Causality and Networks

TL;DR

This paper explores the concept of causality in science and automata networks, arguing that causality can be formalized without fully defining it, highlighting its importance in scientific reasoning.

Contribution

It demonstrates how causality can be deliberately formalized in automata networks without requiring a fixed, comprehensive definition of causality.

Findings

Causality is central in scientific statements and automata networks.

Formalization of causality can be achieved without a complete definition.

Causality's role in science-making can be systematically studied.

Abstract

Causality is omnipresent in scientists' verbalisations of their understanding, even though we have no formal consensual scientific definition for it. In Automata Networks, it suffices to say that automata "influence" one another to introduce a notion of causality. One might argue that this merely is an incidental side effect of preferring statements expressed in natural languages to mathematical formulae. The discussion of this paper shows that if this is the case, then it is worth considering the effects of those preferences on the contents of the statements we make and the formulae we derive. And if it is not the case, that causality is a mere incidental side effect of our preferences of formulation, then causality must be worth some scientific attention per se. In any case, the paper illustrates how the innate sense of causality we have may be made deliberate and formal use of without having to pin down the elusive notion of causality to anything fixed and formal that wouldn't do justice to the wide range of ways it is involved in science-making.