arXiv subjects

Dini Wang

Publications and source records attributed to Dini Wang.

4 recordsLinked to original sources

arXiv2026

Modest Algorithmic Mediation can Maximize Topical Diversity in Hybrid Human-AI Systems

In the artificial intelligence (AI) era, the rise of algorithmic feeds has fundamentally transformed information diffusion on social media. While early platforms organized visibility through explicit social networks, contemporary systems mediate exposure through intelligent recommender algorithms that personalize attention. This paper examines how the social network and algorithmic architecture jointly shape the diversity of information sharing. Analysis of 18,076 users active throughout 2014--2018 shows that the topical diversity of sharing rose and then plateaued after the introduction of algorithmic ranking in 2016 while its inequality across users emerged alongside it. To this end, we introduce a hybrid human-AI information diffusion model in which information exposure is governed by a parameterized mixture of social propagation through the user-following network and algorithmic recommendation. Both qualitative analysis and simulations show that the effect of algorithmic mediation is non-monotonic. Modest mediation can raise average diversity and reduce inequality relative to a purely network-driven baseline, whereas strong mediation reduces diversity and concentrates it among fewer users. Fitting the model to four years of data yields a mediation share that increases from zero before 2016 to approximately 0.50 by 2018, a level that exceeds the compensation point of equality while remaining within the diversity-enhancing range. These results identify the conditions under which recommendation broadens rather than narrows exposure and provide a unified framework for information diffusion in hybrid human-AI systems.

cs.SI↗

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

Emergence of cooperation promoted by higher-order strategy updates

Cooperation is fundamental to human societies, and the interaction structure among individuals profoundly shapes its emergence and evolution. In real-world scenarios, cooperation prevails in multi-group (higher-order) populations, beyond just dyadic behaviors. Despite recent studies on group dilemmas in higher-order networks, the exploration of cooperation driven by higher-order strategy updates remains limited due to the intricacy and indivisibility of group-wise interactions. Here we investigate four categories of higher-order mechanisms for strategy updates in public goods games and establish their mathematical conditions for the emergence of cooperation. Such conditions uncover the impact of both higher-order strategy updates and network properties on evolutionary outcomes, notably highlighting the enhancement of cooperation by overlaps between groups. Interestingly, we discover that the strategical mechanism alternating optimality and randomness -- selecting an outstanding group and then imitating a random individual within this group -- can prominently promote cooperation. Our analyses further unveil that, compared to pairwise interactions, higher-order strategy updates generally improve cooperation in most higher-order networks. These findings underscore the pivotal role of higher-order strategy updates in fostering collective cooperation in complex social systems.

physics.soc-ph↗

arXiv2024

Evolutionary game on any hypergraph

Cooperation plays a fundamental role in societal and biological domains, and the population structure profoundly shapes the dynamics of evolution. Practically, individuals behave either altruistically or egoistically in multiple groups, such as relatives, friends and colleagues, and feedbacks from these groupwise interactions will contribute to one's cognition and behavior. Due to the intricacy within and between groups, exploration of evolutionary dynamics over hypergraphs is relatively limited to date. To uncover this conundrum, we develop a higher-order random walk framework for five distinct updating rules, thus establishing explicit conditions for cooperation emergence on hypergraphs, and finding the overlaps between groups tend to foster cooperative behaviors. Our systematic analysis quantifies how the order and hyperdegree govern evolutionary outcomes. We also discover that whenever following a group wisdom update protocol, choosing a high-fitness group to interact equally within its members, cooperators will significantly prevail throughout the community. These findings underscore a crucial role of higher-order interaction and interdisciplinary collaboration throughout a broad range of living systems, favoring social prosperity.

nlin.AO↗