A scaling law of multilevel evolution: how the balance between within- and among-collective evolution is determined
Nobuto Takeuchi, Namiko Mitarai, Kunihiko Kaneko

TL;DR
This paper explores how the balance between within- and among-collective evolution in hierarchical systems depends on collective size and mutation rate, revealing a scaling law that predicts when altruism or cheating will evolve.
Contribution
It introduces a mathematical scaling relation linking collective size and mutation rate to the evolution of cooperation or cheating in hierarchical systems.
Findings
Increasing $N$ or $m$ accelerates within-collective evolution.
A scaling law $Nm^{eta}$ = constant predicts the balance point.
The impact of $N$ and $m$ becomes equivalent when scaled properly.
Abstract
Numerous living systems are hierarchically organised, whereby replicating components are grouped into reproducing collectives -- e.g., organelles are grouped into cells, and cells are grouped into multicellular organisms. In such systems, evolution can operate at two levels: evolution among collectives, which tends to promote selfless cooperation among components within collectives (called altruism), and evolution within collectives, which tends to promote cheating among components within collectives. The balance between within- and among-collective evolution thus exerts profound impacts on the fitness of these systems. Here, we investigate how this balance depends on the size of a collective (denoted by ) and the mutation rate of components () through mathematical analyses and computer simulations of multiple population genetics models. We first confirm a previous result that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEvolution and Genetic Dynamics · Evolutionary Game Theory and Cooperation · Mathematical and Theoretical Epidemiology and Ecology Models
