arXiv subjects

Feng Fu

Publications and source records attributed to Feng Fu.

At least 19 recordsLinked to original sources

arXiv2026

Modeling microbiome dynamics on social networks: how between-host transmission shapes within-host evolution

A social network perspective on the human microbiome is crucial for understanding how the interplay between within-host microbial dynamics and between-host transmission influences microbial community stability, host metabolism, and population health and nutrition. Here, we provide a mathematical and numerical analysis of human-associated microbial communities in dyadic social ties and within a small network. By utilizing a generalized Lotka-Volterra model, we simulate within-host dynamics while incorporating inter-host microbial sharing driven by social interactions. Furthermore, the study accounts for individual variability in microbial behavior across different hosts. Our work identifies the conditions under which microbial sharing can have long-lasting effects, offering insights into how social networks shape the human microbiome.

physics.soc-ph↗

arXiv2026

Modeling Duelling Contagions of True and False Information in the Face of Inherent Individual biases

Advanced digital communication has revolutionized how people create and consume information, making information diffusion an important topic of research for domains from public health to national security. Real-world scenarios of information diffusion often involve competing narratives - true and false - spreading simultaneously. We propose a novel agent-based co-diffusion model, grounded in "complex-contagion" and "spiral of silence" theories, to capture how network dynamics exploit cognitive biases to shape such interactions. Our findings reveal that manipulative narratives dominate when early spreaders hold them. These network dynamics further exploit inherent cognitive biases to amplify information diffusion regardless of veracity. Further, while favourable previous experience strengthen collective optimism, unfavourable experiences attenuate optimism only modestly. However, we found that early seeding of agents with lower self-censorship not only constrains the spread of manipulation but can also lead to dominance of well-informed populance. This has implications for policies that aim to facilitate healthier discourse, strengthen social cohesion, and ensure equitable access to reliable information.

cs.SI↗

arXiv2026

Stereotyping by strategy standing diversifies cooperation patterns in indirect reciprocity

Indirect reciprocity explains how cooperation evolves through social reputations. People observe others, assign reputations, and condition their future actions on these assignments. This process is cognitively demanding, and stereotyping offers a simpler alternative by replacing individual-level reputation with group-level information. Theoretical models commonly implement stereotyping through exogenously given group labels. In real societies, however, group-level impressions may be associated with observable patterns of behavior. Here we propose a framework of stereotyping by strategy standing, in which mutants may condition their actions on the overall reputation level associated with a resident strategy rather than on the recipient's reputation. We show that this form of stereotyping can diversify stable cooperation in indirect reciprocity. As the strength of stereotyping increases, additional cooperative evolutionarily stable norm-strategy (ESS) pairs emerge in substantial numbers. In particular, we identify eight highly cooperative ESS pairs that become stable under very weak stereotyping. These pairs, which we call the counterparts of the leading eight, share the same social norms as the classical leading eight and differ only in how they prescribe behavior between bad individuals. They are unstable without stereotyping because they can be invaded by their corresponding leading strategies, but they become stable once stereotyping exceeds a critical threshold. Our results suggest that group-level impressions based on strategy standing can provide a coarse-grained informational route to stable cooperation and offer a more behaviorally grounded perspective on how stereotyping affects indirect reciprocity.

physics.soc-ph↗

arXiv2026

Evolution of cooperation in the multiplex

Across biological and social systems, cooperation often depends on phenotypic cues rather than random encounters. To account for real-world interactions unfolding across multiple, simultaneous dimensions, here we develop a general framework for the evolution of cooperation in multiplex networks governed by multi-phenotype homophily. We derive analytical conditions for natural selection to favor cooperation across phenotypic traits that are independent or exhibit epistasis and under different modes of mutation coupling. Despite the integration of fitness across layers, the conditions for cooperation resolve into layer-specific $\sigma$-rules, depending only on the local payoff structure, the effective number of phenotypes, and the mutation rates. We show that phenotypic diversity fosters cooperation by partitioning populations into assortative niches. Furthermore, in finite populations, intensifying the prisoner's dilemma shifts the dependence of cooperation on strategy mutation from monotonically decreasing, through U-shaped, to monotonically increasing. Our work provides a unified account of how multi-phenotype homophily underpins the evolutionary dynamics of cooperation in heterogeneous populations.

q-bio.PE↗

arXiv2026

Indirect reciprocity beyond pairwise interactions

Cooperation in groups underpins collective responses to challenges from climate governance to public goods provision, yet how moral evaluation sustains it remains poorly understood. Indirect reciprocity -- cooperating to build a good reputation -- is well characterized for pairwise interactions, but real collective action requires individuals to be judged against the reputational profile of an entire group. Here we develop a general framework for multiplayer indirect reciprocity and show that stable group cooperation obeys a simple organizing principle: `all good, help; one bad, halt'. This rule is both necessary and sufficient for cooperation to emerge, and it recovers the classical leading eight norms in the pairwise limit. We further show that group structure fundamentally changes reputation dynamics: unlike pairwise models, which are monostable, multiplayer systems exhibit bistability and hysteresis, with a critical tipping point separating cooperative and defective regimes. Assessment of the latent norms of large language models reveals that they shift toward punitive defection when provided with richer social information, yet fail to follow the full logic of `all good, help; one bad, halt'. Our results establish a unifying principle for reputation-based cooperation in groups and provide a benchmark for evaluating cooperative alignment in artificial intelligence.

physics.soc-ph↗

arXiv2026

When minor issues matter: symmetries, pluralism, and polarization in similarity-based opinion dynamics

Understanding how opinions evolve through social interactions is crucial for mitigating polarization. Existing opinion-dynamics models incorporate both attractive and repulsive interactions but typically assume that all issues are equally important. We develop and analyze a stochastic agent-based model where issues carry heterogeneous weights that influence both social affinity and the likelihood of opinion change. Surprisingly, introducing even a single issue with arbitrarily small weight can destabilize otherwise stable states, increasing convergence times by orders of magnitude. To explain these dynamics, we derive a mean-field approach and characterize the equilibrium symmetries governing consensus, polarization, and persistent pluralism. A complete classification of these symmetries for up to five issues reveals that polarization increases when importance is concentrated on a small number of issues. Conversely, distributing importance more broadly across issues promotes diversity of opinions and reduces polarization. Our symmetry-based framework highlights how issue salience and social tolerance jointly shape collective opinion evolution.

physics.soc-ph↗

arXiv2026

Strategies of cooperation and defection in five large language models

Large language models (LLMs) are increasingly deployed to support human decision-making. This use of LLMs has concerning implications, especially when their prescriptions affect the welfare of others. To gauge how LLMs make social decisions, we explore whether five leading models produce sensible strategies in the repeated prisoner's dilemma, which is the main metaphor of reciprocal cooperation. First, we measure the propensity of LLMs to cooperate in a neutral setting, without using language reminiscent of how this game is usually presented. We record to what extent LLMs implement Nash equilibria or other well-known strategy classes. Thereafter, we explore how LLMs adapt their strategies to changes in parameter values. We vary the game's continuation probability, the payoff values, and whether the total number of rounds is commonly known. We also study the effect of different framings. In each case, we test whether the adaptations of the LLMs are in line with basic intuition, theoretical predictions of evolutionary game theory, and experimental evidence from human participants. While all LLMs perform well in many of the tasks, none of them exhibit full consistency over all tasks. We also conduct tournaments between the inferred LLM strategies and study direct interaction between LLMs in games over ten rounds with a known or unknown last round. Our experiments shed light on how current LLMs instantiate reciprocal cooperation.

cs.CY↗

arXiv2026

Dynamics of Multi-Agent Actor-Critic Learning in Stochastic Games: from Multistability and Chaos to Stable Cooperation

Achieving robust coordination and cooperation is a central challenge in multi-agent reinforcement learning (MARL). Uncovering the mechanisms underlying such emergent behaviors calls for a dynamical understanding of learn processes. In this work, we investigate the dynamics of actor-critic agents in stochastic games, focusing on the impact of entropy regularization. By leveraging time-scale separation, we derive the system's evolution equations, which are then formally analyzed using dynamical systems theory. We find that in the constant-sum game of Matching Pennies, the system exhibits chaotic behavior. Entropy regularization mitigates this chaos and drives the dynamics toward convergence to fair cooperation. In contrast, in the general-sum game of the Prisoner's Dilemma, the system displays multistability. Interestingly, the three stable equilibria of the system correspond to the well-known ALLC (Always Cooperate), ALLD (Always Defect), and GRIM (Grim Trigger) strategies from evolutionary game theory (EGT). Entropy regularization strengthens system resilience by enlarging the basin of attraction of the cooperative equilibrium. Our findings reveal a close link between the mechanism of direct reciprocity in EGT and how cooperation emerges in MARL, offering insights for designing more robust and collaborative multi-agent systems.

physics.soc-ph↗

arXiv2025

Group Cooperation Diverges onto Durable Low versus High Paths: Public Goods Experiments in 134 Honduran Villages

We performed large, lab-in-the-field experiment (2,591 participants across 134 Honduran villages; ten rounds) and tracked how contribution behavior unfolds in fixed, anonymous groups of size five. Contribution separates early into two durable paths, one low and one high, with rare convergence thereafter. High-path players can be identified with strong accuracy early on. Groups that begin with an early majority of above-norm contributors (about 60%) are very likely finish high. The empirical finding of a bifurcation, consistent with the theory, shows that early, high contributions by socially central people steer groups onto, and help keep them on, a high-cooperation path.

stat.AP↗

arXiv2025

Evolutionary Kuramoto dynamics unravels origins of chimera states in neural populations

Neural synchronization is central to cognition However, incomplete synchronization often produces chimera states where coherent and incoherent dynamics coexist. While previous studies have explored such patterns using networks of coupled oscillators, it remains unclear why neurons commit to communication or how chimera states persist. Here, we investigate the coevolution of neuronal phases and communication strategies on directed, weighted networks, where interaction payoffs depend on phase alignment and may be asymmetric due to unilateral communication. We find that both connection weights and directionality influence the stability of communicative strategies -- and, consequently, full synchronization -- as well as the strategic nature of neuronal interactions. Applying our framework to the C. elegans connectome, we show that emergent payoff structures, such as the snowdrift game, underpin the formation of chimera states. Our computational results demonstrate a promising neurogame-theoretic perspective, leveraging evolutionary graph theory to shed light on mechanisms of neuronal coordination beyond classical synchronization models.

q-bio.NC↗

arXiv2025

Homophily and wealth inequality shape mitigation behavior in coupled social-climate models

Understanding the role of human behavior in shaping environmental outcomes is crucial for addressing global challenges such as climate change. Environmental systems are influenced not only by natural factors like temperature, but also by human decisions regarding mitigation efforts, which are often based on forecasts or predictions about future environmental conditions. Over time, different outcomes can emerge, including scenarios where the environment deteriorates despite efforts to mitigate, or where successful mitigation leads to environmental resilience. Additionally, fluctuations in the level of human participation in mitigation can occur, reflecting shifts in collective behavior. In this study, we consider a variety of human mitigation decisions, in addition to the feedback loop that is created by changes in human behavior because of environmental changes. While these outcomes are based on simplified models, they offer important insights into the dynamics of human decision-making and the factors that influence effective action in the context of environmental sustainability. This study aims to examine key social dynamics influencing society's response to a worsening climate. While others conclude that homophily prompts greater warming unconditionally, this model finds that homophily can prevent catastrophic effects given a poor initial environmental state. Assuming that poor countries have the resources to do so, a consensus in that class group to defect from the strategy of the rich group (who are generally incentivized to continue ``business as usual'') can frequently prevent the vegetation proportion from converging to 0.

physics.soc-ph↗

arXiv2025

Evolutionary dynamics of pairwise and group cooperation in heterogeneous social networks

Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties -- reinforcing tie strengths between peripheral nodes while weakening those between hubs -- robustly promote cooperation in both pairwise and group dilemmas. Importantly, this configuration enables heterogeneous networks to outperform homogeneous ones in fixation of cooperation, thereby adding to the conventional view that degree heterogeneity inhibits cooperative behavior under the local stochastic strategy update. We further test the generality of degree-inverse weighted social ties in promoting cooperation on 30,000 random networks and 13 empirical networks drawn from real-world systems. Finally, we unveil the underlying mechanism by examining the formation and evolution of cooperative ties under social ties with degree-inverse weights. Our systematic analyses provide new insights into how the network adjustment of tie strengths can effectively steer structured populations toward cooperative outcomes in biological and social systems.

physics.soc-ph↗

arXiv2025

The Niche Connectivity Paradox: Multichrome Contagions Overcome Vaccine Hesitancy more effectively than Monochromacy

The rise of vaccine hesitancy has caused a resurgence of vaccine-preventable diseases such as measles and pertussis, alongside widespread skepticism and refusals of COVID-19 vaccinations. While categorizing individuals as either supportive of or opposed to vaccines provides a convenient dichotomy of vaccine attitudes, vaccine hesitancy is far more complex and dynamic. It involves wavering individuals whose attitudes fluctuate -- those who may exhibit pro-vaccine attitudes at one time and anti-vaccine attitudes at another. Here, we identify and analyze multichrome contagions as potential targets for intervention by leveraging a dataset of known pro-vax and anti-vax Twitter users ($n =135$ million) and a large COVID-19 Twitter dataset ($n = 3.5$ billion; including close analysis of $1,563,472$ unique individuals). We reconstruct an evolving multiplex sentiment landscape using top co-spreading issues, characterizing them as monochrome and multichrome contagions, based on their conceptual overlap with vaccination. We demonstrate switchers as deliberative: they are more moderate, engage with a wider range of topics, and occupy more central positions in their networks. Further examination of their information consumption shows that their discourse often engages with progressive issues such as climate change, which can serve as avenues for multichrome contagion interventions to promote pro-vaccine attitudes. Using data-driven intervention simulations, we demonstrate a paradox of niche connectivity, where multichrome contagions with fragmented, non-overlapping communities generate the highest levels of diffusion for pro-vaccine attitudes. Our work offers insights into harnessing synergistic hitchhiking effect of multichrome contagions to drive desired attitude and behavior changes in network-based interventions, particularly for overcoming vaccine hesitancy.

cs.SI↗

arXiv2025

Nonlinear contagion dynamics on dynamical networks: exact solutions ranging from consensus times to evolutionary trajectories

Understanding nonlinear social contagion dynamics on dynamical networks, such as opinion formation, is crucial for gaining new insights into consensus and polarization. Similar to threshold-dependent complex contagions, the nonlinearity in adoption rates poses challenges for mean-field approximations. To address this theoretical gap, we focus on nonlinear binary-opinion dynamics on dynamical networks and analytically derive local configurations, specifically the distribution of opinions within any given focal individual's neighborhood. This exact local configuration of opinions, combined with network degree distributions, allows us to obtain exact solutions for consensus times and evolutionary trajectories. Our counterintuitive results reveal that neither biased assimilation (i.e., nonlinear adoption rates) nor preferences in local network rewiring -- such as in-group bias (preferring like-minded individuals) and the Matthew effect (preferring social hubs) -- can significantly slow down consensus. Among these three social factors, we find that biased assimilation is the most influential in accelerating consensus. Furthermore, our analytical method efficiently and precisely predicts the evolutionary trajectories of adoption curves arising from nonlinear contagion dynamics. Our work paves the way for enabling analytical predictions for general nonlinear contagion dynamics beyond opinion formation.

physics.soc-ph↗

arXiv2025

Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence

Vaccine hesitancy and misinformation are significant barriers to achieving widespread vaccination coverage. Smaller public health departments may lack the expertise or resources to craft effective vaccine messaging. This paper explores the potential of ChatGPT-augmented messaging to promote confidence in vaccination uptake. We conducted a survey in which participants chose between pairs of vaccination messages and assessed which was more persuasive and to what extent. In each pair, one message was the original, and the other was augmented by ChatGPT. At the end of the survey, participants were informed that half of the messages had been generated by ChatGPT. They were then asked to provide both quantitative and qualitative responses regarding how knowledge of a message's ChatGPT origin affected their impressions. Overall, ChatGPT-augmented messages were rated slightly higher than the original messages. These messages generally scored better when they were longer. Respondents did not express major concerns about ChatGPT-generated content, nor was there a significant relationship between participants' views on ChatGPT and their message ratings. Notably, there was a correlation between whether a message appeared first or second in a pair and its score. These results point to the potential of ChatGPT to enhance vaccine messaging, suggesting a promising direction for future research on human-AI collaboration in public health communication.

cs.CY↗

arXiv2025

Reinforcement Learning Dynamics of Network Vaccination and Hysteresis: A Double-Edged Sword for Addressing Vaccine Hesitancy

Mass vaccination remains a long-lasting challenge for disease control and prevention with upticks in vaccine hesitancy worldwide. Here, we introduce an experience-based learning (Q-learning) dynamics model of vaccination behavior in social networks, where agents choose whether or not to vaccinate given environmental feedbacks from their local neighborhood. We focus on how bounded rationality of individuals impacts decision-making of irrational agents in networks. Additionally, we observe hysteresis behavior and bistability with respect to vaccination cost and the Q-learning hyperparameters such as discount rate. Our results offer insight into the complexities of Q-learning and particularly how foresightedness of individuals will help mitigate - or conversely deteriorate, therefore acting as a double-edged sword - collective action problems in important contexts like vaccination. We also find a diversification of uptake choices, with individuals evolving into complete opt-in vs. complete opt-out. Our results have real-world implications for targeting the persistence of vaccine hesitancy using an interdisciplinary computational social science approach integrating social networks, game theory, and learning dynamics.

physics.soc-ph↗

arXiv2025

Social Imitation Dynamics of Vaccination Driven by Vaccine Effectiveness and Beliefs

Declines in vaccination coverage for vaccine-preventable diseases, such as measles and chickenpox, have enabled their surprising comebacks and pose significant public health challenges in the wake of growing vaccine hesitancy. Vaccine opt-outs and refusals are often fueled by beliefs concerning perceptions of vaccine effectiveness and exaggerated risks. Here, we quantify the impact of competing beliefs -- vaccine-averse versus vaccine-neutral -- on social imitation dynamics of vaccination, alongside the epidemiological dynamics of disease transmission. These beliefs may be pre-existing and fixed, or coevolving attitudes. This interplay among beliefs, behaviors, and disease dynamics demonstrates that individuals are not perfectly rational; rather, they base their vaccine uptake decisions on beliefs, personal experiences, and social influences. We find that the presence of a small proportion of fixed vaccine-averse beliefs can significantly exacerbate the vaccination dilemma, making the tipping point in the hysteresis loop more sensitive to changes in individuals' perceived costs of vaccination and vaccine effectiveness. However, in scenarios where competing beliefs spread concurrently with vaccination behavior, their double-edged impact can lead to self-correction and alignment between vaccine beliefs and behaviors. The results show that coevolution of vaccine beliefs and behaviors makes populations more sensitive to abrupt changes in perceptions of vaccine cost and effectiveness compared to scenarios without beliefs. Our work provides valuable insights into harnessing the social contagion of even vaccine-neutral attitudes to overcome vaccine hesitancy.

physics.soc-ph↗

arXiv2024

Evolutionary Multi-agent Reinforcement Learning in Group Social Dilemmas

Reinforcement learning (RL) is a powerful machine learning technique that has been successfully applied to a wide variety of problems. However, it can be unpredictable and produce suboptimal results in complicated learning environments. This is especially true when multiple agents learn simultaneously, which creates a complex system that is often analytically intractable. Our work considers the fundamental framework of Q-learning in Public Goods Games, where RL individuals must work together to achieve a common goal. This setting allows us to study the tragedy of the commons and free rider effects in AI cooperation, an emerging field with potential to resolve challenging obstacles to the wider application of artificial intelligence. While this social dilemma has been mainly investigated through traditional and evolutionary game theory, our approach bridges the gap between these two by studying agents with an intermediate level of intelligence. Specifically, we consider the influence of learning parameters on cooperation levels in simulations and a limiting system of differential equations, as well as the effect of evolutionary pressures on exploration rate in both of these models. We find selection for higher and lower levels of exploration, as well as attracting values, and a condition that separates these in a restricted class of games. Our work enhances the theoretical understanding of evolutionary Q-learning, and extends our knowledge of the evolution of machine behavior in social dilemmas.

cs.MA↗