Cover Image

Learning to connect in action: Measuring and understanding the emergence of boundary spanners in volatile times

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nespeca, Vittorio, Comes, Tina, Brazier, Frances
Format:	Preprint
Published:	2024
Subjects:	Multiagent Systems
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge
by: Oetker, Frederike, et al.
Published: (2023)

ToMacVF : Temporal Macro-action Value Factorization for Asynchronous Multi-Agent Reinforcement Learning
by: Zhang, Wenjing, et al.
Published: (2025)

A systematic review of norm emergence in multi-agent systems
by: Cordova, Carmengelys, et al.
Published: (2024)

Effects of non-uniform number of actions by Hawkes process on spatial cooperation
by: Miyagawa, Daiki, et al.
Published: (2024)

Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form
by: Wang, Xuefeng, et al.
Published: (2026)

Multi-robot connective collaboration toward collective obstacle field traversal
by: Hu, Haodi, et al.
Published: (2024)

Multi-robot coordination for connectivity recovery after unpredictable environment changes
by: Marchukov, Yaroslav, et al.
Published: (2025)

Measuring Policy Distance for Multi-Agent Reinforcement Learning
by: Hu, Tianyi, et al.
Published: (2024)

Guiding LLM-Based Human Mobility Simulation with Mobility Measures from Shared Data
by: Yan, Hua, et al.
Published: (2026)

Generalizing Liquid Democracy to multi-agent delegation: A Voting Weight Measure and Equilibrium Analysis
by: Bersetche, Francisco M.
Published: (2022)

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
by: Hughes, Edward, et al.
Published: (2025)

Finite-time convergence to an $ε$-efficient Nash equilibrium in potential games
by: Maddux, Anna, et al.
Published: (2024)

Polynomial-time Configuration Generator for Connected Unlabeled Multi-Agent Pathfinding
by: Suzuki, Takahiro, et al.
Published: (2025)

Decentralised multi-agent coordination for real-time railway traffic management
by: D'Amato, Leo, et al.
Published: (2025)

Distributed Finite-time Differentiator for Multi-agent Systems Under Directed Graph
by: Chen, Weile, et al.
Published: (2024)

Learning Efficient Communication Protocols for Multi-Agent Reinforcement Learning
by: Zhang, Xinren, et al.
Published: (2025)

Learning Nudges for Conditional Cooperation: A Multi-Agent Reinforcement Learning Model
by: Kulkarni, Shatayu, et al.
Published: (2024)

Advances in Multi-agent Reinforcement Learning: Persistent Autonomy and Robot Learning Lab Report 2024
by: Azadeh, Reza
Published: (2024)

LLM-ALSO: LLM-Driven Adaptive Learning-Signal Optimization for Multi-Agent Reinforcement Learning
by: Wu, Xiaoguang, et al.
Published: (2026)

Mean Field Correlated Imitation Learning
by: Zhao, Zhiyu, et al.
Published: (2024)

Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
by: Li, Yihong, et al.
Published: (2023)

Performance bound analysis of linear consensus algorithm on strongly connected graphs using effective resistance and reversiblization
by: Yonaiyama, Takumi, et al.
Published: (2025)

Algorithmic Contract Design with Reinforcement Learning Agents
by: Concha, David Molina, et al.
Published: (2024)

Adaptive Social Learning using Theory of Mind
by: Ying, Lance, et al.
Published: (2025)

Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning
by: Liang, Yongheng, et al.
Published: (2024)

The Signaler-Responder Game: Learning to Communicate using Thompson Sampling
by: Bhuckory, Radhika, et al.
Published: (2024)

Confidence-Based Curriculum Learning for Multi-Agent Path Finding
by: Phan, Thomy, et al.
Published: (2024)

Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning
by: Fan, Wenzhe, et al.
Published: (2024)

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning
by: McClusky, Ben
Published: (2024)

LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning
by: Na, Hyungho, et al.
Published: (2024)

Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
by: Fabbro, Nicolò Dal, et al.
Published: (2024)

Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
by: Mguni, David, et al.
Published: (2025)

Predictive Auxiliary Learning for Belief-based Multi-Agent Systems
by: Huang, Qinwei, et al.
Published: (2025)

Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning
by: Lee, Min Whoo, et al.
Published: (2025)

Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game
by: Guo, Honglei, et al.
Published: (2026)

Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
by: Toquebiau, Maxime, et al.
Published: (2025)

LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
by: Hua, Yun, et al.
Published: (2023)

Human-Inspired Pavlovian and Instrumental Learning for Autonomous Agent Navigation
by: Shan, Jingfeng, et al.
Published: (2026)

Consensus seeking in diffusive multidimensional networks with a repeated interaction pattern and time-delays
by: Vu, Hoang Huy, et al.
Published: (2024)

Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
by: Li, Huao, et al.
Published: (2024)