arXiv subjects

Chunpeng Du

Publications and source records attributed to Chunpeng Du.

4 recordsLinked to original sources

arXiv2026

Modulating biodiversity through higher-order interactions and intraspecific competition in rock-paper-scissors dynamics

Understanding the mechanisms that govern species coexistence and biodiversity represents a fundamental challenge in ecology. This study extends the classic rock-paper-scissors model by introducing a context-dependent higher-order interaction mechanism where intraspecific competition is dynamically regulated by local resource availability. Crucially, our quantitative analysis reveals that higher-order interactions significantly modulate the system's structural organization: Enhanced strength of higher-order interactions leads to a decrease in spatial wavelength, resulting in the formation of more compact species domains. However, this structural change makes the system more sensitive to mobility, shifting the extinction threshold to lower values. These findings highlight the dual role of resource-mediated higher-order regulation: it promotes local pattern formation but alters the system's resilience to dispersal, providing new theoretical perspectives for biodiversity conservation.

q-bio.PE↗

arXiv2026

Shaping the learning signal in a combined Q-learning rule to improve structured cooperation

Q-learning provides a standard reinforcement learning framework for studying cooperation by specifying how agents update action values from repeated local interactions outcomes. Although previous work has shown that reputation can promote cooperation in such systems, most models introduce reputation by modifying payoffs, encoding it directly in the state or changing partner selection, which makes it difficult to isolate the role of the learning signal itself. Here, we construct the reinforcement signal as a weighted combination of reputation and game payoffs, leaving the game and network structure unchanged. We find that increasing the weight on reputation generally promotes cooperation by consolidating clusters, but this effect is conditional on the learning dynamics. Specifically, this promoting effect vanishes in two regimes: when the learning rate is extremely small, which prevents effective information propagation and when the discount factor approaches one, as distant future expectations obscure the immediate reputational advantage. Outside these limiting cases, the efficacy of reputation in promoting cooperation is attenuated by higher learning rates but amplified by larger discount factors. These results advance the understanding of cooperative dynamics by demonstrating that cooperation can be stabilized through the reputational shaping of learning signals alone, providing critical insights into the interplay between social information and individual learning parameters.

physics.soc-ph↗

arXiv2025

Edge-based reputation promotes cooperation in simplicial complexes

Understanding how cooperation emerges and persists is a central challenge in the evolutionary dynamics of social and biological systems. Most prior studies have examined cooperation through pairwise interactions, yet real-world interactions often involve groups and higher-order structures. Reputation is a key mechanism for guiding strategic behavior in such contexts, but its role in higher-order networks remains underexplored. In this study, we introduce an edge-based reputation mechanism, incorporating both direct and indirect reputation, to investigate the evolution of cooperation in simplicial complexes. Our results show that coupling reputation mechanisms with higher-order network structures strongly promotes cooperation, with direct reputation exerting a stronger influence than indirect reputation. Moreover, we reveal a nonlinear interplay between network topology and reputation mechanisms, highlighting how multi-level structures shape collective outcomes. These findings provide a novel theoretical framework for understanding cooperation in complex social systems.

physics.soc-ph↗

arXiv2024

Generative Agent-Based Models for Complex Systems Research: a review

The advent of Large Language Models (LLMs) has significantly transformed the fields of natural and social sciences. Generative Agent-Based Models (GABMs), which utilize large language models in place of real subjects, are gaining increasing public attention. Far from aiming for comprehensiveness, this paper aims to offer readers an opportunity to understand how large language models are disrupting complex systems research and behavioral sciences. In particular, we evaluate recent advancements in various domains within complex systems, encompassing network science, evolutionary game theory, social dynamics, and epidemic propagation. Additionally, we propose possible directions for future research to further advance these fields.

physics.soc-ph↗