Most of the grand challenges of humanity today involve complex agent-based systems, such as epidemiology, economics or ecology. However, remains as a pending task the challenge of identifying the general principles underlying the URLanizing capabilitiesof those complex systems . This article presents a novel modeling approach  capable to self-deploy both the system structure and the activities for goal-driven agents that can take appropriate actions to achieve their goals. Humans, robots, and animals are all endowed with this type of behavior. URLanization is shown to emerge from the decisions of a common rational activity algorithm  based on the information of a system-specific goals dependency network. The unique self-deployment feature of this systematic approach can boost considerably the range and depth of application of agent-based modeling.
Most of the grand challenges of humanity today involve complex agent-based systems, such as epidemiology, economics or ecology. However, remains as a pending task the challenge of identifying the general principles underlying their URLanizing capabilities . This article presents a novel modeling approach , capable to self-deploy both the system structure and the activities for goal-driven agents that can take appropriate actions to achieve their goals. Humans, robots, and animals are all endowed with this type of behavior. URLanization is shown to emerge from the decisions of a common rational activity algorithm , based on the information of a system-specific goals dependency network. The unique self-deployment feature of this approach, that can also be applied to non-goal-driven agents, can boost considerably the range and depth of application of agent-based modeling.
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the URLanizing capabilitiesof those complex systems
their URLanizing capabilities
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systematic approach
approach, that can also be applied to non-goal-driven agents,