Dear Colleagues,

Understanding how the adaptive behavior of groups is controlled by the individuals within them is a major challenge for 21st century science. From proteins in a cell to neurons in a brain, and from fish in a school to people in society, we know how individual components behave and interact, but mapping this to consequences for adaptive behavior at the aggregate scale is difficult.

Statistical physics has a long history of success in approaching similar problems in non-living systems, connecting parsimonious macroscopic theories to the microscopic details that produce them. A growing community is working to apply these tools to collective behavior in adaptive systems. This is a particularly challenging arena given that adaptive systems often elude typical simplifying assumptions such as homogeneity, linearity, low-dimensional interaction topology, and clean separation of temporal and spatial scales.

Important questions pertaining to this topic include:

• Can phenomenological statistical models reveal fundamental regularities shared across all adaptive systems, or are their assumptions too simplistic?
• How are components in a collective regulated to produce functional aggregate outputs?
• How can we robustly infer predictive models from detailed datasets in systems that involve a hierarchy of scales?
• Can evolution, learning, and feedback be usefully included in renormalization-style theories of collective systems?

In this Special Issue, we aim to gather contemporary voices exploring these themes, utilizing concepts such as coarse graining, renormalization, scaling, phase transitions, collective instabilities, broken symmetries, dynamical modes, free energy, critical phenomena, and statistical inference to build parsimonious predictive theories describing the collective behavior of proteins, bacteria, neurons, insects, mammals, fish, robots, computers, artificial neural networks, species, people, societies, and ideas.

We welcome contributions that include:

- Empirical examples of collective behavior analyzed in the language of statistical physics.

- Theoretical progress toward a version of statistical mechanics that interfaces more productively with adaptive systems.

- Practical progress in data science to infer predictive models in challenging collective contexts.

Prof. Dr. Bryan Daniels
Guest Editor

Manuscript Submission Information

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• Collective behavior
• Statistical mechanics
• Adaptive systems
• Coarse graining
• Model simplification
• Statistical inference
• Collective computation
• Criticality in biology
• Distributed agency
• Causality across scales
• Strongly interacting systems.