A Simple Hierarchical Causality Primer
2026-06-01 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors explain how to understand cause-and-effect relationships in complex systems that have many levels, like groups within groups. They say that 'actors' are different from 'agents'; actors represent types of causes, while agents do the actual actions inside the system's levels. To study how causes work across these levels, the authors introduce three key ideas: classes of causation to represent types of influence, ways to combine information from different levels, and timelines that track events locally and globally. Their approach is simple and uses discrete steps to keep things clear.
hierarchical causalityactorsagentscausation classesaggregation operatorsdiscrete event-time mapscomplex systemslevels of organizationlocal dynamicscausal influence
Authors
Tim Gebbie
Abstract
We provide a brief primer for the idea behind formalising hierarchical causality in the context of complex systems. Here actors are not simply agents. Actors instantiate causation classes. Agents implement local dynamics in given levels or organisation in a given system. Hierarchical causality then describes how actor-level roles constrain, select, and organise agent-level behaviour across levels. The system then necessarily requires three additional structures. First, causation classes to abstract a given form of causal influence that an actor instantiates. Second, aggregation operators to move across the levels. Third, discrete event-time maps are required because the system comprises events, and the relation between local event counts and any global clock must be specified. Our formulation here is purposefully simple and discrete.