Social learning strategies modify the effect of network structure on group performance
Not on the map. Why?
Abstract (arXiv)
The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 6.
10 papers.
- 2023A Review of Cooperation in Multi-agent Learningcited by 27
- 2025When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMscited by 14
- 2026GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systemscited by 5
- 2022Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learningcited by 3
- 2025Semantic knowledge guides innovation and drives cultural evolutioncited by 0
- 2026Artificial collectives of specialists and generalists excel at different taskscited by 0
- 2026Communication Heterogeneity and Collective Consensus in Neural Cellular Automatacited by 0
- 2026Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experimentscited by 0
- 2026Discovering Adaptive Transmission Programs for Collective Innovationcited by 0
- 2026From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMscited by 0
- 2026From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMs29 Sep 2026
- 2026Discovering Adaptive Transmission Programs for Collective Innovation25 Aug 2026
- 2026Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments16 Jul 2026
- 2026Communication Heterogeneity and Collective Consensus in Neural Cellular Automata19 Jun 2026
- 2026Artificial collectives of specialists and generalists excel at different tasks18 Jun 2026
- 2026GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems20 Mar 2026
- 2025When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs21 Oct 2025
- 2025Semantic knowledge guides innovation and drives cultural evolution13 Oct 2025
- 2023A Review of Cooperation in Multi-agent Learning8 Dec 2023
- 2022Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning26 Apr 2022