A coalgebraic semantics for causality in Petri nets
Roberto Bruni, Ugo Montanari, Matteo Sammartino

TL;DR
This paper develops a coalgebraic framework for modeling causality in Petri nets, providing both set-theoretic and coalgebraic models that capture causal dependencies and enable state reduction.
Contribution
It introduces a novel coalgebraic semantics for Petri nets with causality, linking set-theoretic and coalgebraic models, and offers a compact, minimal representation of causal behavior.
Findings
Set-theoretic causal models correspond with behavior structure semantics.
Finitely-branching models with symmetries enable minimal state representations.
Coalgebraic models can be reduced to compact automata via presheaf-based equivalences.
Abstract
In this paper we revisit some pioneering efforts to equip Petri nets with compact operational models for expressing causality. The models we propose have a bisimilarity relation and a minimal representative for each equivalence class, and they can be fully explained as coalgebras on a presheaf category on an index category of partial orders. First, we provide a set-theoretic model in the form of a a causal case graph, that is a labeled transition system where states and transitions represent markings and firings of the net, respectively, and are equipped with causal information. Most importantly, each state has a poset representing causal dependencies among past events. Our first result shows the correspondence with behavior structure semantics as proposed by Trakhtenbrot and Rabinovich. Causal case graphs may be infinitely-branching and have infinitely many states, but we show how they…
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