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arXiv 最近完成扫描:2026-11-16 01:34 · 全站最近入库:2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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新论文 · v1arXiv:2611.17148

资源景观中网红博弈的学习动力学

Learning Dynamics of the Influencer's Game in Resource Landscapes

Mark Lovett Feng Fu

AI 速读 基于摘要

提出“网红博弈”空间博弈框架,分析玩家在性状空间中争夺资源份额时对称均衡的存在性与稳定性,并探讨分岔与多智能体强化学习下的表现。

cs.GT · econ.TH · physics.soc-ph

查看原文摘要

Across domains as varied as ecology, economics, and online platforms, individuals are incentivized to position themselves within resource-rich strategy spaces. In this work we introduce a new spatial game framework called the influencer's game. Within this new framework players' strategies are represented as positions within a trait space. Players then receive a share of a portion of a resource based on their relative influence at that resource's points traits. For the influencer's game, we show that there is a unique symmetric Nash equilibrium that exists under strict log-concavity of the influence kernel. Using the unique equilibrium we show that the stability of the system depends on the influence kernel's parameters, number of players, and the resource distribution shape. For any dimension we demonstrate a stability threshold that depends on both the scatter and curvature of the players. When this threshold is crossed, the symmetric equilibrium destabilizes and gives way to a variety of bifurcation types; including, specialist clusters, limit cycles, and sub-games that reproduce the larger game's structure. We ground these results using real-world data for resources as a demonstration of the game's application. Finally we extend the game dynamics to use multi-agent reinforcement learning and further show that the predicted bifurcation structure persists under partial observability and discretized strategy spaces. In all, the influencer's game provides a novel framework for predicting when competitive pressure drives convergence versus fragmentation with possible applications across domains.

新论文 · v1arXiv:2611.11029

Evolutionary games on networks with negative edge-based environmental feedback

Katherine Betz, Feng Fu, Naoki Masuda

原文摘要

Environmental feedback can fundamentally alter the game payoff structure underlying the evolution of cooperation. How feedback-evolving games interact with population structure under non-weak selection remains incompletely understood. We study an evolutionary game on networks in which each edge has a binary environmental state that determines the payoff matrix used by the two connected individuals. By assumption, cooperation tends to improve the local environment, which in turn favors defection, thereby creating negative feedback between strategies coupled with the environmental condition. We analyze the model under strong selection using a mean-field approximation, a pair approximation that tracks both strategy pairs and edge environments, and agent-based simulations. In well-mixed populations, the feedback can generate bistability between full cooperation and full defection, as well as sustained oscillations arising through a Hopf bifurcation. Sparse networks (i.e., with low nodes' degrees) can eliminate bistability, suppress oscillations, and produce stable intermediate levels of cooperation. The pair approximation captures these effects and generally agrees more closely with simulations on sparse networks than the mean-field approximation. Our results show that edge-based environmental feedback and network structure, such as density of edges, interact to determine whether the fraction of cooperators stabilizes or oscillates over time.

physics.soc-ph · cs.SI · q-bio.PE

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1783ACL Anthology · ACL 2026

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns

Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie Fudan University, Jen-tse Huang, Jun Gao Momo Inc., Shuai Huang, Yueping Kang hellogroup 等 11 位作者

原文摘要

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). However, achieving authentic alignment with human cognitive and behavioral patterns remains a critical challenge for these agents. We present HumanLLM, a framework treating psychological patterns as interacting causal forces.We construct 244 patterns from \sim12,000 academic papers and synthesize 11,359 scenarios where 2-5 patterns reinforce, conflict, or modulate each other, with multi-turn conversations expressing inner thoughts, actions, and dialogue.Our dual-level checklists evaluate both individual pattern fidelity and emergent multi-pattern dynamics, achieving strong human alignment (r=0.90) while revealing that holistic metrics conflate simulation accuracy with social desirability.HumanLLM-8B outperforms Qwen3-32B on multi-pattern dynamics despite 4\times fewer parameters, demonstrating that authentic anthropomorphism requires cognitive modeling—simulating not just what humans do, but the psychological processes generating those behaviors.Our dataset, code, and model are available at:https://github.com/YJGoodbye2024/HumanLLM

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表