Experimental evidence for tipping points in social convention

Modeling and testing strategic interdependence and tipping in public policy implementation

We develop a game-theoretic model of strategic interdependence and tipping in public policy choices and show that the model can be estimated by probit and logit estimators. We test its validity and applicability by using daily data on state-level COVID-19 responses in the United States. Social distancing via shelter-in-place (SIP) strategies and wearing masks emerged as the most effective nonpharmaceutical ways of combatting COVID-19. In the United States, choices about these policies are made by individual states. We develop a game-theoretic model of such choices and test it econometrically, confirming strong interdependence in the implementation of these policies. If enough states engage in social distancing or mask wearing, others will be tipped to follow suit. Policy choices are influenced mainly by the choices of other states, especially those of similar political orientation and to a lesser degree by the number of new COVID-19 cases. The choice of mask-wearing policies is more sensitive to peer choices than the choice of SIP policies, and Republican states are much less likely than Democratic to introduce mask-wearing policies. The choices of policies are influenced more by political than public health considerations. These findings emphasize strategic interdependence in policy choice and offer an analytical framework for these complex dynamics.

From trendsetters to second movers: Commitments as catalysts for social change

This paper examines the dual role of commitment in accelerating the mitigation of maladaptive norms. By drawing on the literature on social norms and commitment, I analyze how commitments (i) explain the deviant behavior of trendsetters and (ii) expedite social change by prompting counterpreferential choices among second movers. Specifically, the paper explores two key aspects of this dynamic: (1) the importance of moral norms, particularly in the relationship between trendsetters' commitments and their unconditional preferences, and (2) the unique role of second movers, whose commitment to trendsetters can lead them to make choices that contradict their conditional preferences in favor of trendsetters' behavior. This suggests that second movers, despite their tendency to conform to established social norms, may act against their conditional preferences due to their commitment to trendsetters. Understanding the role of commitments in shaping the behaviors of trendsetters and second movers is essential to overcoming psychological barriers that impede social change. Nonetheless, further exploration is needed to determine which maladaptive norms are most responsive to commitments and under what circumstances counterpreferential choices are likely to prevail.

A new sociology of humans and machines

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

Building capacity and capability for quality improvement: insights from a nascent regional health system

OBJECTIVES

Quality improvement (QI) is critical in facilitating advancements in patient outcomes, system efficiency and professional growth. This paper aimed to elucidate the underlying rationale and framework guiding JurongHealth Campus (JHC), a nascent Regional Health System, in developing its QI capacity and capability at all levels of the organisation.

METHODS

An exhaustive analysis of high-performance management systems and effective improvement frameworks was conducted, and the principles were customised to suit the local context.A three-phased approach was applied: (1) developing the JHC QI framework; (2) building capacity through a dosing approach and (3) building capability through QI projects and initiatives using the model for improvement (MFI). Three components of the RE-AIM implementation strategy were assessed: (1) Reach-overall percentage of staff trained; (2) Effectiveness-outcomes from organisation-wide improvement projects and (3) Adoption-number of QI projects collated and presented.

RESULTS

The percentage of staff trained in QI increased from 11.3% to 22.0% between January 2020 and March 2024, with over 350 projects documented in the central repository. The effectiveness of the MFI was demonstrated by improving inpatient discharges before 12pm performance from 21.52% to 25.84% and reducing the 30-day inpatient readmission rate from 13.92% to 12.96%.

CONCLUSION

Four critical factors for an effective QI framework were identified: (1) establishing a common language for improvement; (2) defining distinct roles and skills for improvement at different levels of the organisation; (3) adopting a dosing approach to QI training according to the defined roles and skills and (4) building a critical mass of committed staff trained in QI practice. The pragmatic approach to developing QI capability is both scalable and applicable to emerging healthcare institutions.

A Scoping Review of Culturally Adapted Mindfulness-Based Interventions for Communities of Color

Introduction: Mindfulness-based interventions (MBIs) are effective in improving mental health outcomes, including depression, anxiety, and post-traumatic stress disorder (PTSD). However, research on how MBIs have been tailored for racial and ethnic minoritized communities is limited. To address this gap, this scoping review utilizes the Ecological Validity Framework to systematically explore cultural adaptations in MBIs for communities of color. Concurrently, this review examines the effectiveness of culturally adapted MBIs. Methods: Following PRISMA guidelines, the authors conducted a search on MEDLINE, PsycINFO, and Embase databases from 2010 to 2023. Inclusion criteria required studies to be published in English, accessible in full-text, and peer-reviewed, focusing primarily on communities of color or diverse non-White populations (comprising 75% or more of the sample). Exclusion criteria were studies primarily centered on behavioral interventions other than MBIs, studies lacking primary outcomes, and studies not explicitly addressing cultural adaptations. Results: Search results identified 371 publications, 13 of which met the inclusion criteria. The most frequently reported cultural adaptations were surface-level adaptations, which can enhance the relevance of MBIs by modifying the language, content, format, or intervention delivery. MBIs with surface-level adaptations reported significant improvements in mental health outcomes, including depression, anxiety, and stress levels. Conclusion: Findings from this review indicate that culturally adapted MBIs for communities of color could potentially make them more relevant and acceptable. Surface-level and deep structure adaptations are both necessary to ensure MBIs are responsive, relevant, and sustainable across diverse contexts and populations.

Effect of individual activity level heterogeneity on disease spreading in higher-order networks

The active state of individuals has a significant impact on disease spread dynamics. In addition, pairwise interactions and higher-order interactions coexist in complex systems, and the pairwise networks proved insufficient for capturing the essence of complex systems. Here, we propose a higher-order network model to study the effect of individual activity level heterogeneity on disease-spreading dynamics. Activity level heterogeneity radically alters the dynamics of disease spread in higher-order networks. First, the evolution equations for infected individuals are derived using the mean field method. Second, numerical simulations of artificial networks reveal that higher-order interactions give rise to a discontinuous phase transition zone where the coexistence of health and disease occurs. Furthermore, the system becomes more unstable as individual activity levels rise, leading to a higher likelihood of disease outbreaks. Finally, we simulate the proposed model on two real higher-order networks, and the results are consistent with the artificial networks and validate the inferences from theoretical analysis. Our results explain the underlying reasons why groups with higher activity levels are more likely to initiate social changes. Simultaneously, the reduction in group activity, characterized by measures such as "isolation," emerges as a potent strategy for disease control.

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.

A survey of Australian public opinion on using comorbidity to triage intensive care patients in a pandemic

Objectives This study aimed to determine which method to triage intensive care patients using chronic comorbidity in a pandemic was perceived to be the fairest by the general public. Secondary objectives were to determine whether the public perceived it fair to provide preferential intensive care triage to vulnerable or disadvantaged people, and frontline healthcare workers. Methods A postal survey of 2000 registered voters randomly selected from the Australian Electoral Commission electoral roll was performed. The main outcome measures were respondents' fairness rating of four hypothetical intensive care triage methods that assess comorbidity (chronic medical conditions, long-term survival, function and frailty); and respondents' fairness rating of providing preferential triage to vulnerable or disadvantaged people, and frontline healthcare workers. Results The proportion of respondents who considered it fair to triage based on chronic medical conditions, long-term survival, function and frailty, was 52.1, 56.1, 65.0 and 62.4%, respectively. The proportion of respondents who considered it unfair to triage based on these four comorbidities was 31.9, 30.9, 23.8 and 23.2%, respectively. More respondents considered it unfair to preferentially triage vulnerable or disadvantaged people, than fair (41.8% versus 21.2%). More respondents considered it fair to preferentially triage frontline healthcare workers, than unfair (44.2% versus 30.0%). Conclusion Respondents in this survey perceived all four hypothetical methods to triage intensive care patients based on comorbidity in a pandemic disaster to be fair. However, the sizable minority who consider this to be unfair indicates that these triage methods could encounter significant opposition if they were to be enacted in health policy.

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.

Crop booms as regime shifts

A crop boom is a sudden, nonlinear and intense expansion of a new crop. Despite their large impacts, boom-bust dynamics are not well understood; booms are largely unpredictable and difficult to steer once they unfold. Based on the striking resemblances between land regime shifts and crop booms, we apply complex systems theory, highlighting the potential for regime shifts, to provide new insights about crop boom dynamics. We analyse qualitative and quantitative data of rubber and banana plantation expansion in two forest frontier regions of northern Laos. We show that preconditions, including previous booms, explain the occurrence (why) of booms, and triggers like policy and market changes explain their timing (when). Yet, the most important features of booms, their intensity and nonlinearity (how), strongly depended on internal self-reinforcing feedbacks. We identify built-in feedbacks (neighbourhood effects and imitation) and emergent feedbacks (land rush) and show that they were social in nature, multi-scale from plot to region and subject to thresholds. We suggest that these are regular features of booms and propose a definition and causal-mechanistic explanation of crop booms, examining the overlap between booms and regime shifts and the role of frontiers. We then identify opportunities for management interventions before, during and after booms.

Tipping points of evolving epidemiological networks: Machine learning-assisted, data-driven effective modeling

We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram. We observe a subcritical Hopf bifurcation in the evolving network's effective SIS dynamics that causes the tipping point behavior; this takes the form of large amplitude collective oscillations that spontaneously-yet rarely-arise from the neighborhood of a (noisy) stationary state. We study the statistics of these rare events both through repeated brute force simulations and by using established mathematical/computational tools exploiting the right-hand side of the identified SDE. We demonstrate that such a collective SDE can also be identified (and the rare event computations also performed) in terms of data-driven coarse observables, obtained here via manifold learning techniques, in particular, Diffusion Maps. The workflow of our study is straightforwardly applicable to other complex dynamic problems exhibiting tipping point dynamics.

From a false sense of safety to resilience under uncertainty

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

A mechanistic model of gossip, reputations, and cooperation

Social reputations facilitate cooperation: those who help others gain a good reputation, making them more likely to receive help themselves. But when people hold private views of one another, this cycle of indirect reciprocity breaks down, as disagreements lead to the perception of unjustified behavior that ultimately undermines cooperation. Theoretical studies often assume population-wide agreement about reputations, invoking rapid gossip as an endogenous mechanism for reaching consensus. However, the theory of indirect reciprocity lacks a mechanistic description of how gossip actually generates consensus. Here, we develop a mechanistic model of gossip-based indirect reciprocity that incorporates two alternative forms of gossip: exchanging information with randomly selected peers or consulting a single gossip source. We show that these two forms of gossip are mathematically equivalent under an appropriate transformation of parameters. We derive an analytical expression for the minimum amount of gossip required to reach sufficient consensus and stabilize cooperation. We analyze how the amount of gossip necessary for cooperation depends on the benefits and costs of cooperation, the assessment rule (social norm), and errors in reputation assessment, strategy execution, and gossip transmission. Finally, we show that biased gossip can either facilitate or hinder cooperation, depending on the direction and magnitude of the bias. Our results contribute to the growing literature on cooperation facilitated by communication, and they highlight the need to study strategic interactions coupled with the spread of social information.

Induction of social contagion for diverse outcomes in structured experiments in isolated villages

Certain people occupy topological positions within social networks that enhance their effectiveness at inducing spillovers. We mapped face-to-face networks among 24,702 people in 176 isolated villages in Honduras and randomly assigned villages to targeting methods, varying the fraction of households receiving a 22-month health education package and the method by which households were chosen (randomly versus using the friendship-nomination algorithm). We assessed 117 diverse knowledge, attitude, and practice outcomes. Friendship-nomination targeting reduced the number of households needed to attain specified levels of village-wide uptake. Knowledge spread more readily than behavior, and spillovers extended to two degrees of separation. Outcomes that were intrinsically easier to adopt also manifested greater spillovers. Network targeting using friendship nomination effectively promotes population-wide improvements in welfare through social contagion.

Explosive Cooperation in Social Dilemmas on Higher-Order Networks

Understanding how cooperative behaviors can emerge from competitive interactions is an open problem in biology and social sciences. While interactions are usually modeled as pairwise networks, the units of many real-world systems can also interact in groups of three or more. Here, we introduce a general framework to extend pairwise games to higher-order networks. By studying social dilemmas on hypergraphs with a tunable structure, we find an explosive transition to cooperation triggered by a critical number of higher-order games. The associated bistable regime implies that an initial critical mass of cooperators is also required for the emergence of prosocial behavior. Our results show that higher-order interactions provide a novel explanation for the survival of cooperation.

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'.

Modelling social norms: an integration of the norm-utility approach with beliefs dynamics

We review theoretical approaches for modelling the origin, persistence and change of social norms. The most comprehensive models describe the coevolution of behaviours, personal, descriptive and injunctive norms while considering influences of various authorities and accounting for cognitive processes and between-individual differences. Models show that social norms can improve individual and group well-being. Under some conditions though, deleterious norms can persist in the population through conformity, preference falsification and pluralistic ignorance. Polarization in behaviour and beliefs can be maintained, even when societal advantages of particular behaviours or belief systems over alternatives are clear. Attempts to change social norms can backfire through cognitive processes including cognitive dissonance and psychological reactance. Under some conditions social norms can change rapidly via tipping point dynamics. Norms can be highly susceptible to manipulation, and network structure influences their propagation. Future models should incorporate network structure more thoroughly, explicitly study online norms, consider cultural variations and be applied to real-world processes. This article is part of the theme issue 'Social norm change: drivers and consequences'.

When norm change hurts

Applied cultural evolution includes any effort to mobilize social learning and cultural evolution to promote behaviour change. Social tipping is one version of this idea based on conformity and coordination. Conformity and coordination can reinforce a harmful social norm, but they can also accelerate change from a harmful norm to a beneficial alternative. Perhaps unfortunately, the link between the size of an intervention and social tipping is complex in heterogeneous populations. A small intervention targeted at one segment of society can induce tipping better than a large intervention targeted at a different segment. We develop and examine two models showing that the link between social tipping and social welfare is also complex in heterogeneous populations. An intervention strategy that creates persistent miscoordination, exactly the opposite of tipping, can lead to higher social welfare than another strategy that leads to tipping. We show that the potential benefits of miscoordination often hinge specifically on the preferences of people most resistant to behaviour change. Altogether, ordinary forms of heterogeneity complicate applied cultural evolution considerably. Heterogeneity weakens both the link between the size of a social planner's intervention and behaviour change and the link between behaviour change and the well-being of society. This article is part of the theme issue 'Social norm change: drivers and consequences'.

Prosocial punishment bots breed social punishment in human players

Prosocial punishment, an important factor to stabilize cooperation in social dilemma games, often faces challenges like second-order free-riders-who cooperate but avoid punishing to save costs-and antisocial punishers, who defect and retaliate against cooperators. Addressing these challenges, our study introduces prosocial punishment bots that consistently cooperate and punish free-riders. Our findings reveal that these bots significantly promote the emergence of prosocial punishment among normal players due to their 'sticky effect'-an unwavering commitment to cooperation and punishment that magnetically attracts their opponents to emulate this strategy. Additionally, we observe that the prevalence of prosocial punishment is greatly enhanced when normal players exhibit a tendency to follow a 'copying the majority' strategy, or when bots are strategically placed in high-degree nodes within scale-free networks. Conversely, bots designed for defection or antisocial punishment diminish overall cooperation levels. This stark contrast underscores the critical role of strategic bot design in enhancing cooperative behaviours in human/AI interactions. Our findings open new avenues in evolutionary game theory, demonstrating the potential of human-machine collaboration in solving the conundrum of punishment.

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.

Network structure shapes the impact of diversity in collective learning

It is widely believed that diversity arising from different skills enhances the performance of teams, and in particular, their ability to learn and innovate. However, diversity has also been associated with negative effects on the communication and coordination within collectives. Yet, despite the importance of diversity as a concept, we still lack a mechanistic understanding of how its impact is shaped by the underlying social network. To fill this gap, we model skill diversity within a simple model of collective learning and show that its effect on collective performance differs depending on the complexity of the task and the network density. In particular, we find that diversity consistently impairs performance in simple tasks. In contrast, in complex tasks, link density modifies the effect of diversity: while homogeneous populations outperform diverse ones in sparse networks, the opposite is true in dense networks, where diversity boosts collective performance. Our findings also provide insight on how to forge teams in an increasingly interconnected world: the more we are connected, the more we can benefit from diversity to solve complex problems.

Norm Dynamics: Interdisciplinary Perspectives on Social Norm Emergence, Persistence, and Change

Social norms are the glue that holds society together, yet our knowledge of them remains heavily intellectually siloed. This article provides an interdisciplinary review of the emerging field of norm dynamics by integrating research across the social sciences through a cultural-evolutionary lens. After reviewing key distinctions in theory and method, we discuss research on norm psychology-the neural and cognitive underpinnings of social norm learning and acquisition. We then overview how norms emerge and spread through intergenerational transmission, social networks, and group-level ecological and historical factors. Next, we discuss multilevel factors that lead norms to persist, change, or erode over time. We also consider cultural mismatches that can arise when a changing environment leads once-beneficial norms to become maladaptive. Finally, we discuss potential future research directions and the implications of norm dynamics for theory and policy.

Modeling opinion misperception and the emergence of silence in online social system

In the last decades an increasing deal of research has investigated the phenomenon of opinion misperception in human communities and, more recently, in social media. Opinion misperception is the wrong evaluation by community's members of the real distribution of opinions or beliefs about a given topic. In this work we explore the mechanisms giving rise to opinion misperception in social media groups, which are larger than physical ones and have peculiar topological features. By means of numerical simulations, we suggest that the structure of connections of such communities plays indeed a role in distorting the perception of the agents about others' beliefs, but it is essentially an indirect effect. Moreover, we show that the main ingredient that generates misperception is a spiral of silence induced by few, well connected and charismatic agents, which rapidly drives the majority of individuals to stay silent without disclosing their true belief, leading minoritarian opinions to appear more widespread throughout the community.

When and Why Do People Accept Public-Policy Interventions? An Integrative Public-Policy-Acceptance Framework

The successful introduction of public policies to prompt behavior change hinges on the degree to which citizens endorse the proposed policies. Although there is a large body of research on psychological determinants of public policy acceptance, these determinants have not yet been synthesized into an integrative framework that proposes hypotheses about their interplay. In this article, we develop a review-based, integrative public-policy-acceptance framework that introduces the desire for governmental support as a motivational foundation in public-policy acceptance. The framework traces the route from problem awareness to policy acceptance and, ultimately, policy compliance. We propose this relationship to be mediated by a desire for governmental support. We integrate numerous key variables assumed to qualify the relationship between problem awareness and the desire for governmental support, such as control attributions, trust, and value fit, as well as the relationship between the desire for governmental support and policy acceptance, such as perceived policy effectiveness, intrusiveness, and fairness. We exemplify the use of the proposed framework by applying it to climate policies.

Integrating evolutionary theory and social-ecological systems research to address the sustainability challenges of the Anthropocene

The rapid, human-induced changes in the Earth system during the Anthropocene present humanity with critical sustainability challenges. Social-ecological systems (SES) research provides multiple approaches for understanding the complex interactions between humans, social systems, and environments and how we might direct them towards healthier and more resilient futures. However, general theories of SES change have yet to be fully developed. Formal evolutionary theory has been applied as a dynamic theory of change of complex phenomena in biology and the social sciences, but rarely in SES research. In this paper, we explore the connections between both fields, hoping to foster collaboration. After sketching out the distinct intellectual traditions of SES research and evolutionary theory, we map some of their terminological and theoretical connections. We then provide examples of how evolutionary theory might be incorporated into SES research through the use of systems mapping to identify evolutionary processes in SES, the application of concepts from evolutionary developmental biology to understand the connections between systems changes and evolutionary changes, and how evolutionary thinking may help design interventions for beneficial change. Integrating evolutionary theory and SES research can lead to a better understanding of SES changes and positive interventions for a more sustainable Anthropocene. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Drivers of social influence in the Twitter migration to Mastodon

The migration of Twitter users to Mastodon following Elon Musk's acquisition presents a unique opportunity to study collective behavior and gain insights into the drivers of coordinated behavior in online media. We analyzed the social network and the public conversations of about 75,000 migrated users and observed that the temporal trace of their migrations is compatible with a phenomenon of social influence, as described by a compartmental epidemic model of information diffusion. Drawing from prior research on behavioral change, we delved into the factors that account for variations of the effectiveness of the influence process across different Twitter communities. Communities in which the influence process unfolded more rapidly exhibit lower density of social connections, higher levels of signaled commitment to migrating, and more emphasis on shared identity and exchange of factual knowledge in the community discussion. These factors account collectively for 57% of the variance in the observed data. Our results highlight the joint importance of network structure, commitment, and psycho-linguistic aspects of social interactions in characterizing grassroots collective action, and contribute to deepen our understanding of the mechanisms that drive processes of behavior change of online groups.

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.

Robustness and Complexity of Directed and Weighted Metabolic Hypergraphs

Metabolic networks are probably among the most challenging and important biological networks. Their study provides insight into how biological pathways work and how robust a specific organism is against an environment or therapy. Here, we propose a directed hypergraph with edge-dependent vertex weight as a novel framework to represent metabolic networks. This hypergraph-based representation captures higher-order interactions among metabolites and reactions, as well as the directionalities of reactions and stoichiometric weights, preserving all essential information. Within this framework, we propose the communicability and the search information as metrics to quantify the robustness and complexity of directed hypergraphs. We explore the implications of network directionality on these measures and illustrate a practical example by applying them to a small-scale E. coli core model. Additionally, we compare the robustness and the complexity of 30 different models of metabolism, connecting structural and biological properties. Our findings show that antibiotic resistance is associated with high structural robustness, while the complexity can distinguish between eukaryotic and prokaryotic organisms.

Racial and Ethnic Inequities in Health: Examining the Contributions of the American Journal of Epidemiology to Advancing the Science

The perverseness of racial and ethnic inequities in the United States continues to implore the investigation of their causes. While there have been improvements in the health of the US population, these improvements have not been equally distributed. To commemorate the 100th anniversary of the American Journal of Epidemiology, in this commentary, we aim to highlight the Journal's contributions to: 1) the definition and use of race and ethnicity in research, and 2) understanding racial and ethnic inequities, both empirically and methodologically, over the past decade. We commend the Journal for its contributions and for spearheading many of the challenges related to measuring and interpreting racial and ethnic data for the past 20 years. We identify 3 additional areas in which the Journal could make further impact to address racial and ethnic inequities: 1) devote a section in every issue of the Journal to scientific papers that make substantive epidemiologic or methodological contributions to racial and ethnic inequities in health; 2) update the Journal's guidelines for authors to include justifying the use of race and ethnicity; and 3) diversify the field of epidemiology by bringing a new cadre of scholars from minoritized racial and ethnic groups who represent the most affected communities into the research process.

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.

Hyper-cores promote localization and efficient seeding in higher-order processes

Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major step to better describe complex systems. In the resulting hypergraph representation, tools to identify structures and central nodes are scarce. We consider the decomposition of a hypergraph in hyper-cores, subsets of nodes connected by at least a certain number of hyperedges of at least a certain size. We show that this provides a fingerprint for data described by hypergraphs and suggests a novel notion of centrality, the hypercoreness. We assess the role of hyper-cores and nodes with large hypercoreness in higher-order dynamical processes: such nodes have large spreading power and spreading processes are localized in central hyper-cores. Additionally, in the emergence of social conventions very few committed individuals with high hypercoreness can rapidly overturn a majority convention. Our work opens multiple research avenues, from comparing empirical data to model validation and study of temporally varying hypergraphs.

Algorithm-mediated social learning in online social networks

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

First-order transition to oscillation death in coupled oscillators with higher-order interactions

We investigate the dynamical evolution of Stuart-Landau oscillators globally coupled through conjugate or dissimilar variables on simplicial complexes. We report a first-order explosive phase transition from an oscillatory state to oscillation death, with higher-order (2-simplex triadic) interactions, as opposed to the second-order transition with only pairwise (1-simplex) interactions. Moreover, the system displays four distinct homogeneous steady states in the presence of triadic interactions, in contrast to the two homogeneous steady states observed with dyadic interactions. We calculate the backward transition point analytically, confirming the numerical results and providing the origin of the dynamical states in the transition region. The results are robust against the application of noise. The study will be useful in understanding complex systems, such as ecological and epidemiological, having higher-order interactions and coupling through conjugate variables.

Rise of post-pandemic resilience across the distrust ecosystem

Why does online distrust (e.g., of medical expertise) continue to grow despite numerous mitigation efforts? We analyzed changing discourse within a Facebook ecosystem of approximately 100 million users who were focused pre-pandemic on vaccine (dis)trust. Post-pandemic, their discourse interconnected multiple non-vaccine topics and geographic scales within and across communities. This interconnection confers a unique, system-level (i.e., at the scale of the full network) resistance to mitigations targeting isolated topics or geographic scales-an approach many schemes take due to constrained funding. For example, focusing on local health issues but not national elections. Backed by numerical simulations, we propose counterintuitive solutions for more effective, scalable mitigation: utilize "glocal" messaging by blending (1) strategic topic combinations (e.g., messaging about specific diseases with climate change) and (2) geographic scales (e.g., combining local and national focuses).

The ecological footprint of physicians: A survey of physicians in Canada, India, and USA

Combating climate change may be the greatest public health opportunity of the 21st century. While physicians play an important role in addressing climate change, given their affluence in society, they may be an important source of greenhouse gas emissions themselves. We sought to examine the size and nature of the ecological footprint of physicians and medical students. We conducted an online survey from December 2021-May 2022 examining resource consumption, changes in consumption patterns over time, and beliefs about climate change. Participants were medical students, residents, and staff physicians in Canada, India, or USA. Only 20 out of 162 valid respondents had a low ecological footprint (12%), defined as meat intake ≤2 times per week, living in an apartment or condominium, and using public transport, bicycle, motorcycle or walking to work. 14 of these 20 participants were from India. 91% of participants were open to reducing their own ecological footprint, though only 40% had made changes in that regard. 49% participants who discussed climate change at work and at home had decreased their ecological footprint, compared to 29% of participants who rarely engaged in such conversations (OR 2.39, 95% CI 1.24-4.63, P = 0.01). We conclude that physicians have a large ecological footprint, especially those from Canada and USA. A majority of physicians are interested in reducing their ecological footprint, and those who engage in conversations around climate change are more likely to have done so. Talking frequently about climate change, at work and at home, will likely increase climate change action amongst physicians.

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.

World scientists' warning: The behavioural crisis driving ecological overshoot

Previously, anthropogenic ecological overshoot has been identified as a fundamental cause of the myriad symptoms we see around the globe today from biodiversity loss and ocean acidification to the disturbing rise in novel entities and climate change. In the present paper, we have examined this more deeply, and explore the behavioural drivers of overshoot, providing evidence that overshoot is itself a symptom of a deeper, more subversive modern crisis of human behaviour. We work to name and frame this crisis as 'the Human Behavioural Crisis' and propose the crisis be recognised globally as a critical intervention point for tackling ecological overshoot. We demonstrate how current interventions are largely physical, resource intensive, slow-moving and focused on addressing the symptoms of ecological overshoot (such as climate change) rather than the distal cause (maladaptive behaviours). We argue that even in the best-case scenarios, symptom-level interventions are unlikely to avoid catastrophe or achieve more than ephemeral progress. We explore three drivers of the behavioural crisis in depth: economic growth; marketing; and pronatalism. These three drivers directly impact the three 'levers' of overshoot: consumption, waste and population. We demonstrate how the maladaptive behaviours of overshoot stemming from these three drivers have been catalysed and perpetuated by the intentional exploitation of previously adaptive human impulses. In the final sections of this paper, we propose an interdisciplinary emergency response to the behavioural crisis by, amongst other things, the shifting of social norms relating to reproduction, consumption and waste. We seek to highlight a critical disconnect that is an ongoing societal gulf in communication between those that know such as scientists working within limits to growth, and those members of the citizenry, largely influenced by social scientists and industry, that must act.

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.

Unlocking and accelerating transformations to the SDGs: a review of existing knowledge

As we cross the 2030 deadline to achieve the Sustainable Development Goals (SDGs), there is a growing sense of urgency around the need to accelerate the necessary transformations. These encompass a broad range of systems and require fundamental changes in system goals and design. In this paper, we undertake a narrative review of the literature relating to the acceleration of transformations and offer a framework for unlocking and accelerating transformations to the SDGs. While there is no blueprint for acceleration, there is an expanding knowledge base on important dynamics, impediments and enabling conditions across diverse literatures which can help to inform strategic interventions by actors. The emerging literature on positive tipping points and deep leverage points identifies opportunities to rewire systems design so that important system feedbacks create the conditions for acceleration. Transformation takes time and actors will need to build momentum to reorient systems around new goals, informed by knowledge of common policy, technology and behavioural feedbacks that govern system dynamics. Where resistance is strong, actors can seek to augment system design in ways that weaken balancing feedbacks that stabilise existing system configurations and strengthen reinforcing feedbacks that promote emerging system configurations oriented towards the SDGs. Well-designed and sequenced interventions can promote innovation and behaviour change and build and maintain political support. This can build critical enabling conditions and push systems towards large-scale tipping points, paving the way for decisive policy action that is crucial for triggering acceleration. We conclude by highlighting gaps and priorities for further research.

Link updating strategies influence consensus decisions as a function of the direction of communication

Consensus decision-making in social groups strongly depends on communication links that determine to whom individuals send, and from whom they receive, information. Here, we ask how consensus decisions are affected by strategic updating of links and how this effect varies with the direction of communication. We quantified the coevolution of link and opinion dynamics in a large population with binary opinions using mean-field numerical simulations of two voter-like models of opinion dynamics: an incoming model (IM) (where individuals choose who to receive opinions from) and an outgoing model (OM) (where individuals choose who to send opinions to). We show that individuals can bias group-level outcomes in their favour by breaking disagreeing links while receiving opinions (IM) and retaining disagreeing links while sending opinions (OM). Importantly, these biases can help the population avoid stalemates and achieve consensus. However, the role of disagreement avoidance is diluted in the presence of strong preferences-highly stubborn individuals can shape decisions to favour their preferences, giving rise to non-consensus outcomes. We conclude that collectively changing communication structures can bias consensus decisions, as a function of the strength of preferences and the direction of communication.

A 3D framework of implicit attitude change

According to early theories, implicit (automatic) social attitudes are difficult if not impossible to change. Although this view has recently been challenged by research relying on experimental, developmental, and cultural approaches, relevant work remains siloed across research communities. As such, the time is ripe to systematize and integrate disparate (and seemingly contradictory) findings and to identify gaps in existing knowledge. To this end, we introduce a 3D framework classifying research on implicit attitude change by levels of analysis (individual vs. collective), sources of change (experimental, ontogenetic, and cultural), and timescales (short term vs. long term). This 3D framework highlights where evidence for implicit attitude change is more versus less well established and pinpoints directions for future research, including at the intersection of fields.

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Two of the main factors shaping an individual's opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases' strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.

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.

Physiological tipping points in the relationship between foraging success and lifetime fitness of a long-lived mammal

Although anthropogenic change is often gradual, the impacts on animal populations may be precipitous if physiological processes create tipping points between energy gain, reproduction or survival. We use 25 years of behavioural, diet and demographic data from elephant seals to characterise their relationships with lifetime fitness. Survival and reproduction increased with mass gain during long foraging trips preceding the pupping seasons, and there was a threshold where individuals that gained an additional 4.8% of their body mass (26 kg, from 206 to 232 kg) increased lifetime reproductive success three-fold (from 1.8 to 4.9 pups). This was due to a two-fold increase in pupping probability (30% to 76%) and a 7% increase in reproductive lifespan (6.0 to 6.4 years). The sharp threshold between mass gain and reproduction may explain reproductive failure observed in many species and demonstrates how small, gradual reductions in prey from anthropogenic disturbance could have profound implications for animal populations.

Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men

BACKGROUND

Research suggests that structural racism and homophobia are associated with mental well-being. However, structural discrimination measures which are relevant to lived experiences and that evade self-report biases are needed. Social media and global-positioning systems (GPS) offer opportunity to measure place-based negative racial sentiment linked to relevant locations via precise geo-coding of activity spaces. This is vital for young sexual minority men (YSMM) of color who may experience both racial and sexual minority discrimination and subsequently poorer mental well-being.

METHODS

P18 Neighborhood Study (n = 147) data were used. Measures of place-based negative racial and sexual-orientation sentiment were created using geo-located social media as a proxy for racial climate via socially-meaningfully-defined places. Exposure to place-based negative sentiment was computed as an average of discrimination by places frequented using activity space measures per person. Outcomes were number of days of reported poor mental health in last 30 days. Zero-inflated Poisson regression analyses were used to assess influence of and type of relationship between place-based negative racial or sexual-orientation sentiment exposure and mental well-being, including the moderating effect of race/ethnicity.

RESULTS

We found evidence for a non-linear relationship between place-based negative racial sentiment and mental well-being among our racially and ethnically diverse sample of YSMM (p < .05), and significant differences in the relationship for different race/ethnicity groups (p < .05). The most pronounced differences were detected between Black and White non-Hispanic vs. Hispanic sexual minority men. At two standard deviations above the overall mean of negative racial sentiment exposure based on activity spaces, Black and White YSMM reported significantly more poor mental health days in comparison to Hispanic YSMM.

CONCLUSIONS

Effects of discrimination can vary by race/ethnicity and discrimination type. Experiencing place-based negative racial sentiment may have implications for mental well-being among YSMM regardless of race/ethnicity, which should be explored in future research including with larger samples sizes.

Rethinking academia in a time of climate crisis

Addressing the climate crisis requires radical and urgent action at all levels of society. Universities are ideally positioned to lead such action but are largely failing to do so. At the same time, many academic scientists find their work impeded by bureaucracy, excessive competitiveness, and a loss of academic freedom. Here, drawing on the framework of "Doughnut Economics," developed by Kate Raworth, we suggest seven new principles for rethinking the norms of scientific practice. Based on these, we propose a call to action, and encourage academics to take concrete steps towards the creation of a flourishing scientific enterprise that is fit for the challenges of the 21st century.

Cooperation without punishment

A fundamental question in social and biological sciences is whether self-governance is possible when individual and collective interests are in conflict. Free riding poses a major challenge to self-governance, and a prominent solution to this challenge has been altruistic punishment. However, this solution is ineffective when counter-punishments are possible and when social interactions are noisy. We set out to address these shortcomings, motivated by the fact that most people behave like conditional cooperators-individuals willing to cooperate if a critical number of others do so. In our evolutionary model, the population contains heterogeneous conditional cooperators whose decisions depend on past cooperation levels. The population plays a repeated public goods game in a moderately noisy environment where individuals can occasionally commit mistakes in their cooperative decisions and in their imitation of the role models' strategies. We show that, under moderate levels of noise, injecting a few altruists into the population triggers positive reciprocity among conditional cooperators, thereby providing a novel mechanism to establish stable cooperation. More broadly, our findings indicate that self-governance is possible while avoiding the detrimental effects of punishment, and suggest that society should focus on creating a critical amount of trust to harness the conditional nature of its members.