Emergence in GEAMAS has been identified through a specific mechanism, the "Recomposition", which has been isolated in the architecture. Recomposition transfers information from reactive agents to cognitive agents. A Recomposition message is used by reactive agents to express their instability. The agent's instability is given by critical values (thresholds) of state parameters. The values of state parameters evolve during the simulation, according to multiple local interactions between reactive agents. When a threshold is hit, the agent is assumed to be unstable, and information is then transferred to the cognitive agent with a Recomposition message.
Hence, a Recomposition message is provided by reactive agents to alert cognitive agents and respectively the global society of agents, that something unusual happens. Shifting from the micro-level up to the upper-level, cognitive agent or society collects data on micro-behaviours and combines them to determine macro-behaviour and adapts himself to the situation. A higher behaviour is then emerging from this adaptation. In such a sense, interactions between reactive agents set emerging behaviours.