From description to design: Automated engineering of complex systems with desirable emergent properties
This work addresses the challenge of moving complex systems science from descriptive analysis to practical engineering for researchers and practitioners, though it is incremental as it builds on existing optimization methods.
The authors tackled the problem of engineering complex systems to exhibit specific emergent properties by proposing an optimization-based pipeline that repurposes descriptive statistics as loss functions, enabling gradient descent to design micro-scale features. They demonstrated the approach on Kuramoto oscillator systems, reliably producing non-trivial global properties like synergistic information and meso-scale structures while accommodating constraints such as connection costs.
The study of complex systems has produced a huge library of different descriptive statistics that scientists can use to describe the various emergent patterns that characterize complex systems. The problem of engineering systems to display those patterns from first principles is a much harder one, however, as a hallmark of complexity is that macro-scale emergent properties are often difficult to predict from micro-scale features. Here, we propose a general optimization-based pipeline to automate the difficult problem of engineering emergent features by re-purposing descriptive statistics as loss functions, and letting a gradient descent optimizer do the hard work of designing the relevant micro-scale features and interactions. Using Kuramoto systems of coupled oscillators as a test bed, we show that our approach can reliably produce systems with non-trivial global properties, including higher-order synergistic information, multi-attractor metastability, and meso-scale structures such as modules and integrated information. We further show that this pipeline can also account for and accommodate constraints on the system properties, such as the costs of connections, or topological restrictions. This work is a step forward on the path moving complex systems science from a field predicated largely on description and post-hoc storytelling towards one capable of engineering real-world systems with desirable emergent meso-scale and macro-scale properties.