Diverse sports team formed with AI, equations in background.

Dividing the Spoils: Can AI Make Team Assignments Fair?

Reese Marlowe in Tech & Innovation December 2025 • 4 min read.

"Explore how fair division algorithms, particularly those enhanced with AI, are changing the way teams are formed, ensuring equity and satisfaction for all involved."

Imagine you're a league organizer tasked with forming balanced and satisfied teams from a pool of diverse players. It's a challenge that goes beyond simply assigning individuals to groups. It requires a delicate balancing act to ensure fairness and satisfaction for everyone involved. Traditionally, this process might rely on manual selection, prone to biases and inefficiencies.

Fair division, a field of economics and computer science, tackles the problem of dividing resources or tasks among individuals or groups in a way that minimizes conflict and maximizes satisfaction. Recently, researchers have been exploring how to apply fair division algorithms to team assignments, considering the preferences of both the teams and the individuals involved.

A new study delves into the complexities of fair division in team formation, introducing algorithms that aim to guarantee envy-freeness and stability. These algorithms not only seek to distribute talent equitably but also take into account the personal preferences of the participants, ensuring a more harmonious and productive team environment.

The Two-Sided Preference Problem: Why It Matters

Diverse sports team formed with AI, equations in background.

Traditional fair division algorithms often focus on one-sided preferences, meaning they only consider the desires of one party involved. In team assignments, this might mean only considering the team's preferences for players, without regard for the players' own preferences for which team they'd like to join. This oversight can lead to suboptimal outcomes and dissatisfaction.

The new research addresses this limitation by incorporating two-sided preferences, where both the teams and the individuals have a say in the assignment process. This approach acknowledges that individuals might have reasons to prefer certain teams, such as familiarity with the coach, proximity to their home, or the team's overall culture. By considering these preferences, the algorithm aims to create assignments that are not only fair but also mutually beneficial.

Envy-Freeness: Ensures that no team envies another, meaning they wouldn't prefer another team's set of members over their own.
Swap Stability: Prevents situations where two individuals would both be better off swapping teams, maintaining overall satisfaction.
Individual Stability: Guarantees that no individual would prefer to leave their assigned team for another, promoting team cohesion and commitment.

The study focuses on guaranteeing envy-freeness up to one participant (EF1), which means that any envy can be eliminated by removing just one member from the envied team. This condition, combined with swap stability and individual stability, ensures a robust and equitable allocation that minimizes dissatisfaction and promotes team harmony.

The Future of Fair Team Formation

As AI continues to evolve, fair division algorithms hold immense potential for revolutionizing team formation across various domains. By incorporating two-sided preferences and prioritizing stability, these algorithms can create more equitable and satisfying team environments, leading to improved performance and overall morale. Embracing these innovative approaches promises a future where team assignments are not only fair but also contribute to the success and well-being of all involved.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.geb.2024.07.008,

Title: Fair Division With Two-Sided Preferences

Subject: cs.gt econ.th

Authors: Ayumi Igarashi, Yasushi Kawase, Warut Suksompong, Hanna Sumita

Published: 12-06-2022

Everything You Need To Know

What is fair division, and how is it applied to team assignments?

Fair division is a field in economics and computer science focused on dividing resources or tasks fairly among individuals or groups. When applied to team assignments, it uses algorithms to create teams that minimize conflict and maximize satisfaction among participants. Researchers are employing these algorithms to consider preferences of both teams and individuals, ensuring equitable distribution of talent and a harmonious team environment. The goal is to move beyond manual selection, which is prone to bias, and achieve more objective and efficient team formations.

How do two-sided preference algorithms improve team assignments compared to traditional methods?

Traditional algorithms often rely on one-sided preferences, typically focusing on team desires without considering individual preferences. This can lead to suboptimal outcomes and dissatisfaction. Two-sided preference algorithms, however, incorporate the preferences of both teams and individuals. This means the algorithm considers factors like a player's preference for a team based on the coach, location, or team culture. By considering these two-sided preferences, the algorithm aims to create assignments that are not only fair but also mutually beneficial, leading to better team cohesion and overall satisfaction.

What are the key principles of fairness used in the algorithms mentioned, and what do they mean?

The study emphasizes three key principles: * **Envy-Freeness:** Ensures that no team envies another, meaning they wouldn't prefer to have another team's members over their own. * **Swap Stability:** Prevents situations where two individuals would both be better off swapping teams, which preserves overall satisfaction. * **Individual Stability:** Guarantees that no individual would prefer to leave their assigned team for another, thus promoting team cohesion and commitment. The research focuses on 'envy-freeness up to one participant (EF1)' which means any envy can be eliminated by removing just one member from the envied team, which contributes to a more robust and equitable allocation.

How does considering individual preferences impact team dynamics and morale?

Incorporating individual preferences significantly impacts team dynamics and morale. When individuals are assigned to teams based on their preferences (like team culture, proximity, or coach familiarity), they are more likely to feel valued and satisfied. This leads to better team cohesion, increased commitment, and improved overall morale. When individuals are placed on teams where they want to be, it creates a more positive and productive team environment, where members are more likely to collaborate effectively and contribute to the team's success.

What are the future implications of AI-driven fair division algorithms for team formation?

The future of team formation, as envisioned by AI-driven fair division algorithms, promises a revolution across various domains. By leveraging two-sided preferences and prioritizing stability, these algorithms have the potential to create more equitable and satisfying team environments. This leads to improved team performance, higher morale, and a more positive overall experience for all involved. As AI continues to evolve, these algorithms can be refined and adapted to complex scenarios, ensuring that team assignments are not just fair but also contribute to the success and well-being of every participant.