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4. Autonomy & Regulation

Autonomy & Regulation

  • In a multi-agent system, agents act on their own, following their own goals, beliefs, and abilities. The overall system behavior comes from how they interact, which means it can be hard to predict or control from the outside. But in complex or critical situations, the needs and expectations of the whole system must be considered. This includes stability over time, predictability, and commitment to shared goals — factors that cannot be reduced to the actions of individual agents.

  • From this view, interactions in a multi-agent system cannot rely only on what each agent can do. The structure and rules of the agency must be defined in advance. Agency approaches to multi-agent systems use high-level, agent-independent concepts to describe the environment in which agents operate. These include the rules and shared objectives that guide the activities of a group, organization, or larger system, and they are set outside the design of individual agents.

  • In a multi-agent system, we cannot assume that agents will naturally coordinate their actions, and in organizational models, we cannot assume they will always act in line with global needs and expectations. This creates a balance to be managed between regulation and autonomy. For this reason, multi-agent organizations need clear specifications that define mechanisms for guiding, protecting, and encouraging the right kind of behavior. These mechanisms help build trust among agents that choose to join the organization.

  • In Agencies, the structure is defined in advance and does not depend on who the participants are. From the agency's perspective, the main role of an agent is to carry out a function that supports the shared goals of the group. The agency's goals shape the roles and the rules for interaction, and agents are seen as the actors who perform these roles.

  • From the agent’s perspective, autonomy means deciding its own actions, plans, and beliefs. An agent’s behavior is guided by its own goals and abilities, which shape how it chooses to carry out its role. This means agents bring their own methods, preferences, and motivations into the system. The overall behavior of the agency then emerges from the mix of individual agents pursuing their own goals within the boundaries set by the agency.

  • In short, AgencyGrid and AgentGrid bring together the strengths of many agents to achieve shared goals while still respecting the individual aims and personalities of autonomous agents. In open environments, AgencyGrid meets two main requirements:

    • Internal autonomy – The Agency's structure and interactions must be defined without depending on the internal design of the agents.
    • Collaboration autonomy – The Agency's activities and interactions should be set up without fixing all possible interactions in advance, leaving room for flexibility.

  • The first requirement is important because an open agency can include many different agents with unknown designs and capabilities. The second addresses the balance between achieving the agency's goals and allowing agents to keep their independence.

  • The more details an agency design includes about interactions, the easier it is to check and guarantee certain requirements during the design stage. This can ensure that specific rules are always followed. However, giving agents more freedom allows them to decide how to collaborate, which can increase flexibility and adaptability.

  • The level of detail in the agency's design also affects how much autonomy agents need. A very detailed design gives agents fewer options and less freedom, while a more abstract design requires agents to have greater autonomy and reasoning skills so they can interpret the specification and decide how to work with others. Abstract models give more flexibility, but also place more responsibility on the agents.

  • Choosing the right level of detail is a design decision that helps balance control and autonomy.