Structural Coordination Capacity, Not Incentive Intensity, Governs Decentralized Heterogeneous Multi-Robot Missions: Evidence from the MOSAIC Agent-Based Model
Year: 2026
Type: preprint
Abstract: Decentralized multi-robot missions require task discovery, communication, capability-aware allocation, temporary coalition formation, synchronized execution, and recovery from failed assignments. Incentives are often proposed as a mechanism for aligning local decisions with mission outcomes, but their effectiveness depends on whether the system can first establish informational and operational feasibility. This manuscript introduces MOSAIC—Mission-Oriented Self-Organization through Auctions, Incentives, and Coalitions—an agent-based model of heterogeneous robots operating under partial observability, spatially variable risk, limited energy, dynamic communication, and individual and cooperative task requirements. Seven paired-seed experiments comprising 690 official simulation runs evaluate baseline viability, reward regimes, communication structure, capability heterogeneity, cooperative-task demand, reputation and adaptive strategies, and mission-incentive strength. The analyses use non-parametric repeated-measures tests, planned paired contrasts, Holm corrections, rank-biserial effect sizes, Kendall’s W, and bootstrap intervals. The results indicate that mission completion is governed primarily by structural coordination capacity. High cooperative demand creates a nonlinear coalition-formation bottleneck, moderate capability heterogeneity outperforms both homogeneous and highly heterogeneous teams, and increased communication reach improves both mission and process outcomes. By contrast, leader-priority rewards increase inequality and auction congestion without improving completion, and stronger mission incentives increase payouts without producing reliable mission-performance gains. The complete MOSAIC model, source code, experimental datasets, documentation, analysis notebooks, statistical outputs, Technical Report, and reproducibility materials are publicly available at: https://github.com/ka-means/mosaic-agent-based-multi-robot-coordination This manuscript has not yet undergone journal peer review.
Authors: Molina, Katherin, Holguin, Lorena
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7172003027
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