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Although models are built on the basis of some observations of reality, theconcepts that derive theoretically from their definitions as well as from theircharacteristics and properties are not necessarily direct consequences of theseinitial observations. Indeed, many of them rather follow from chains oftheoretical inferences that are only based on the precise model definitions andrely strongly, in addition, on some consequential working hypotheses. Thus, itis important to address the question of which features of a model effectivelycarry some modelling meaning and which only result from the task of formalisingobservations of reality into a mathematical language. In this article, weaddress this question with a theoretical point view that sets our discussionstrictly between the two stages of the modelling process that require knowledgeof real systems, that is, between the initial stage that chooses a globaltheoretical framework to build the model and the final stage that exploits itsformal predictions by comparing them to the reality that the model was designedto simulate. Taking Boolean automata networks as instances of models of systemsobserved in reality, we analyse in this setting the remaining stages of themodelling process and we show how the meaning of theoretical concepts cansubtly rely on formal choices such as definitions and hypotheses.

Towards a theory of modelling with Boolean automata networks - I. Theorisation and observations