arXiv subjects

Fanyuan Meng

Publications and source records attributed to Fanyuan Meng.

13 recordsLinked to original sources

arXiv2026

Hypergraph Minority Game with Local Hyperedge Payoffs

We provide a theoretical derivation of the Hypergraph Minority Game with Local Hyperedge Payoffs (HMG-L), in which $N$ adaptive agents compete simultaneously in multiple overlapping groups modeled as hyperedges of a static hypergraph $\Hyper=(\Vset,\Eset)$. Each hyperedge constitutes an independent local minority game, and agents accumulate payoffs across all groups to which they belong. We derive the continuum-time limit of the score dynamics, from which we obtain a set of coupled nonlinear stochastic differential equations for the agents' strategy polarization variables. The deterministic drift is shown to derive from a global cost function that generalizes the standard Minority Game Hamiltonian to hypergraph-structured interactions. We perform a sparse-annealed replica analysis of the stationary state for the case of a $k$-uniform, $d$-regular random hypergraph, obtaining the saddle-point equations within the replica-symmetric ansatz, an explicit replicon stability criterion, and Bethe/cavity equations for sparse corrections. The leading sparse-regime transition occurs on a critical surface $\alphacrit(k,d)$, while the globally coupled MG value $\alphacrit\simeq0.3374$ is recovered only in the separate single-hyperedge limit. We derive expressions for the order parameters -- global volatility $\sigma^2$, predictability $\theta$, hyperedge frustration $F_e$, and frozen fraction $\phi$ -- and discuss their scaling behavior near criticality. The Fokker-Planck equation governing finite-$N$ fluctuations is presented, and the noise covariance matrix is computed from the hypergraph structure. Limiting cases ($k\to N$, $k\to2$, $d\to\infty$) are analyzed in detail, establishing connections to the standard MG, networked MG, and parallel MG models.

physics.soc-ph↗

arXiv2026

Exact log-odds representation and mean-field criticality of a growing social group model

We present an exact analytical reformulation of a growing social group model -- a Hamiltonian-free nonequilibrium process in which a group grows by noisy, consensus-driven admission. Cast as a gradient flow on logarithmic time, the fixed-point structure collapses to a single self-consistent equation: $\arctanh(\phi^*) = m \cdot \arctanh(\alpha\phi^*)$, where $\phi$ is the polarization, $\alpha=1-2\eta$ the evaluation reliability, and $m$ the number of evaluators. The equation has a direct log-odds interpretation: each verdict contributes log-likelihood ratio $2\arctanh(\alpha\phi)$; unanimity accumulates $m$ independent evidence pieces. The dynamics thus constitutes an exact mean-field theory of self-consistent inference, ordering when the collective gain $m\alpha$ overcomes the dilution of growth. We develop a systematic three-layer framework: core theory (Landau-like effective potential, comparison with the mean-field Ising model, and features without equilibrium counterpart), mathematical foundations (criticality from correlated verdicts, P\'{o}lya-urn martingale convergence, and an RG-like flow with group size as scale), and complementary perspectives on irreversibility and information geometry. A frozen-$N$ Freidlin--Wentzell quasipotential yields Kramers-type escape estimates for metastable states, while Monte Carlo simulations collapse onto a parameter-free deterministic master curve on logarithmic time. Systematic comparison with the mean-field Ising model reveals shared critical exponents but a nested arctanh structure unique to growth. These results provide a detailed analytical characterization of a minimal model of growth-driven collective behavior and map which elements of the equilibrium critical toolbox -- suitably reinterpreted -- survive without a Hamiltonian.

cond-mat.stat-mech↗

arXiv2025

Adaptive Punishment in Social Dilemmas

We introduce a coevolutionary framework in which punishment intensity dynamically adapts to the fraction of cooperators in the population. Unlike static models, adaptive punishment reshapes the effective payoff landscape, driving transitions among canonical games, including the Prisoner's Dilemma, Harmony, Stag Hunt, and Chicken games. Analytical results reveal rich dynamical behaviors such as coexistence, bistability, limit cycle and Hopf bifurcation. These findings highlight adaptive punishment as a robust mechanism for sustaining cooperation by the coevolutionary feedback and offer insights into institutional design, ecological interactions, and social governance.

physics.soc-ph↗

arXiv2024

Natural Language Processing with Commonsense Knowledge: A Survey

Commonsense knowledge is essential for advancing natural language processing (NLP) by enabling models to engage in human-like reasoning, which requires a deeper understanding of context and often involves making inferences based on implicit external knowledge. This paper explores the integration of commonsense knowledge into various NLP tasks. We begin by reviewing prominent commonsense knowledge bases and then discuss the benchmarks used to evaluate the commonsense reasoning capabilities of NLP models, particularly language models. Furthermore, we highlight key methodologies for incorporating commonsense knowledge and their applications across different NLP tasks. The paper also examines the challenges and emerging trends in enhancing NLP systems with commonsense reasoning. All literature referenced in this survey can be accessed via our GitHub repository: https://github.com/yuboxie/awesome-commonsense.

cs.CL↗

arXiv2024

Cascading failures with group support in interdependent hypergraphs

The functionality of an entity frequently necessitates the support of a group situated in another layer of the system. To unravel the profound impact of such group support on a system's resilience against cascading failures, we devise a framework comprising a double-layer interdependent hypergraph system, wherein nodes are capable of receiving support via hyperedges. Our central hypothesis posits that the failure may transcend to another layer when all support groups of each dependent node fail, thereby initiating a potentially iterative cascade across layers. Through rigorous analytical methods, we derive the critical threshold for the initial node survival probability that marks the second-order phase transition point. A salient discovery is that as the prevalence of dependent nodes escalates, the system dynamics shift from a second-order to a first-order phase transition. Notably, irrespective of the collapse pattern, systems characterized by scale-free hyperdegree distributions within both hypergraph layers consistently demonstrate superior robustness compared to those adhering to Poisson hyperdegree distributions. In summary, our research underscores the paramount significance of group support mechanisms and intricate network topologies in determining the resilience of interconnected systems against the propagation of cascading failures. By exploring the interplay between these factors, we have gained insights into how systems can be designed or optimized to mitigate the risk of widespread disruptions, ensuring their continued functionality and stability in the face of adverse events.

physics.soc-ph↗

arXiv2024

A simple model of global cascades on random hypergraphs

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $ϕ_m$. However, actual adoption of the information only occurs when the cumulative influence across all communication channels exceeds a second threshold, $ϕ_k$. We analytically derive the cascade condition for both the case of a single seed node using percolation methods and the case of any seed size employing mean-field approximation. Our findings underscore that when considering the fractional seed size, $r_0 \in (0,1]$, the connectivity pattern of the random hypergraph, characterized by the hyperdegree, $k$, and cardinality, $m$, distributions, exerts an asymmetric impact on the global cascade boundary. This asymmetry manifests in the observed differences in the boundaries of the global cascade within the $(ϕ_m, \langle m \rangle)$ and $(ϕ_k, \langle k \rangle)$ planes. However, as $r_0 \to 0$, this asymmetric effect gradually diminishes. Overall, by elucidating the mechanisms driving information cascades within a broader context of higher-order interactions, our research contributes to theoretical advancements in complex systems theory.

physics.soc-ph↗

arXiv2024

A simple model of decision-making in the application process

In decision-making, individuals often rely on intuition, which can occasionally yield suboptimal outcomes. This study examines the impact of intuitive decision-making on individuals who are confronted with limited position information in the job application process. We propose a measure, the mismatch index, that gauges allocation efficiency by comparing the final application rate to the preset admission rate. By simulation and analytical results, we counter-intuitively find that under the intuitive strategy, acquiring more information does not always lead to more efficient allocation. Additionally, a shift from despondency to a bandwagon effect occurs when the initial application rate surpasses the admission rate, which can be observed in our field experiments. Meanwhile, experimental data also unveil variations in individuals' reliance on intuition, indicating the presence of inherent adventurous and conservative inclinations. To account for these effects, we introduce an enhancement factor into our model. The improved results align well with these real data, showing that compared to mediate competitive scenarios, individuals exhibit a stronger conservative tendency in fierce or less competitive scenarios. These findings offer significant insights into resource allocation, especially in the competitive job market context.

physics.soc-ph↗

arXiv2024

A Theoretical Model of False Information Control

When considering a specific event, news that accurately reflects the ground truth is deemed as real information, while news that deviates from the ground truth is classified as false information. False information often spreads fast due to its novel and attention-grabbing content, which poses a threat to our society. By extending the Susceptible-Infected (SI) model, our research offers analytical decision boundaries that enable effective interventions to get desirable results, even when intermediate functions cannot be analytically solved. These analytical results may provide valuable insights for policymakers in false information control. When assessing intervention costs using the model, the results indicate that the sooner we intervene, the lower the overall intervention cost tends to be.

physics.soc-ph↗

arXiv2023

Group cohesion under asymmetric voting behaviors

Cohesion plays a crucial role in achieving collective goals, promoting cooperation and trust, and improving efficiency within social groups. To gain deeper insights into the dynamics of group cohesion, we have extended our previous model of noisy group formation by incorporating asymmetric voting behaviors. Through a combination of theoretical analysis and numerical simulations, we have explored the impact of asymmetric voting noise, the attention decay rate, voter selection methods, and group sizes on group cohesion. For a single voter, we discovered that as the group size approaches infinity, group cohesion converges to $1/(R+1)$, where $R$ represents the ratio of asymmetric voting noise. Remarkably, even in scenarios with extreme voting asymmetry ($R \to \infty$), a significant level of group cohesion can be maintained. Furthermore, when the positive or negative voter's voting noise surpasses or falls below the phase transition point of $R_c=1$, a higher rate of attention decay can lead to increased group cohesion. In the case of multiple voters, a similar phenomenon arises when the attention decay rate reaches a critical point. These insights provide practical implications for fostering effective collaboration and teamwork within growing groups striving to achieve shared objectives.

physics.soc-ph↗

arXiv2023

Cascading failure with memory effect in random networks

In many cases of attacks or failures, memory effects play a significant role. Therefore, we present a model that not only considers the dependencies between nodes but also incorporates the memory effects of attacks. Our research demonstrates that the survival probability of a random node reached by a random edge surpasses the inverse of the average degree ($1/{\langle k \rangle}$), and a giant component emerges regardless of the strength of dependencies. Moreover, if the dependency strength exceeds $1/{\langle k \rangle}$, the network experiences an abrupt collapse when an infinitesimally small fraction of nodes is removed, irrespective of the memory effect. Our proposed model provides insights into the interplay between dependencies between nodes, memory effects, and the network structures under attacks or failures. By considering these factors, we can better assess the vulnerability of complex systems and develop strategies to mitigate cascading failures.

physics.soc-ph↗

arXiv2023

Modeling opinion polarization under perception bias

Social networks have provided a platform for the effective exchange of ideas or opinions but also served as a hotbed of polarization. While much research attempts to explore different causes of opinion polarization, the effect of perception bias caused by the network structure itself is largely understudied. To this end, we propose a threshold model that simulates the evolution of opinions by taking into account the perception bias, which is the gap between global information and locally available information from the neighborhood within networks. Our findings suggest that polarization occurs when the collective stubbornness of the population exceeds a critical value which is largely affected by the perception bias. In addition, as the level of stubbornness grows, the occurrence of first-order and second-order phase transitions between consensus and polarization becomes more prevalent, and the types of these phase transitions rely on the initial proportion of active opinions. Notably, for regular network structures, a step-wise pattern emerges that corresponds to various levels of polarization and is strongly associated with the formation of echo chambers. Our research presents a valuable framework for investigating the connection between perception bias and opinion polarization and provides valuable insights for mitigating polarization in the context of biased information.

physics.soc-ph↗

arXiv2022

Phase transitions in growing groups: How cohesion can persist

The cohesion of a social group is the group's tendency to remain united. It has important implications for the stability and survival of social organizations, such as political parties, research teams, or online groups. Empirical studies suggest that cohesion is affected by both the admission process of new members and the group size. Yet, a theoretical understanding of their interplay is still lacking. To this end, we propose a model where a group grows by a noisy admission process of new members who can be of two different types. Cohesion is defined in this framework as the fraction of members of the same type and the noise in the admission process represents the level of randomness in the evaluation of new candidates. The model can reproduce the empirically reported decrease of cohesion with the group size. When the admission of new candidates involves the decision of only one group member, the group growth causes a loss of cohesion even for infinitesimal levels of noise. However, when admissions require a consensus of several group members, there is a critical noise level below which the growing group remains cohesive. The nature of the transition between the cohesive and non-cohesive phases depends on the model parameters and forms a rich structure reminiscent of critical phenomena in ferromagnetic materials.

physics.soc-ph↗

arXiv2022

Disagreement and fragmentation in growing groups

The arise of disagreement is an emergent phenomenon that can be observed within a growing social group and, beyond a certain threshold, can lead to group fragmentation. To better understand how disagreement emerges, we introduce an analytically tractable model of group formation where individuals have multidimensional binary opinions and the group grows through a noisy homophily principle, i.e., like-minded individuals attract each other with exceptions occurring with some small probability. Assuming that the level of disagreement is correlated with the number of different opinions coexisting within the group, we find analytically and numerically that in growing groups disagreement emerges spontaneously regardless of how small the noise in the system is. Moreover, for groups of infinite size, fragmentation is inevitable. We also show that the model outcomes are robust under different group growth mechanisms.

physics.soc-ph↗