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Although such methods seem promising pertaining to exploring emergent behaviour, obtaining the governing rulesets of such methods remains challenging. In this paper, we present a concept of combined use of methods targeting different levels\/scales in an emergent behaviour to obtain software-based artificial life rulesets from observed data. Additionally, we investigate the consequences of using this combination of methods by proposing an instance of combining Cellular Automata and Agent-based modelling with Koopman-based linearization. Our experiments on systems of Elementary Cellular Automaton (Rule 30), Game of Life (GOL), and Vicsek\u2019s flocking show that the combined method can learn the overall non-linear and emergent behaviour, and the underlying governing rulesets. 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