The Evolutionary Origins of Hierarchy, or how networks organize themselves

A few adaptationist arguments assume that organization is always a result of direct selection, so I thought it would be interesting to discuss a paper on how a few simple constraints can cause evolving systems to organize themselves regardless of what they're selected for.

As usual, Tuesday at 10:00 am in Darwin (with fika).

The Evolutionary Origins of Hierarchy

Mengistu H, Huizinga J, Mouret JB, Clune J (2016). PLOS Computational Biology 12(6): e1004829. doi: 10.1371/journal.pcbi.1004829

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.