Carlos Gershenson
Responses to his published papers on Boolean networks, emergence and self-organization
Carlos Gershenson studies complex systems at the Universidad Nacional Autónoma de México, where he is affiliated with the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas and the Centro de Ciencias de la Complejidad. His papers on random Boolean networks, and on measuring emergence, self-organization and complexity with information, cover much of the ground this framework works on. This page collects the site’s responses to those papers, one paper at a time.
How to read these responses
Each response takes one public paper, quotes it directly, and links to the original. It says where this framework agrees with the paper, where the framework adds its own layer, and what research the paper suggests next. Where a response maps Gershenson’s terms onto this framework, that mapping is the author’s reading. Gershenson does not use this framework, and nothing here should be read as his endorsement of it.
Responses
Introduction to Random Boolean Networks (2004)
Gershenson, C. (2004). Introduction to Random Boolean Networks. In M. Bedau et al. (Eds.), Workshop and Tutorial Proceedings, Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife IX), pp. 160–173. arXiv:nlin/0408006.
A network of simple on/off switches, each wired to a few others, settles into repeating cycles. The response agrees that these cycles are attractors with basins, in the same sense this framework uses. It then asks a question the tutorial does not ask: what keeps a cycle going, and who pays for it. In a Boolean network the answer is that nothing pays, because the cycle runs for free. The response closes with a proposed experiment that adds a running cost to Gershenson’s own networks.
Read the response →Emergence in Artificial Life (2023)
Gershenson, C. (2023). Emergence in Artificial Life. Artificial Life, 29(2), 153–167. https://doi.org/10.1162/artl_a_00397
Gershenson defines emergence as “information that is not present at one scale but present at another,” and builds measures of emergence, self-organization and complexity from it. The response recommends six directions for further research, each a test that separates two cases the current measures score alike:
- Order made inside or supplied from outside. Remove the outside source of order and measure again. Gershenson names this problem himself as “exo-organization.”
- Where the line is drawn. State the boundary and the scale before measuring, then repeat the measurement at other boundaries and scales.
- Complex, and able to come back. Push the system and see whether it returns, and plot that next to the complexity score.
- A whole that keeps its parts. Separate the parts from the whole and see which survives, and whether the whole can rebuild the parts.
- Where new rules come from. In long evolutionary runs, record whether each new rule was made by the system or supplied by the experimenter.
- What a measure misses. Publish each measure with a list of the cases where it gets the answer wrong.