Nested Team Formation

• Nested team formation is a framework that organizes agents into hierarchical, overlapping teams to dynamically optimize both local and global objectives.
• It leverages algorithms such as TFC, BERTeam, and dynamic programming to achieve adaptive, multilevel decision-making in diverse settings.
• The approach reveals complex stability phenomena, balancing efficiency and robustness through equilibrium analyses and decentralized coordination.

Nested team formation is the organization of agents (human, robot, algorithmic, or hybrid) into hierarchical or overlapping subgroups, often with multilevel control or interaction structures. Teams may dynamically split, merge, or reconfigure, producing structures where subteams act both independently and collectively to optimize global or local objectives. This concept generalizes classical models—such as bipartite matching, coalition formation, or group control—to settings where teams are non-disjoint, heterogeneous in size/composition, and can be embedded within other teams or communities.

1. Mathematical Foundations and Models

Nested team formation is formalized in various frameworks, ranging from discrete combinatorial optimization to continuous control. Canonical mathematical elements include:

• State Description: A nested team configuration is a collection $x \subseteq P$—where $P$ encodes feasible teams/projects—subject to resource, time, or technological constraints. Agents’ participation can overlap: each agent may divide their resources (e.g., time) among multiple teams, permitting both nested and cross-linked memberships (Boncinelli et al., 2021).
• Dynamic Information Structures: In sequential decision problems, agents’ information sets may be nested; e.g., $M_t^2 \subseteq M_t^1$ represents agent 2’s knowledge as a subset of agent 1’s, with control policies derived by exploiting this nesting, particularly via dynamic programming decompositions (Dave et al., 2021).
• Hierarchical Decomposition: For robotic teams, nested formation is implemented as a multi-level graph structure, where high-level “team cut” partitions the full team; subteams maintain adaptive micro-formations; and individual policies operate at the lowest level (Deng et al., 19 Sep 2025).

These models allow for the representation, optimization, and stability analysis of team structures featuring overlapping memberships, hierarchical control, and heterogeneous team sizes.

2. Algorithms for Nested and Multi-Level Team Formation

Several algorithmic paradigms address nested team formation:

• Community-Based Multi-Team Selection: Algorithms such as TFC (Team Formation using Communities), TFC-R, and TFC-N induce nested teams by leveraging the (possibly overlapping) modularity of real-world networks. Teams are assembled locally within identified communities, allowing multiple, nested solutions per task. Leaders are selected based on connectivity and skill coverage, with teams expanded by neighbor aggregation and skill coverage heuristics (Addanki et al., 2020).
• Transformer-Guided Selection and Coevolution: BERTeam applies deep sequence modeling (transformers with masked language modeling) on top of populations generated via coevolutionary reinforcement learning. This stack uses hierarchical optimization, generating complex, non-trivial compositions of agents. The nested optimization arises from the separation of agent-level coevolution and team-level selection via transformer modeling (Rajbhandari et al., 17 Oct 2024).
• Hierarchical Learning Frameworks in Robotics: STAF decomposes control into three learning stages: (1) high-level deep graph cut for dynamic team splitting; (2) intermediate graph learning for intra-subteam coordination and adaptive formation control (spring-damper models); (3) low-level decentralized policies for execution. This permits teams to split, traverse constraints, and regroup autonomously while retaining global coherence (Deng et al., 19 Sep 2025).
• Dynamic Programming with Nested Information: For agents with sequential decisions, dynamic programs are constructed to recursively optimize team actions, exploiting the nested structure of information and prescriptions between agents to reduce computation and derive explicit optimal control laws (Dave et al., 2021).

The efficient scaling of these algorithms is often attributed to leveraging network/community structure, hierarchical decomposition, and tailored stability criteria.

3. Stability, Efficiency, and Equilibrium Concepts

Nested team formation admits rich equilibrium and stability phenomena:

• Myopic Team-wise Stability (MTS): Team configurations are stable if no subset of agents can dissolve or form teams that would strictly improve their utility. MTS captures a broad set of maximal feasible states (possibly nested and overlapping) under mild assumptions (Boncinelli et al., 2021).
• Coalitional Stability (CS): Stronger than MTS, CS requires that no coalition of agents can mutually reconfigure teams to their collective advantage. However, under linear preferences, CS and MTS often coincide, meaning nested structures are robust to deep coordination attempts.
• Stochastic Stability (SS): In dynamic environments with rare mistakes, the system tends toward configurations maximizing the number of possible activities (teams), favoring highly nested and overlapping states. SS serves as a “selection principle” for emergent team structures under bounded rationality (Boncinelli et al., 2021).
• Nested Information-based Simplification: When agent knowledge is nested, dynamic programs admit reduced state spaces; for instance, agent 1’s policies need only agent 2’s information plus her own belief, allowing a substantial reduction in search complexity (Dave et al., 2021).
• Adaptive Formation Integrity: In hierarchical robotics, stability is related to formation metrics such as success rate, travel time, and contextual formation integrity, with subteams capable of maintaining formation and recombining after constraint traversal (Deng et al., 19 Sep 2025).

A plausible implication is that the nestedness of team structure and information flow fundamentally determines both the tractability of optimization and the nature of equilibrium configurations.

4. Practical Applications and Empirical Results

Nested team formation frameworks are validated in a range of domains:

• Multiagent Adversarial Games: In Marine Capture-The-Flag, BERTeam outperforms population-based heuristics by selecting diverse and strategically robust teams, with higher empirical win rates and resilience to novel opponents (Rajbhandari et al., 17 Oct 2024).
• Multi-Robot Coordination: STAF achieves 100% success rate in simulated and real-world navigation of robot teams through constrained environments, retaining high formation integrity and robust regrouping (Deng et al., 19 Sep 2025).
• Scientific Collaborations: Community-based team formation algorithms efficiently generate multiple, nested author teams for writing papers given required skills, with substantial improvements in computational efficiency and comparable communication costs versus standard benchmarks (Addanki et al., 2020).
• Dynamic Control Teams: In problems with nested information, optimal control policies are derived for vehicle platoons, hierarchical production, and communication systems, exploiting the reduction in possible policy spaces (Dave et al., 2021).
• Coauthorship and R&D Networks: Nested, overlapping teams naturally arise in models of collaboration, with stochastic stability favoring maximal use of available resources, though not always guaranteeing Pareto efficiency due to congestion phenomena (Boncinelli et al., 2021).

This suggests that scalable, hierarchical approaches to nested team formation yield both computational and organizational benefits in systems where team diversity, redundancy, or spatial adaptability are valued.

5. Implications, Limitations, and Prospects

Nested team formation models and algorithms provide generalizable templates for coordination, selection, and stability in complex agent systems. However, limitations and open issues persist:

• Efficiency vs. Maximal Activity: Stochastically stable states may not coincide with Pareto efficient team structures, highlighting the potential for inefficiency due to overlapping team congestion or negative externalities (Boncinelli et al., 2021).
• Scalability and Decentralization: While hierarchical learning frameworks (e.g., STAF) are effective, centralized decisions at higher levels can be limiting; extensions to fully decentralized or distributed architectures remain active research directions (Deng et al., 19 Sep 2025).
• Diversity and Redundancy: Community-based multi-team formation supports organizational resilience but can raise issues of resource allocation or communication overhead in deeply nested or overlapping teams (Addanki et al., 2020).
• Generalizability: Approaches leveraging deep sequence models or graph attention have primarily been demonstrated in game and robotics contexts but are applicable to broader domains involving agent composition, control, and selection.

A plausible implication is that ongoing methodological development—including graph-based learning, dynamic programming for nested information, and robust equilibrium analysis—will further extend the reach of nested team formation to distributed systems, large-scale collaborations, and adaptive control environments.

6. Summary Table: Selected Models and Their Properties

Model/Algorithm	Nested/Overlapping Supported	Primary Domain
TFC, TFC-R, TFC-N (Addanki et al., 2020)	Yes	Networks, collaboration, organizations
BERTeam (Rajbhandari et al., 17 Oct 2024)	Implicit nested selection	Multiagent games
STAF (Deng et al., 19 Sep 2025)	Yes (hierarchical subteaming)	Multi-robot navigation
Dynamic program (Dave et al., 2021)	Yes (nested info structures)	Team control, sequential decision
General model (Boncinelli et al., 2021)	Yes (fully general)	Collaboration, matching, networks

Nested team formation is a structurally rich and broadly applicable paradigm, supporting the creation, adaptation, and analysis of multilevel teams in real-world and synthetic systems. Its mathematical, algorithmic, and experimental properties elucidate the interplay between team composition, stability, efficiency, and scalability across domains.