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hubie [soylentnews.org] writes:

Biological swarms [wikipedia.org] are fascinating and even mesmerizing things to watch, as hundreds or even thousands of individual entities behave in a manner such that their collective behavior can act almost as a great organism that responds to its immediate environment. A large and diverse number of organisms exhibit collective behavior, so it is generally assumed that this permits tasks to be achieved that are well beyond what a single individual can achieve while operating without the need for top-down control. There has long been significant interest in understanding how to exploit this in engineered systems such as drone or bot swarms.

The challenge in understanding collective behavior is that one normally has to assume a priori a mathematical model to simulate, which means trying to extract rules for how the individual entities interact, and their relative interactions can change depending upon their changing environment. A new paper [nature.com] studied swarms of midges and they argued the case for not worrying about the individual particles, but treating it as a thermodynamics problem.

We argue that the essential nature of the group functionality is encoded in its properties—and therefore that understanding these properties both allows one to quantify the purpose of the collective behaviour and to predict the response of the group to environmental changes. As recent work has demonstrated, a powerful way to characterize these properties is to borrow ideas from other areas of physics. For groups on the move such as human crowds, hydrodynamics is a natural choice, and empirically measured constitutive laws have allowed the formulation of equations of motion that accurately predict how crowds flow. But for stationary groups such as insect swarms, where the group as a whole does not move even though its constituent individuals are continuously rearranging, thermodynamics is a more natural framework, as it allows one to precisely describe the state of the system irrespective of its net motion. The most fundamental relationship for doing so is the equation of state, which links the state variables that describe the macroscopic properties of the system and encodes how they co-vary in response to environmental changes.

The researchers applied external stimuli to induce pressure and temperature changes and found that they could bring the swarm through a thermodynamic cycle [wikipedia.org] similar to the behavior of an ideal gas. They note that this approach gives a quantitative way of analyzing and interpreting swarm data at the macroscale.

Finally, these results also provide a natural starting point for designing artificial collective systems by outlining a framework for adapting intuition and expertise gained from engineering thermodynamic systems to this new situation. This approach could, for example, be useful to guide the design of engineered drone swarms via machine learning techniques and to provide a precise and quantifiable global description of the collective nature of swarms.

Sinhuber, M., van der Vaart, K., Feng, Y. et al. An equation of state for insect swarms. Sci Rep 11, 3773 (2021).
DOI: 10.1038/s41598-021-83303-z [doi.org]

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