Towards a general theory of the major cooperative evolutionary transitions
John E.Stewart

Biosystems
Volume 198, December 2020, 104237

Major Cooperative Evolutionary Transitions occur when smaller-scale entities cooperate together to give rise to larger-scale entities that evolve and adapt as coherent wholes. Key examples of cooperative transitions are the emergence of the complex eukaryote cell from communities of simpler cells, the transition from eukaryote cells to multicellular organisms, and the organization of humans into complex, modern societies. A number of attempts have been made to develop a general theory of the major cooperative transitions. This paper begins by critiquing key aspects of these previous attempts. Largely, these attempts comprise poorly-integrated collections of separate models that were each originally developed to explain particular transitions. In contrast, this paper sets out to identify processes that are common to all cooperative transitions. It develops an alternative theoretical framework known as Management Theory. This general framework suggests that all major cooperative transitions are the result of the emergence of powerful, evolvable ‘managers’ that derive benefit from using their power to organize smaller-scale entities into larger-scale cooperatives. Management Theory is a contribution to the development of a general, “all levels” understanding of major cooperative transitions that is capable of identifying those features that are level-specific, those that are common across levels and those that are involved in trends across levels.

Source: www.sciencedirect.com

Amir Goldental and Ido Kanter

Journal of Physics A: Mathematical and Theoretical

This study investigates the dynamics of traffic containing human-driven vehicles along with a fraction of self-organized artificial intelligence (AI) autonomous vehicles (AVs) on multilane freeways. We propose guidelines for the development of AI agents, such that a small fraction of AVs forms local constellations that significantly accelerate the entire traffic flow while reducing fuel consumption and increasing safety. Specifically, we report a 40% enhancement in traffic flow efficiency and up to 27% reduction in fuel consumption even when only 5% of vehicles are autonomous. This scenario does not require changes to current infrastructure or communication between vehicles; it only requires proper regulations. The results indicate that more efficient, safer, faster, and greener traffic flow can be realized in the near future.

Source: iopscience.iop.org

Zachary G. Nicolaou, Takashi Nishikawa, Schuyler B. Nicholson, Jason R. Green, Adilson E. Motter

Complex chemical reaction networks, which underlie many industrial and biological processes, often exhibit non-monotonic changes in chemical species concentrations, typically described using nonlinear models. Such non-monotonic dynamics are in principle possible even in linear models if the matrices defining the models are non-normal, as characterized by a necessarily non-orthogonal set of eigenvectors. However, the extent to which non-normality is responsible for non-monotonic behavior remains an open question. Here, using a master equation to model the reaction dynamics, we derive a general condition for observing non-monotonic dynamics of individual species, establishing that non-normality promotes non-monotonicity but is not a requirement for it. In contrast, we show that non-normality is a requirement for non-monotonic dynamics to be observed in the Rényi entropy. Using hydrogen combustion as an example application, we demonstrate that non-monotonic dynamics under experimental conditions are supported by a linear chain of connected components, in contrast with the dominance of a single giant component observed in typical random reaction networks. The exact linearity of the master equation enables development of rigorous theory and simulations for dynamical networks of unprecedented size (approaching 10^5 dynamical variables, even for a network of only 20 reactions and involving less than 100 atoms). Our conclusions are expected to hold for other combustion processes, and the general theory we develop is applicable to all chemical reaction networks, including biological ones.

Source: arxiv.org