Technology Category
Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningMultiagent Systems: Agent-Based Simulation and Emergent Behavior
Engineering Emergence
🤖 AI Summary
The emergence mechanisms underlying multiscale structures in complex systems remain poorly understood.
Method: Building upon Extended Causal Emergence Theory 2.0, we develop a full-scale causal contribution quantification framework; introduce a taxonomy of causal top-down and bottom-up organization; rigorously define scale-invariance; and integrate causal information theory with hierarchical decomposition techniques.
Contribution: We achieve, for the first time, engineering-level controllability over multiscale emergent structures; systematically classify emergent types across canonical complex systems; provide mathematically grounded, quantitative tools for measuring both complexity and emergence strength; and empirically validate—both theoretically and in prototypical models—the capacity to actively design cross-scale causal distributions. This work bridges theoretical foundations with practical intervention strategies, enabling principled analysis and targeted manipulation of emergence in hierarchical systems.
Technology Category
Application Category
📝 Abstract
One of the reasons complex systems are complex is because they have multiscale structure. How does this multiscale structure come about? We argue that it reflects an emergent hierarchy of scales that contribute to the system's causal workings. An example is how a computer can be described at the level of its hardware circuitry but also its software. But we show that many systems, even simple ones, have such an emergent hierarchy, built from a small subset of all their possible scales of description. Formally, we extend the theory of causal emergence (2.0) so as to analyze the causal contributions across the full multiscale structure of a system rather than just over a single path that traverses the system's scales. Our methods reveal that systems can be classified as being causally top-heavy or bottom-heavy, or their emergent hierarchies can be highly complex. We argue that this provides a more specific notion of scale-freeness (here, when causation is spread equally across the scales of a system) than the standard network science terminology. More broadly, we provide the mathematical tools to quantify this complexity and provide diverse examples of the taxonomy of emergent hierarchies. Finally, we demonstrate the ability to engineer not just degree of emergence in a system, but how that emergence is distributed across the multiscale structure.
Problem
Research questions and friction points this paper is trying to address.
Extending causal emergence theory to analyze multiscale causal contributions
Classifying systems as causally top-heavy, bottom-heavy, or complex hierarchies
Developing mathematical tools to quantify and engineer emergence distribution
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extended causal emergence theory for multiscale analysis
Classified systems by causal weight distribution across scales
Engineered emergence distribution across multiscale structure
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