Trust-Regulated Dependence Networks for Multi-Agent Cooperation: A Simulation Study

This study investigates how structural properties of dependence networks and trust-based partner selection jointly shape cooperation in multi-agent societies. We develop a simulation framework grounded in a Block World environment in which agents pursue their goals under resource scarcity, heterogeneous competence, bounded network visibility, and explicit monetary exchange. Dependence emerges dynamically at the subgoal level, while trust thresholds regulate access to potential partners within structurally constrained influence zones. Results reveal a structural trade-off between effectiveness and efficiency: permissive trust strategies increase total utility, but at the cost of higher expenditures and lower utility-to-investment ratios. Conversely, more selective strategies reduce task completion but enhance economic sustainability. On the other hand, trustworthiness influences economic redistribution effects, representing a concrete advantage for reliable agents. These findings highlight the multidimensional nature of performance in dependence networks and provide theoretical and practical insights for scalable hybrid human–AI systems.