# Predicting future conflict between team-members with parameter-free models of social networks

**Authors:** Nuria Rovira-Asenjo, Tania Gumi, Marta Sales-Pardo, Roger Guimera  
**arXiv:** [1411.1530](https://arxiv.org/abs/1411.1530)  
**Date:** 2014-11-06  
**Summary page:** https://www.summarizepaper.com/en/paper/1411.1530v1/

## Key points

- Teamwork can lead to improved performance and productivity
- Communication, coordination, and management costs can result in personal conflicts between team members
- Understanding conflict and developing diagnostic tools to prevent it is essential for teams
- Researchers investigated whether future conflict in small teams can be predicted using parameter-free models of social network structure
- Group-based models of complex networks successfully anticipated conflict in small teams while micro-based models did not provide accurate predictions
- Understanding social network structures within teams is important in preventing potential conflicts
- Organizations can proactively identify potential conflicts before they escalate by utilizing group-based models of complex networks.

## Summary

The benefits of teamwork are well-documented and can lead to improved performance and productivity. However, it also comes with communication, coordination, and management costs that can result in personal conflicts between team members. As teams play an increasingly important role in various contexts, understanding conflict and developing diagnostic tools to prevent it is essential. In this study by Nuria Rovira-Asenjo, Tania Gumi, Marta Sales-Pardo, and Roger Guimera, the authors investigate whether it is possible to predict future conflict in small teams using parameter-free models of social network structure. The researchers analyzed data from 86 team members in 16 small teams who worked on a real project for nine consecutive months. They found that group-based models of complex networks successfully anticipated conflict in small teams while micro-based models of structural balance traditionally used to model conflict did not provide accurate predictions. This study highlights the importance of understanding social network structures within teams and how they can impact potential conflicts. By utilizing group-based models of complex networks, organizations can proactively identify potential conflicts before they escalate and develop strategies to prevent them altogether.