networks, and the final proportion of each opinion at the steady state when there are committed people in networks. The results of numerical simulations show good agreement with those obtained through an analytical approach. This study provides insight into the collective influence of individual attributes, local social networks and global media on opinion diffusion, and contributes to a comprehensive understanding of competing diffusion behaviours in the real world.

Effects of communication burstiness on consensus formation and tipping points in social dynamics

Current models for opinion dynamics typically utilize a Poisson process for speaker selection, making the waiting time between events exponentially distributed. Human interaction tends to be bursty though, having higher probabilities of either extremely short waiting times or long periods of silence. To quantify the burstiness effects on the dynamics of social models, we place in competition two groups exhibiting different speakers' waiting-time distributions. These competitions are implemented in the binary naming game and show that the relevant aspect of the waiting-time distribution is the density of the head rather than that of the tail. We show that even with identical mean waiting times, a group with a higher density of short waiting times is favored in competition over the other group. This effect remains in the presence of nodes holding a single opinion that never changes, as the fraction of such committed individuals necessary for achieving consensus decreases dramatically when they have a higher head density than the holders of the competing opinion. Finally, to quantify differences in burstiness, we introduce the expected number of small-time activations and use it to characterize the early-time regime of the system.

Modelling the spread of innovation in wild birds

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms.

Voting contagion: Modeling and analysis of a century of U.S. presidential elections

Social influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science—elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.